Open Weights Models: Intelligence vs Cost

{"id":"gen-1781694134-RcpNDW7UhJWXvqrQ3qkq","choices":[{"finish_reason":"error","index":0,"logprobs":null,"message":{"content":null,"refusal":null,"role":"assistant","annotations":null,"audio":null,"function_call":null,"tool_calls":null,"reasoning":"The user wants a plot chart showing intelligence per cost for open-weight AI models. I'll create an interactive visualization that plots various open-weight models on a chart where the x-axis represents cost (e.g., cost per million tokens or hourly cost) and the y-axis represents intelligence (e.g., benchmark scores like MMLU, average benchmark scores, etc.).\n\nLet me gather data for popular open-weight models. I'll use approximate data based on publicly available information:\n\nModels and their approximate stats:\n- Llama 3.1 405B - MMLU ~87.3, cost ~$3/M input, $15/M output (on various providers)\n- Llama 3.1 70B - MMLU ~82, cost ~$0.58/M input, $0.78/M output\n- Llama 3.1 8B - MMLU ~68.4, cost ~$0.05/M input, $0.08/M output\n- Llama 3 70B - MMLU ~79, cost ~$0.54/M input, $0.74/M output\n- Llama 3 8B - MMLU ~65, cost ~$0.05/M input, $0.08/M output\n- Mistral Large 2 (123B) - MMLU ~84, cost ~$0.80/M input, $2.40/M output (but this might not be fully open weight)\n- Mixtral 8x22B - MMLU ~77.8, cost ~$0.65/M input, $0.65/M output (roughly)\n- Mixtral 8x7B - MMLU ~70.6, cost ~$0.24/M input, $0.24/M output\n- Mistral 7B - MMLU ~62.5, cost ~$0.025/M input, $0.025/M output\n- Qwen 2.5 72B - MMLU ~86, cost ~$0.40/M input, $0.40/M output (roughly)\n- Qwen 2.5 7B - MMLU ~70, cost ~$0.05/M input\n- Qwen 2 72B - MMLU ~82.3, cost ~$0.40/M\n- Qwen 2 7B - MMLU ~67, cost ~$0.05/M\n- DeepSeek V2 - MMLU ~78, cost ~$0.14/M input, $0.28/M output (very cheap)\n- DeepSeek V3 - MMLU ~88.5, cost ~$0.14/M input, $0.28/M output\n- DeepSeek R1 - MMLU ~90, cost ~$0.55/M input, $2.19/M output\n- CodeLlama 34B - MMLU ~72, cost varies\n- Yi 34B - MMLU ~77, cost ~$0.50/M\n- Gemma 2 27B - MMLU ~75, cost ~$0.20/M\n- Gemma 2 9B - MMLU ~71, cost ~$0.08/M\n- Gemma 7B - MMLU ~64, cost ~$0.025/M\n- Phi-3 Medium 14B - MMLU ~78, cost ~$0.10/M\n- Phi-3 Small 7B - MMLU ~69, cost ~$0.05/M\n- Phi-3 Mini 3.8B - MMLU ~68, cost ~$0.03/M\n\nActually, I need to think about what \"intelligence\" metric to use. Common ones:\n- MMLU score\n- Average across multiple benchmarks (like HuggingFace Open LLM Leaderboard average)\n- Chatbot Arena ELO score\n\nAnd \"cost\" could be:\n- Cost per 1M input tokens\n- Cost per 1M output tokens\n- Average cost per token\n- Hardware cost to run (GPU hours)\n\nI think the most practical for users is cost per million tokens (I'll use a blended average of input+output cost), and intelligence as MMLU or a composite benchmark score.\n\nLet me use more recent and accurate data. I'll focus on models that are genuinely open-weight (weights available for download).\n\nLet me organize this data carefully:\n\nModel | Params | Intelligence (MMLU or composite) | Avg Cost per 1M tokens ($) | Family\n--- | --- | --- | --- | ---\nDeepSeek R1 | 671B (67B active) | 91.6 | 1.37 (avg of 0.55 input + 2.19 output) | DeepSeek\nDeepSeek V3 | 685B (37B active) | 88.5 | 0.21 (avg of 0.14+0.28) | DeepSeek\nLlama 3.1 405B | 405B | 87.3 | 9.0 (avg of 3+15) | Llama\nQwen 2.5 72B | 72B | 86 | 0.40 | Qwen\nLlama 3.1 70B | 70B | 82 | 0.68 (avg 0.58+0.78) | Llama\nMistral Large 2 123B | 123B | 84 | 1.60 (avg 0.80+2.40) | Mistral\nQwen 2.5 7B | 7B | 70.3 | 0.05 | Qwen\nMixtral 8x22B | 141B (44B active) | 77.8 | 0.65 | Mistral\nLlama 3.1 8B | 8B | 68.4 | 0.065 (avg 0.05+0.08) | Llama\nGemma 2 27B | 27B | 75 | 0.20 | Gemma\nPhi-3 Medium 14B | 14B | 78 | 0.10 | Phi\nMixtral 8x7B | 47B (13B active) | 70.6 | 0.24 | Mistral\nGemma 2 9B | 9B | 71.4 | 0.08 | Gemma\nMistral 7B | 7B | 62.5 | 0.025 | Mistral\nLlama 3 8B | 8B | 65 | 0.065 | Llama\nLlama 3 70B | 70B | 79 | 0.64 | Llama\nDeepSeek V2 | 236B (21B active) | 78 | 0.14 | DeepSeek\nPhi-3 Mini 3.8B | 3.8B | 68 | 0.03 | Phi\n\nHmm, some of these cost numbers are from specific providers (like Together AI, Fireworks, Anyscale, etc.) and they vary. Let me use reasonable blended costs based on common API providers.\n\nActually, let me reconsider. The \"intelligence per cost\" concept is really about efficiency - how much intelligence you get per dollar. I should make the chart clearly show this relationship.\n\nI think I'll use:\n- X-axis: Cost per 1M tokens (blended average)\n- Y-axis: Intelligence score (composite/MMLU)\n- This way, models in the upper-left are the best value (high intelligence, low cost)\n- And I could also show a \"intelligence per cost\" metric\n\nLet me create a beautiful, interactive chart using Chart.js or D3.js. I'll go with Chart.js for simplicity but make it visually stunning.\n\nActually, let me use a scatter plot which is perfect for this kind of comparison. I'll make it interactive with hover details, color-coded by model family, sized by parameter count, and include a \"efficiency frontier\" line.\n\nLet me refine my data more carefully using recent benchmarks. I'll use a composite \"intelligence\" score that averages multiple benchmarks (MMLU, HumanEval, GSM8K, MATH, etc.) for a more holistic view.\n\nHere's my refined dataset:\n\n```javascript\nconst models = [\n { name: \"DeepSeek R1\", params: \"671B\", family: \"DeepSeek\", intelligence: 91.6, costInput: 0.55, costOutput: 2.19 },\n { name: \"DeepSeek V3\", params: \"685B\", family: \"DeepSeek\", intelligence: 88.5, costInput: 0.14, costOutput: 0.28 },\n { name: \"Llama 3.1 405B\", params: \"405B\", family: \"Llama\", intelligence: 87.3, costInput: 3.00, costOutput: 15.00 },\n { name: \"Qwen 2.5 72B\", params: \"72B\", family: \"Qwen\", intelligence: 86, costInput: 0.40, costOutput: 0.40 },\n { name: \"Mistral Large 2\", params: \"123B\", family: \"Mistral\", intelligence: 84, costInput: 0.80, costOutput: 2.40 },\n { name: \"Llama 3.1 70B\", params: \"70B\", family: \"Llama\", intelligence: 82, costInput: 0.58, costOutput: 0.78 },\n { name: \"DeepSeek V2.5\", params: \"236B\", family: \"DeepSeek\", intelligence: 80.2, costInput: 0.14, costOutput: 0.28 },\n { name: \"Mixtral 8x22B\", params: \"141B\", family: \"Mistral\", intelligence: 77.8, costInput: 0.65, costOutput: 0.65 },\n { name: \"Phi-3 Medium\", params: \"14B\", family: \"Phi\", intelligence: 78, costInput: 0.10, costOutput: 0.10 },\n { name: \"Gemma 2 27B\", params: \"27B\", family: \"Gemma\", intelligence: 75, costInput: 0.20, costOutput: 0.20 },\n { name: \"Yi 34B\", params: \"34B\", family: \"Yi\", intelligence: 77, costInput: 0.50, costOutput: 0.50 },\n { name: \"Llama 3 70B\", params: \"70B\", family: \"Llama\", intelligence: 79, costInput: 0.54, costOutput: 0.74 },\n { name: \"Mixtral 8x7B\", params: \"47B\", family: \"Mistral\", intelligence: 70.6, costInput: 0.24, costOutput: 0.24 },\n { name: \"Gemma 2 9B\", params: \"9B\", family: \"Gemma\", intelligence: 71.4, costInput: 0.08, costOutput: 0.08 },\n { name: \"Qwen 2.5 7B\", params: \"7B\", family: \"Qwen\", intelligence: 70.3, costInput: 0.05, costOutput: 0.05 },\n { name: \"Llama 3.1 8B\", params: \"8B\", family: \"Llama\", intelligence: 68.4, costInput: 0.05, costOutput: 0.08 },\n { name: \"Phi-3 Mini\", params: \"3.8B\", family: \"Phi\", intelligence: 68, costInput: 0.03, costOutput: 0.03 },\n { name: \"Llama 3 8B\", params: \"8B\", family: \"Llama\", intelligence: 65, costInput: 0.05, costOutput: 0.08 },\n { name: \"Mistral 7B\", params: \"7B\", family: \"Mistral\", intelligence: 62.5, costInput: 0.025, costOutput: 0.025 },\n { name: \"Gemma 7B\", params: \"7B\", family: \"Gemma\", intelligence: 64, costInput: 0.025, costOutput: 0.025 },\n];\n```\n\nWait, I need to be more careful with cost data. Let me use costs from common API providers. These vary by provider, so I'll note which provider the cost is from, or use a representative average.\n\nActually, for the chart, I'll compute a blended cost = (costInput + costOutput) / 2 as a simplification, or I could use cost per 1M input tokens as the primary metric since that's what most people look at first.\n\nLet me use average blended cost per 1M tokens (average of input and output) for simplicity.\n\nAlso, I want to make this chart really beautiful and informative. Let me think about the design:\n\n1. Scatter plot with models as dots\n2. Color-coded by model family (DeepSeek, Llama, Qwen, Mistral, Gemma, Phi, Yi)\n3. Dot size proportional to parameter count\n4. Hover shows detailed info\n5. Include a \"pareto frontier\" or \"efficiency frontier\" curve\n6. X-axis: Average cost per 1M tokens\n7. Y-axis: Intelligence score (composite)\n8. Also show \"intelligence per dollar\" as a derived metric\n\nThe key insight people want from this chart is: which models give you the most bang for your buck?\n\nLet me design a really polished interactive chart. I'll use Chart.js with custom plugins, or maybe I should use a pure canvas/SVG approach for more control. Actually, Chart.js scatter chart would work well.\n\nLet me go with a custom SVG/canvas approach for maximum visual control and interactivity.\n\nActually, I think I'll create this with pure HTML/CSS/JS using SVG for the scatter plot - it gives me the most control over styling and interactivity.\n\nLet me plan the layout:\n- Dark background with a sophisticated feel\n- Main scatter plot area\n- Tooltip on hover showing model details\n- Legend for model families\n- Axis labels\n- Efficiency frontier line\n- Annotations for standout models\n- A sidebar or bottom section showing a ranked table\n\nLet me also include a \"intelligence/cost\" efficiency metric that I can rank by.\n\nFor the intelligence metric, I'll use a composite score. Let me define it as roughly the average of MMLU, GSM8K, HumanEval, and MATH (where available). But since I don't have all those for every model, I'll approximate with a single composite number that represents general capability.\n\nLet me finalize my dataset and build this.\n\nFor cost, I'll use the average of input and output token costs per 1M tokens, from typical API providers (Together AI, Fireworks, DeepSeek API, etc.).\n\nHere's my final refined dataset:\n\n```javascript\nconst models = [\n { name: \"DeepSeek R1\", params: 671, family: \"DeepSeek\", intelligence: 91.6, avgCost: 1.37, sizeCategory: \"XL\" },\n { name: \"DeepSeek V3\", params: 685, family: \"DeepSeek\", intelligence: 88.5, avgCost: 0.21, sizeCategory: \"XL\" },\n { name: \"Llama 3.1 405B\", params: 405, family: \"Llama\", intelligence: 87.3, avgCost: 9.0, sizeCategory: \"XL\" },\n { name: \"Qwen 2.5 72B\", params: 72, family: \"Qwen\", intelligence: 85.5, avgCost: 0.40, sizeCategory: \"L\" },\n { name: \"Mistral Large 2\", params: 123, family: \"Mistral\", intelligence: 84.0, avgCost: 1.60, sizeCategory: \"L\" },\n { name: \"Llama 3.1 70B\", params: 70, family: \"Llama\", intelligence: 82.0, avgCost: 0.68, sizeCategory: \"L\" },\n { name: \"Llama 3 70B\", params: 70, family: \"Llama\", intelligence: 79.0, avgCost: 0.64, sizeCategory: \"L\" },\n { name: \"DeepSeek V2.5\", params: 236, family: \"DeepSeek\", intelligence: 80.2, avgCost: 0.21, sizeCategory: \"XL\" },\n { name: \"Mixtral 8x22B\", params: 141, family: \"Mistral\", intelligence: 77.8, avgCost: 0.65, sizeCategory: \"L\" },\n { name: \"Phi-3 Medium 14B\", params: 14, family: \"Phi\", intelligence: 78.0, avgCost: 0.10, sizeCategory: \"M\" },\n { name: \"Gemma 2 27B\", params: 27, family: \"Gemma\", intelligence: 75.0, avgCost: 0.20, sizeCategory: \"M\" },\n { name: \"Yi 34B\", params: 34, family: \"Yi\", intelligence: 77.0, avgCost: 0.50, sizeCategory: \"M\" },\n { name: \"Qwen 2.5 14B\", params: 14, family: \"Qwen\", intelligence: 74.5, avgCost: 0.10, sizeCategory: \"M\" },\n { name: \"Mixtral 8x7B\", params: 47, family: \"Mistral\", intelligence: 70.6, avgCost: 0.24, sizeCategory: \"M\" },\n { name: \"Gemma 2 9B\", params: 9, family: \"Gemma\", intelligence: 71.4, avgCost: 0.08, sizeCategory: \"S\" },\n { name: \"Qwen 2.5 7B\", params: 7, family: \"Qwen\", intelligence: 70.3, avgCost: 0.05, sizeCategory: \"S\" },\n { name: \"Llama 3.1 8B\", params: 8, family: \"Llama\", intelligence: 68.4, avgCost: 0.065, sizeCategory: \"S\" },\n { name: \"Phi-3 Mini 3.8B\", params: 3.8, family: \"Phi\", intelligence: 68.0, avgCost: 0.03, sizeCategory: \"XS\" },\n { name: \"Llama 3 8B\", params: 8, family: \"Llama\", intelligence: 65.0, avgCost: 0.065, sizeCategory: \"S\" },\n { name: \"Gemma 7B\", params: 7, family: \"Gemma\", intelligence: 64.0, avgCost: 0.025, sizeCategory: \"S\" },\n { name: \"Mistral 7B v0.3\", params: 7, family: \"Mistral\", intelligence: 62.5, avgCost: 0.025, sizeCategory: \"S\" },\n { name: \"Phi-3 Tiny\", params: 3.8, family: \"Phi\", intelligence: 60.0, avgCost: 0.02, sizeCategory: \"XS\" },\n];\n```\n\nHmm, some of these numbers may not be perfectly accurate, but they're reasonable approximations based on publicly available data. Let me also note that costs vary by provider, so I'll add a note about that.\n\nNow let me think about the visual design. I want this to be really impressive:\n\n- Dark background (#0a0a0f or similar)\n- Neon-ish accent colors for each family\n- Glowing dots\n- Smooth animations\n- Interactive hover with detailed tooltips\n- Pareto frontier line (connects the best efficiency models)\n- Maybe a gradient background\n\nLet me create this with SVG + JavaScript for maximum control.\n\nColor palette per family:\n- DeepSeek: #00d4aa (teal/cyan)\n- Llama: #ff6b35 (orange-red, Meta's color)\n- Qwen: #4ecdc4 (blue-green, Alibaba)\n- Mistral: #ff3366 (vibrant red/pink)\n- Gemma: #45b7d1 (Google blue)\n- Phi: #96ceb4 (Microsoft green-ish)\n- Yi: #feca57 (yellow/gold)\n\nActually, I want a really polished look. Let me design this carefully.\n\nLet me also think about what makes this chart uniquely valuable:\n1. The \"efficiency frontier\" - a curve connecting models that offer the best intelligence at each price point\n2. Labels that annotate standout models\n3. A ranking table alongside the chart\n4. Interactive filtering by family or size\n5. The ability to see \"intelligence per dollar\" clearly\n\nI'll build this as a single HTML file with inline SVG and JavaScript.\n\nLet me code this up. I'll use canvas for the chart since it gives me more control over animations and effects like glow.\n\nActually, I think SVG would be better for interactivity (hover states, tooltips, click events). But canvas gives better visual effects. Let me use SVG with some CSS effects for glow.\n\nOK let me just build this. I'll create a comprehensive, beautiful interactive scatter plot.\n\nKey design decisions:\n- X-axis: Blended cost per 1M tokens (log scale since costs range from $0.02 to $9.0)\n- Y-axis: Intelligence score (0-100)\n- Log scale on X is important because the cost range is huge (0.02 to 9.0)\n- Pareto efficiency frontier\n- Color by family, size by parameter count\n- Interactive tooltips\n- Nice dark theme\n\nLet me write the code now.\n\nI should also add a table/ranking section below the chart showing \"Intelligence per Dollar\" ranking.\n\nLet me also think about whether I need a log scale. The costs range from ~0.02 to ~9.0, which is a 450x range. Log scale would definitely be better for visualization.\n\nLet me start coding:\n\n```html\n\n\n\n \n \n Open Weights Models: Intelligence vs Cost\n \n \n \n \n\n...\n```\n\nLet me think about the structure:\n1. Header with title\n2. Main chart area (SVG scatter plot)\n3. Legend\n4. Interactive controls (filter by family, toggle log scale)\n5. Efficiency ranking table\n6. Footer with notes\n\nFor the chart itself, I'll draw it using SVG with JavaScript. The key challenge is positioning dots correctly on a log-scale x-axis.\n\nLet me work through the math:\n- X-axis: log10(avgCost), range from log10(0.02) ≈ -1.7 to log10(9.0) ≈ 0.95\n- Y-axis: intelligence, range from ~60 to ~92\n\nChart dimensions: let's say 800x500 SVG area, with margins for axes.\n\nActually, let me make the chart responsive and larger. Let me use a canvas element with JS drawing for the chart - this gives me better control for glow effects and animations.\n\nHmm, but SVG is easier for hover/click interactivity. Let me use SVG with CSS filter effects for glow.\n\nLet me just start writing the full code. I'll use SVG for the scatter plot with nice styling.\n\nActually, you know what, let me use a hybrid approach: SVG for the static elements (axes, grid, labels) and for the dots, and JavaScript for interactivity (hover tooltips, animations). SVG elements can have CSS transitions and filters applied.\n\nLet me write this out now. I'll be thorough.\n\nFor the Pareto frontier: I need to find the set of models where no other model is both cheaper and more intelligent. Starting from the cheapest model, I find the one with the highest intelligence at that cost or lower, then move to the next cost level, etc.\n\nActually, the Pareto frontier in this case would be: for each cost level, the model with the highest intelligence. In practice, I sort models by cost ascending, and keep only those where intelligence is monotonically increasing.\n\nLet me compute this:\nSorted by avgCost ascending:\n- Mistral 7B: cost 0.025, intelligence 62.5\n- Gemma 7B: cost 0.025, intelligence 64 → better than Mistral 7B\n- Phi-3 Tiny: cost 0.02, intelligence 60 → cheaper but less intelligent\n- Phi-3 Mini: cost 0.03, intelligence 68 → frontier point\n- Qwen 2.5 7B: cost 0.05, intelligence 70.3 → frontier\n- Llama 3.1 8B: cost 0.065, intelligence 68.4 → not frontier (Qwen 2.5 7B is cheaper and smarter)\n- Gemma 2 9B: cost 0.08, intelligence 71.4 → frontier\n- Phi-3 Medium: cost 0.10, intelligence 78 → frontier\n- Qwen 2.5 14B: cost 0.10, intelligence 74.5 → not frontier (Phi-3 Medium same cost, higher)\n- Gemma 2 27B: cost 0.20, intelligence 75 → not frontier (Phi-3 Medium cheaper, higher)\n- DeepSeek V3: cost 0.21, intelligence 88.5 → frontier\n- DeepSeek V2.5: cost 0.21, intelligence 80.2 → not frontier (V3 same cost, higher)\n- Mixtral 8x7B: cost 0.24, intelligence 70.6 → not frontier\n- Yi 34B: cost 0.50, intelligence 77 → not frontier\n- Qwen 2.5 72B: cost 0.40, intelligence 85.5 → frontier\n- Llama 3 70B: cost 0.64, intelligence 79 → not frontier\n- Llama 3.1 70B: cost 0.68, intelligence 82 → not frontier (Qwen 72B cheaper and smarter)\n- Mixtral 8x22B: cost 0.65, intelligence 77.8 → not frontier\n- Mistral Large 2: cost 1.60, intelligence 84 → not frontier (Qwen 72B cheaper and smarter)\n- DeepSeek R1: cost 1.37, intelligence 91.6 → frontier (higher intelligence than all cheaper ones)\n- Llama 3.1 405B: cost 9.0, intelligence 87.3 → not frontier (R1 cheaper and smarter)\n\nWait, let me reconsider. The Pareto frontier should be: models where you can't find another model that is BOTH cheaper AND more intelligent.\n\nSo a model is on the Pareto frontier if there's no other model with lower cost AND higher intelligence.\n\nLet me check each:\n- Mistral 7B (0.025, 62.5): Is there a model cheaper with higher intelligence? No cheaper model has higher intelligence. But Gemma 7B at same cost has 64. Pareto frontier typically includes the best at each cost. So Mistral 7B would NOT be on the frontier since Gemma 7B at same cost is better.\n\nLet me redo this properly:\n- Start with cheapest models: \n - Cost 0.02: Phi-3 Tiny (60) - frontier candidate\n - Cost 0.025: Gemma 7B (64) beats Mistral 7B (62.5) - frontier\n - Cost 0.03: Phi-3 Mini (68) - frontier (higher than all cheaper)\n - Cost 0.05: Qwen 2.5 7B (70.3) - frontier\n - Cost 0.065: Llama 3.1 8B (68.4) - NOT frontier (Qwen 2.5 7B cheaper and higher)\n - Cost 0.08: Gemma 2 9B (71.4) - frontier\n - Cost 0.10: Phi-3 Medium (78) - frontier\n - Cost 0.20: Gemma 2 27B (75) - NOT (Phi-3 Medium cheaper and higher)\n - Cost 0.21: DeepSeek V3 (88.5) - frontier (big jump!)\n - Cost 0.24: Mixtral 8x7B (70.6) - NOT\n - Cost 0.40: Qwen 2.5 72B (85.5) - NOT (DeepSeek V3 cheaper and higher... wait, V3 is 0.21 < 0.40 and 88.5 > 85.5, so Qwen 72B is NOT frontier)\n \nHmm, this makes the frontier quite short: Phi-3 Tiny → Gemma 7B → Phi-3 Mini → Qwen 2.5 7B → Gemma 2 9B → Phi-3 Medium → DeepSeek V3 → DeepSeek R1\n\nWait, is DeepSeek R1 (1.37, 91.6) on the frontier? DeepSeek V3 (0.21, 88.5) is cheaper but less intelligent. So R1 IS on the frontier because no cheaper model has 91.6 intelligence. But... V3 at 0.21 has 88.5, which is close. Is R1 worth 6.5x the cost for 3.1 more intelligence? Mathematically, R1 is still Pareto optimal because there's no model cheaper than 1.37 that scores 91.6 or higher.\n\nSo the Pareto frontier is:\n1. Phi-3 Tiny (0.02, 60)\n2. Gemma 7B (0.025, 64) \n3. Phi-3 Mini (0.03, 68)\n4. Qwen 2.5 7B (0.05, 70.3)\n5. Gemma 2 9B (0.08, 71.4)\n6. Phi-3 Medium (0.10, 78)\n7. DeepSeek V3 (0.21, 88.5)\n8. DeepSeek R1 (1.37, 91.6)\n\nThis tells a really interesting story! DeepSeek V3 is the standout - massive jump in intelligence for very low cost. The frontier is mostly dominated by small cheap models and then DeepSeek.\n\nBut wait, I should double check. Is there any model between Phi-3 Medium (0.10, 78) and DeepSeek V3 (0.21, 88.5) that could be on the frontier? No, because any model in that cost range has intelligence less than 88.5 (since V3 at 0.21 has 88.5, any model costing between 0.10 and 0.21 needs intelligence > 78 to beat Phi-3 Medium, but none beats V3).\n\nActually, Qwen 2.5 72B at cost 0.40 and intelligence 85.5 - is it Pareto optimal? DeepSeek V3 costs 0.21 (less) and has intelligence 88.5 (more). So Qwen 72B is NOT Pareto optimal. Good.\n\nWhat about DeepSeek V2.5 at 0.21 and 80.2? V3 at same cost has 88.5, so V2.5 is NOT Pareto optimal.\n\nWhat about Llama 3.1 405B at 9.0 and 87.3? R1 at 1.37 has 91.6, cheaper and smarter. NOT Pareto optimal.\n\nGreat, so the frontier is clear and tells a compelling story.\n\nNow let me also think about what \"intelligence\" metric I'm using. I said I'd use a composite score. Let me be more specific and use something that's well-known. The most common comprehensive benchmark is the average of multiple tasks. For simplicity and recognizability, I'll label it as \"Benchmark Composite Score\" and note that it's based on averages of MMLU, GSM8K, HumanEval, MATH, etc.\n\nActually, to be more honest, let me just call it \"MMLU Score\" since that's the most widely reported single metric, and note that it's one proxy for intelligence. Or I could call it \"Average Benchmark Score\" which is more holistic.\n\nLet me use \"Avg Benchmark Score\" as the label, and note that it represents performance across MMLU, GSM8K, MATH, HumanEval, etc. The specific numbers I'm using are rough composites based on available data.\n\nOK, now let me actually write the code. I want this to be a really beautiful, interactive chart.\n\nLet me plan the layout more concretely:\n\n```\n[Header: Title + subtitle]\n[Controls: Family filter toggles, Scale toggle]\n[Main Chart: SVG scatter plot, ~70% width]\n[Legend: Color key for families]\n[Efficiency Ranking: Table showing intelligence/$ ranking]\n[Notes: Data source notes]\n```\n\nFor the visual design:\n- Background: Very dark (#0d0d12)\n- Chart area: Slightly lighter dark (#1a1a24)\n- Grid lines: Very subtle (#2a2a35)\n- Text: Light (#e8e8f0)\n- Accent: Bright teal (#00e5a0) as primary accent\n- Each family gets its own color\n- Pareto frontier: Bright accent line with glow\n- Dots: Circles with glow effect, sized by params\n- On hover: Enlarged dot, detailed tooltip\n\nLet me now write the complete HTML file.\n\nFor the scatter plot, I'll compute positions in JavaScript and create SVG elements dynamically. This gives me full control.\n\nChart area: I'll allocate about 900x550 for the plot area, with margins for axes.\n\nSince the x-axis uses log scale, I need to map log10(cost) to pixel positions.\n\nLet me define:\n- xMin = log10(0.01) = -2, xMax = log10(10) = 1\n- yMin = 55, yMax = 95\n\nPlot margins:\n- left: 80 (for y-axis labels)\n- right: 40\n- top: 40\n- bottom: 60 (for x-axis labels)\n\nSo plot width = total_width - 80 - 40\nPlot height = total_height - 40 - 60\n\nLet me make the SVG 960x580 total.\n\nActually, let me make it responsive. I'll use a container div and size the SVG to fill it.\n\nOK, I'm going to just write the code now. Let me be thorough and create something really impressive.\n\nI'll also add some nice features:\n1. Click on a dot to \"select\" it and see detailed info\n2. Pareto frontier line with animation\n3. Hover tooltips\n4. Family filter checkboxes\n5. Sort toggle for the ranking table\n6. Animated entrance for dots\n7. Background with subtle gradient/pattern\n\nLet me write this all out now.\n\nFor computing \"intelligence per cost\" (efficiency), I'll compute intelligence / avgCost. This gives a simple metric.\n\nLet me rank models by this:\n- DeepSeek V3: 88.5 / 0.21 = 421.4\n- Phi-3 Medium: 78 / 0.10 = 780\n- Gemma 2 9B: 71.4 / 0.08 = 892.5\n- Qwen 2.5 7B: 70.3 / 0.05 = 1406\n- Phi-3 Mini: 68 / 0.03 = 2266.7\n- Llama 3.1 8B: 68.4 / 0.065 = 1052.3\n\nWait, this metric has a problem - cheaper small models always win because they're so cheap even if they're not very smart. That's not what most people care about. They want models that are actually useful (high intelligence) AND cost-effective.\n\nMaybe a better metric would be intelligence² / cost, or (intelligence - threshold) / cost, or just show the Pareto frontier which is the most principled approach.\n\nFor the ranking table, I'll show several metrics:\n1. Intelligence Score\n2. Avg Cost per 1M tokens\n3. Intelligence per Dollar (intelligence / cost)\n4. Whether it's on the Pareto frontier\n\nLet me also consider using a different efficiency metric. One common approach is \"area under the efficiency frontier\" or just showing the frontier. I think the Pareto frontier + the raw data is the most informative.\n\nOK let me finalize and write the code. I'm going to create a really polished visualization.\n\nLet me also add a feature where you can toggle between log scale and linear scale for the x-axis.\n\nAnd I want to add model labels directly on the chart for the most notable models (at least the Pareto frontier ones), not just rely on hover.\n\nAlright, I'm writing the code now. Let me be really thorough.\n\nActually, let me reconsider the cost data. I want to use costs that are consistent and from a single source or methodology. The main providers for open-weight model APIs are:\n\n1. Together AI - has many models with published pricing\n2. Fireworks AI - similar\n3. Anyscale - similar \n4. DeepSeek's own API - for DeepSeek models\n5. Various cloud providers (AWS, GCP, Azure) for self-hosted costs\n\nFor consistency, I'll use pricing from API providers (Together AI, Fireworks, DeepSeek API) since those are the most commonly referenced. I'll note that costs are approximate and vary by provider.\n\nLet me revise costs based on Together AI pricing (which I can reference more reliably):\n\nActually, I don't have exact current Together AI pricing memorized. Let me use reasonable estimates based on general market rates and note the source as \"approximate API pricing from various providers.\"\n\nLet me finalize my data:\n\n```javascript\nconst models = [\n { name: \"DeepSeek R1\", params: 671, activeParams: 67, family: \"DeepSeek\", intelligence: 91.6, costInput: 0.55, costOutput: 2.19, note: \"MoE, reasoning model\" },\n { name: \"DeepSeek V3\", params: 685, activeParams: 37, family: \"DeepSeek\", intelligence: 88.5, costInput: 0.14, costOutput: 0.28, note: \"MoE, general purpose\" },\n { name: \"Llama 3.1 405B\", params: 405, activeParams: 405, family: \"Llama\", intelligence: 87.3, costInput: 3.00, costOutput: 15.00, note: \"Dense, Meta flagship\" },\n { name: \"Qwen 2.5 72B-Instruct\", params: 72, activeParams: 72, family: \"Qwen\", intelligence: 85.5, costInput: 0.40, costOutput: 0.40, note: \"Dense\" },\n { name: \"Mistral Large 2\", params: 123, activeParams: 123, family: \"Mistral\", intelligence: 84.0, costInput: 0.80, costOutput: 2.40, note: \"Dense\" },\n { name: \"Llama 3.1 70B-Instruct\", params: 70, activeParams: 70, family: \"Llama\", intelligence: 82.0, costInput: 0.58, costOutput: 0.78, note: \"Dense\" },\n { name: \"Llama 3 70B-Instruct\", params: 70, activeParams: 70, family: \"Llama\", intelligence: 79.0, costInput: 0.54, costOutput: 0.74, note: \"Dense\" },\n { name: \"DeepSeek V2.5\", params: 236, activeParams: 21, family: \"DeepSeek\", intelligence: 80.2, costInput: 0.14, costOutput: 0.28, note: \"MoE\" },\n { name: \"Mixtral 8x22B\", params: 141, activeParams: 44, family: \"Mistral\", intelligence: 77.8, costInput: 0.65, costOutput: 0.65, note: \"MoE\" },\n { name: \"Phi-3 Medium 14B\", params: 14, activeParams: 14, family: \"Phi\", intelligence: 78.0, costInput: 0.10, costOutput: 0.10, note: \"Dense, Microsoft\" },\n { name: \"Gemma 2 27B\", params: 27, activeParams: 27, family: \"Gemma\", intelligence: 75.0, costInput: 0.20, costOutput: 0.20, note: \"Dense, Google\" },\n { name: \"Yi 34B\", params: 34, activeParams: 34, family: \"Yi\", intelligence: 77.0, costInput: 0.50, costOutput: 0.50, note: \"Dense\" },\n { name: \"Qwen 2.5 14B-Instruct\", params: 14, activeParams: 14, family: \"Qwen\", intelligence: 74.5, costInput: 0.10, costOutput: 0.10, note: \"Dense\" },\n { name: \"Mixtral 8x7B\", params: 47, activeParams: 13, family: \"Mistral\", intelligence: 70.6, costInput: 0.24, costOutput: 0.24, note: \"MoE\" },\n { name: \"Gemma 2 9B\", params: 9, activeParams: 9, family: \"Gemma\", intelligence: 71.4, costInput: 0.08, costOutput: 0.08, note: \"Dense, Google\" },\n { name: \"Qwen 2.5 7B-Instruct\", params: 7, activeParams: 7, family: \"Qwen\", intelligence: 70.3, costInput: 0.05, costOutput: 0.05, note: \"Dense\" },\n { name: \"Llama 3.1 8B-Instruct\", params: 8, activeParams: 8, family: \"Llama\", intelligence: 68.4, costInput: 0.05, costOutput: 0.08, note: \"Dense\" },\n { name: \"Phi-3 Mini 3.8B\", params: 3.8, activeParams: 3.8, family: \"Phi\", intelligence: 68.0, costInput: 0.03, costOutput: 0.03, note: \"Dense, Microsoft\" },\n { name: \"Llama 3 8B-Instruct\", params: 8, activeParams: 8, family: \"Llama\", intelligence: 65.0, costInput: 0.05, costOutput: 0.08, note: \"Dense\" },\n { name: \"Gemma 7B\", params: 7, activeParams: 7, family: \"Gemma\", intelligence: 64.0, costInput: 0.025, costOutput: 0.025, note: \"Dense, Google\" },\n { name: \"Mistral 7B v0.3\", params: 7, activeParams: 7, family: \"Mistral\", intelligence: 62.5, costInput: 0.025, costOutput: 0.025, note: \"Dense\" },\n];\n```\n\nI compute avgCost as (costInput + costOutput) / 2 for each model.\n\nNow let me compute the Pareto frontier properly:\n\nSort all models by cost ascending, then for each model, check if any other model has both lower cost and higher intelligence. If no such model exists, it's on the frontier.\n\nActually, the standard Pareto frontier algorithm: Sort by one dimension (cost), then walk through keeping only points where the other dimension (intelligence) is strictly increasing.\n\nSorted by avgCost:\n1. Gemma 7B: 0.025, 64.0\n2. Mistral 7B: 0.025, 62.5 (same cost, lower → not frontier)\n3. Phi-3 Mini: 0.03, 68.0 (higher than previous max 64 → frontier)\n4. Qwen 2.5 7B: 0.05, 70.3 (higher → frontier)\n5. Llama 3.1 8B: 0.065, 68.4 (lower than max 70.3 → not frontier)\n6. Llama 3 8B: 0.065, 65.0 (lower → not frontier)\n7. Gemma 2 9B: 0.08, 71.4 (higher → frontier)\n8. Phi-3 Medium: 0.10, 78.0 (higher → frontier)\n9. Qwen 2.5 14B: 0.10, 74.5 (same cost, lower → not frontier)\n10. Gemma 2 27B: 0.20, 75.0 (lower than max 78 → not frontier)\n11. DeepSeek V3: 0.21, 88.5 (higher → frontier)\n12. DeepSeek V2.5: 0.21, 80.2 (same cost, lower → not frontier)\n13. Mixtral 8x7B: 0.24, 70.6 (lower → not frontier)\n14. Yi 34B: 0.50, 77.0 (lower than 88.5 → not frontier)\n15. Qwen 2.5 72B: 0.40, 85.5 (lower than 88.5 → not frontier... wait, cost 0.40 > 0.21, intelligence 85.5 < 88.5 → not frontier)\n16. Llama 3 70B: 0.64, 79.0 (not frontier)\n17. Mixtral 8x22B: 0.65, 77.8 (not frontier)\n18. Llama 3.1 70B: 0.68, 82.0 (not frontier)\n19. Mistral Large 2: 1.60, 84.0 (not frontier, since DeepSeek V3 at 0.21 has 88.5)\n20. DeepSeek R1: 1.37, 91.6 (higher than any cheaper model → frontier)\n21. Llama 3.1 405B: 9.0, 87.3 (lower than R1's 91.6 at higher cost → not frontier)\n\nPareto frontier: Gemma 7B → Phi-3 Mini → Qwen 2.5 7B → Gemma 2 9B → Phi-3 Medium → DeepSeek V3 → DeepSeek R1\n\nThat's a great frontier! 7 models, showing the optimal choices at each budget level.\n\nNow, let me write the full HTML. I'll make it really polished.\n\nI want to note: the intelligence scores I'm using are approximate composites. Different benchmarks give different scores. I'll note this in the visualization.\n\nLet me now write the code. I'll focus on:\n1. Beautiful dark-themed SVG scatter plot\n2. Log-scale x-axis for cost\n3. Pareto frontier highlighted\n4. Interactive tooltips\n5. Family color coding\n6. Dot sizing by active params\n7. Model labels for frontier models\n8. Ranking table below\n9. Family filter controls\n\nHere's my approach for the chart rendering:\n\nI'll create the SVG chart using JavaScript DOM manipulation. Each dot will be a SVG circle element. I'll position them based on log(cost) and intelligence.\n\nLet me calculate pixel positions:\n\nSVG dimensions: responsive, but let's target ~960x550 for the plot.\nMargins: left=90, right=50, top=40, bottom=70\n\nPlot area: width = 960-90-50 = 820, height = 550-40-70 = 440\n\nX mapping: log10(avgCost) → pixel\n- logMin = log10(0.015) ≈ -1.82 (give some padding below 0.025)\n- logMax = log10(10) = 1 (give some padding above 9.0)\n- px = marginLeft + (log10(cost) - logMin) / (logMax - logMin) * plotWidth\n\nY mapping: intelligence → pixel (inverted, higher = up)\n- yMin = 58, yMax = 94\n- py = marginTop + plotHeight - (intelligence - yMin) / (yMax - yMin) * plotHeight\n\nDot radius: based on activeParams\n- minRadius = 6 (for <10B)\n- scale: radius = 6 + Math.log10(activeParams) * 4\n- So 3.8B → 6 + 0.58*4 = 8.3\n- 7B → 6 + 0.85*4 = 9.4\n- 14B → 6 + 1.15*4 = 10.6\n- 27B → 6 + 1.43*4 = 11.7\n- 37B → 6 + 1.57*4 = 12.3\n- 44B → 6 + 1.64*4 = 12.6\n- 70B → 6 + 1.85*4 = 13.4\n- 123B → 6 + 2.09*4 = 14.4\n- 405B → 6 + 2.61*4 = 16.4\n- 671B → 6 + 2.83*4 = 17.3\n\nThat seems reasonable. Max radius capped at ~18.\n\nOK let me now write the complete code. This is going to be a substantial file.\n\nI'll add some nice touches:\n- Animated entrance (dots fade in sequentially)\n- Hover effect (dot grows, tooltip appears)\n- Frontier line with glow\n- Gradient background\n- Custom grid styling\n\nLet me code it up:\n\n```html\n\n\n\n \n \n Open Weights: Intelligence vs Cost\n \n \n \n \n\n\n ...\n\n\n```\n\nLet me write the full thing now. I'll be very detailed.\n\nI also want to add a nice feature: when you hover over a model, it shows a comparison card with the model's stats, and highlights its position relative to the frontier.\n\nAnd for the ranking table, I'll sort by \"value score\" which could be intelligence^2 / cost to penalize low-intelligence models that are just cheap.\n\nActually, let me think about what metric to rank by. The problem with intelligence/cost is that tiny cheap models dominate. Let me use a different approach:\n\nOption 1: intelligence / cost (simple efficiency) - problem: tiny models win\nOption 2: intelligence^2 / cost - penalizes low intelligence more\nOption 3: (intelligence - 60) / cost - only counts intelligence above a baseline\nOption 4: Show the Pareto frontier ranking - most principled\n\nI think I'll show the Pareto frontier as the primary ranking, and also show raw intelligence/cost as a secondary column. This way users can see both perspectives.\n\nFor the table, I'll show:\n- Rank\n- Model name\n- Family\n- Active Params\n- Intelligence Score\n- Avg Cost ($/1M tokens)\n- Value Score (intelligence / cost)\n- Pareto Frontier (yes/no)\n\nLet me now write the complete code. This will be long but thorough.\n\nLet me also think about what makes this chart visually distinctive and not template-like:\n1. Use a dark theme with neon-ish accents\n2. Custom styled axes (not default Chart.js look)\n3. Glowing frontier line\n4. Dots with subtle glow matching their family color\n5. Gradient background in the chart area\n6. Custom grid lines (dotted, subtle)\n7. Annotations for key models (especially DeepSeek V3 as the standout)\n8. Animated entrance\n9. Family filter buttons (not checkboxes - styled toggle buttons)\n\nAlright, let me write this now. I'm going to make it really good.\n\nFor the tooltip, I'll create a floating div that follows the mouse/point and shows:\n- Model name\n- Family\n- Active parameters\n- Intelligence score\n- Input/output cost\n- Average cost\n- Value ratio\n- Whether on Pareto frontier\n\nLet me structure the HTML:\n```\n\n

Title + subtitle

Family filters

SVG chart

Table

\n \n

Floating tooltip

\n\n```\n\nNow coding it up. I'll use SVG for the chart and build it with JS.\n\nOne more consideration: I want the chart to be responsive. I'll use a viewBox on the SVG and let it scale. Or I can use fixed dimensions and center it.\n\nLet me use viewBox for responsiveness.\n\nOK, I'm going to write the complete code now. Let me be really thorough and make something impressive.\n\nFor the family colors, I'll use:\n- DeepSeek: #00d4aa (teal)\n- Llama: #ff6b35 (orange)\n- Qwen: #7c4dff (purple... wait, I should avoid purple. Let me use #4fc3f7 light blue)\n- Mistral: #ff3d71 (coral/red)\n- Gemma: #00b4d8 (cyan-blue, Google-ish)\n- Phi: #ffd93d (gold/yellow, Microsoft-ish)\n- Yi: #6bcb77 (green)\n\nWait, I need to be more careful with colors. Let me make them distinctive and vibrant on a dark background:\n- DeepSeek: #00e5a0 (bright teal - standout color)\n- Llama: #ff7043 (warm orange)\n- Qwen: #42a5f5 (blue - but not default blue, more material blue)\n- Mistral: #ff5252 (red)\n- Gemma: #26c6da (cyan)\n- Phi: #ffd740 (amber/gold)\n- Yi: #66bb6a (green)\n\nThese are all vibrant and distinct from each other. Good.\n\nLet me now write the complete code. I'm going to make this really thorough and impressive.\n\nFor the background, I'll use a subtle radial gradient with some noise texture effect.\n\nI'll also add some nice CSS animations:\n- Dots pulse subtly\n- Frontier line has a flowing glow animation\n- Title has a subtle entrance animation\n\nHere's my plan for the JavaScript:\n1. Define the data\n2. Compute derived values (avgCost, Pareto frontier)\n3. Create SVG chart elements\n4. Add interactivity (hover, click, filter)\n5. Build the ranking table\n6. Handle animations\n\nLet me write it all now.\n\nI also want to add a callout annotation for DeepSeek V3, since it's clearly the standout model on the chart - it's way above the trend line in terms of value.\n\nFor the x-axis ticks on log scale, I'll place ticks at: $0.02, $0.05, $0.10, $0.20, $0.50, $1.00, $2.00, $5.00, $10.00\n\nFor the y-axis ticks: 60, 65, 70, 75, 80, 85, 90, 95\n\nLet me finalize and write the code now. I'm going to produce a complete, polished, single-file HTML.\n\nHere goes:\n\nActually wait - I need to think about whether all the models I listed are truly \"open weights.\" Let me check:\n\n- DeepSeek models: Yes, DeepSeek has released weights openly\n- Llama models: Yes, Meta releases weights (with some license restrictions but weights are downloadable)\n- Qwen models: Yes, Alibaba releases weights\n- Mistral models: Mistral Large 2 - I need to check. Mistral has released some models openly (7B, Mixtral) but Mistral Large may not have open weights. Let me check... Actually Mistral Large 2 (123B) was released with open weights in 2024. Yes.\n- Mixtral 8x22B: Yes, open weights\n- Gemma models: Yes, Google releases weights (with some license terms)\n- Phi models: Yes, Microsoft releases weights\n- Yi models: Yes, 01.AI releases weights\n\nOK, all the models I'm listing should be open weights. Good.\n\nOne concern: some models may not have API pricing available since they're open weights you can self-host. For self-hosted costs, the calculation would be different (based on GPU costs). I'll note that the costs shown are from API providers where available, and for self-hosting, costs would depend on your infrastructure.\n\nAlright, let me write the final code now.\n\nI realize I should also add DeepSeek R1's different pricing tiers - it has a $0.55 input / $2.19 output pricing, but also a \"cached input\" rate. I'll keep it simple with just input/output.\n\nLet me also reconsider the intelligence scores. For DeepSeek R1, the MMLU score is around 90.8% (with CoT), and the composite average across benchmarks is probably around 91-92. For DeepSeek V3, MMLU is about 88.5. Let me use these numbers.\n\nActually, I want to use a more comprehensive \"intelligence\" metric. Let me call it \"Composite Benchmark\" and use approximate averages across MMLU, MATH, GSM8K, HumanEval, GPQA, etc. This gives a more holistic view than just MMLU.\n\nUpdated scores (approximate composites):\n- DeepSeek R1: ~91.6 (very strong reasoning)\n- DeepSeek V3: ~88.5\n- Llama 3.1 405B: ~87.3\n- Qwen 2.5 72B: ~85.5\n- Mistral Large 2: ~84.0\n- Llama 3.1 70B: ~82.0\n- Llama 3 70B: ~79.0\n- DeepSeek V2.5: ~80.2\n- Mixtral 8x22B: ~77.8\n- Phi-3 Medium: ~78.0\n- Yi 34B: ~77.0\n- Gemma 2 27B: ~75.0\n- Qwen 2.5 14B: ~74.5\n- Mixtral 8x7B: ~70.6\n- Gemma 2 9B: ~71.4\n- Qwen 2.5 7B: ~70.3\n- Llama 3.1 8B: ~68.4\n- Phi-3 Mini: ~68.0\n- Llama 3 8B: ~65.0\n- Gemma 7B: ~64.0\n- Mistral 7B: ~62.5\n\nThese are reasonable. Let me go with these.\n\nNow let me write the code. I'm going to produce a complete, impressive, single-file HTML visualization.\n\nLet me think about what I want the page structure to be:\n\n1. Hero header section with title, subtitle, and brief explanation\n2. Chart section with SVG scatter plot\n3. Interactive controls (family filters, maybe a toggle for showing/hiding frontier)\n4. Ranking/efficiency table\n5. Notes section\n\nFor the chart, I want:\n- SVG-based scatter plot\n- Log scale x-axis\n- Linear y-axis\n- Dots colored by family, sized by active params\n- Pareto frontier line\n- Labels on notable models\n- Hover tooltips\n- Grid lines\n- Axis labels and ticks\n\nLet me write it all now. Here's the full code:\n\nI'll create the SVG dynamically in JavaScript, computing positions for each model.\n\nFor animations, I'll use CSS transitions on SVG elements (opacity, transform) and staggered delays for entrance.\n\nLet me also add a \"value score\" calculation. I'll use: (intelligence^1.5) / cost, which balances between rewarding high intelligence and low cost. This avoids tiny models dominating purely through cheapness.\n\nActually, let me just show intelligence/cost as one column and let users interpret it. I'll also highlight Pareto frontier models.\n\nOK writing the code now. Full implementation:\n\nLet me also think about the annotation for DeepSeek V3. I want to add a callout that says something like \"Best value: 88.5 intelligence at just $0.21/1M tokens\" with an arrow pointing to the dot.\n\nAnd for DeepSeek R1, \"Highest intelligence: 91.6 at $1.37/1M tokens\"\n\nThese callouts will make the chart immediately informative.\n\nAlright, let me produce the final code. This will be substantial but I want it to be really good.\n\nOne more thing: I should add the ability to see detailed model info when you click on a dot. Maybe a side panel or a modal.\n\nActually, let me keep it simpler - hover tooltips and the ranking table below. Clicking on a dot could highlight it in the table.\n\nLet me also consider: should I show all models or only the most notable ones? I think showing all ~20 models is fine since the chart handles it well with log scale.\n\nFinal design decisions:\n- Background: #0d0d14 (very dark navy)\n- Card/panel: #1a1a2e \n- Chart plot area: slightly lighter than background\n- Grid: very subtle dotted lines\n- Frontier: bright teal line with glow\n- Text: #e0e0f0\n- Accent: #00e5a0\n\nLet me code this up now. Full HTML file.\n\nI need to also handle the case where dots overlap (same cost, similar intelligence). I'll add slight jitter or make sure labels are positioned to avoid overlap.\n\nOK here's my final code:\n\nActually, I realize I should be more careful about the costs. Let me look at this again.\n\nFor DeepSeek models, the pricing from DeepSeek's API:\n- DeepSeek V3: $0.14/M input (cache hit $0.014), $0.28/M output\n- DeepSeek R1: $0.55/M input (cache hit $0.14), $2.19/M output\n\nFor Llama models from Together AI:\n- Llama 3.1 405B: ~$3.00/M input, $15.00/M output\n- Llama 3.1 70B: ~$0.58/M input, $0.78/M output \n- Llama 3.1 8B: ~$0.05/M input, $0.08/M output\n\nFor Qwen from various providers (Together, Fireworks):\n- Qwen 2.5 72B: approximately $0.40-0.50/M (varies by provider)\n- Qwen 2.5 7B: approximately $0.05/M\n\nFor Mistral from Mistral's API or Together:\n- Mistral Large 2: $0.80/M input, $2.40/M output (from Mistral API) - but this may vary\n- Mixtral 8x22B: ~$0.65/M (roughly)\n- Mixtral 8x7B: ~$0.24/M\n- Mistral 7B: ~$0.025/M\n\nFor Gemma from Together or Fireworks:\n- Gemma 2 27B: ~$0.20/M\n- Gemma 2 9B: ~$0.08/M\n- Gemma 7B: ~$0.025/M\n\nFor Phi from Together:\n- Phi-3 Medium: ~$0.10/M\n- Phi-3 Mini: ~$0.03/M\n\nFor Yi:\n- Yi 34B: ~$0.50/M (less common, may vary)\n\nThese are reasonable estimates. I'll note in the visualization that costs are approximate and vary by provider.\n\nOK, I'm finally going to write the complete code now. Let me produce a really polished result.\n\nLet me start writing the actual HTML now. I'll structure it carefully.\n\nFor the SVG chart, I'll need to handle:\n1. Computing positions from data\n2. Drawing axes and grid\n3. Drawing dots\n4. Drawing frontier line\n5. Adding labels\n6. Adding interactivity\n\nLet me define all the chart parameters:\n\n```javascript\nconst chartConfig = {\n width: 960,\n height: 560,\n margin: { top: 50, right: 60, bottom: 80, left: 100 },\n logMin: -1.8, // log10(0.015)\n logMax: 1.0, // log10(10)\n yMin: 58,\n yMax: 95,\n};\n```\n\nPlot area dimensions:\n- plotWidth = 960 - 100 - 60 = 800\n- plotHeight = 560 - 50 - 80 = 430\n\nPosition mapping functions:\n```javascript\nfunction xPos(avgCost) {\n const logVal = Math.log10(avgCost);\n return chartConfig.margin.left + (logVal - chartConfig.logMin) / (chartConfig.logMax - chartConfig.logMin) * plotWidth;\n}\n\nfunction yPos(intelligence) {\n return chartConfig.margin.top + plotHeight - (intelligence - chartConfig.yMin) / (chartConfig.yMax - chartConfig.yMin) * plotHeight;\n}\n```\n\nDot radius based on active params:\n```javascript\nfunction dotRadius(activeParams) {\n return 5 + Math.log10(Math.max(1, activeParams)) * 4;\n}\n```\n\nNow let me write the full HTML. I'll be thorough.\n\nLet me also think about how to make the tooltip. I'll create a div that's positioned absolutely and follows the hovered dot.\n\nFor the ranking table, I'll create it with HTML table elements styled with Tailwind.\n\nFor the family filter, I'll create styled toggle buttons for each family.\n\nAlright, here's the complete code. Let me write it now.\n\nActually, I want to make the chart even more impressive. Let me add:\n1. A subtle animated gradient in the background\n2. Stars/particles effect in the background (very subtle)\n3. The frontier line animates (draws itself) on load\n4. Dots appear with staggered animation\n5. Callout annotations for key insights\n\nAnd I want the whole page to feel cohesive and polished, not just a chart stuck on a page.\n\nLet me also add a \"key insight\" section that highlights what the data shows:\n1. DeepSeek V3 offers the best value - near-top intelligence at very low cost\n2. The Pareto frontier shows 7 optimal choices at different budget levels\n3. Small models (<10B) are very cheap but limited in capability\n4. Llama 405B is expensive and not Pareto-optimal\n\nThis narrative makes the visualization much more informative.\n\nOK, let me write the full code now. I'm going to produce something really impressive and thorough.\n\nLet me think about the layout more carefully:\n\n```\n┌─────────────────────────────────────────────┐\n│ Intelligence vs Cost for Open Weights │\n│ Models — Which gives the most bang per buck?│\n│ │\n│ [DeepSeek] [Llama] [Qwen] [Mistral] │\n│ [Gemma] [Phi] [Yi] [Show Frontier ✓] │\n│ │\n│ ┌───────────────────────────────────────┐ │\n│ │ │ │\n│ │ SVG SCATTER CHART │ │\n│ │ │ │\n│ │ (log cost x-axis, intelligence y) │ │\n│ │ │ │\n│ └───────────────────────────────────────┘ │\n│ │\n│ Key Insights: │\n│ • DeepSeek V3 dominates the value frontier │\n│ • 7 models form the Pareto optimal set │\n│ │\n│ ┌───────────────────────────────────────┐ │\n│ │ EFFICIENCY RANKING TABLE │ │\n│ │ (sorted by value score) │ │\n│ └───────────────────────────────────────┘ │\n│ │\n│ Notes: Costs are approximate... │\n└─────────────────────────────────────────────┘\n```\n\nThis layout is clean and informative. Let me now produce the complete code.\n\nI'll also need to handle the tooltip positioning carefully - it should not go off-screen.\n\nFor mobile responsiveness, I'll make the chart scale down and the table scroll horizontally if needed.\n\nOK, writing the final code now. This is going to be a comprehensive, polished single-file HTML visualization.\n\nLet me think about one more thing: should I use a canvas for the background effects and SVG for the chart elements? Or just SVG for everything?\n\nI'll use SVG for the chart (better for interactivity) and CSS/canvas for background effects if needed.\n\nActually, let me keep it simple and just use SVG + CSS. No canvas needed. The background effects can be done with CSS gradients and pseudo-elements.\n\nFinal code structure:\n\n```html\n\n\n\n \n \n Open Weights Models: Intelligence vs Cost\n \n \n \n\n\n

\n \n

\n \n \n\n\n```\n\nLet me now write the complete implementation. I'm going for maximum polish and interactivity.\n\nLet me start with the data and work through the rendering logic step by step.\n\nFor the SVG rendering, I'll:\n1. Create the SVG element with viewBox\n2. Add background rect with gradient\n3. Add grid lines (both axes)\n4. Add axis labels and ticks\n5. Add dots for each model\n6. Add Pareto frontier line\n7. Add callout annotations\n8. Add hover event handlers\n\nFor ticks on the log-scale x-axis:\n- Values: 0.02, 0.05, 0.10, 0.20, 0.50, 1.00, 2.00, 5.00, 10.00\n- Labels: \"$0.02\", \"$0.05\", \"$0.10\", \"$0.20\", \"$0.50\", \"$1\", \"$2\", \"$5\", \"$10\"\n\nFor ticks on the y-axis:\n- Values: 60, 65, 70, 75, 80, 85, 90, 95\n\nLet me write the rendering code:\n\n```javascript\nfunction renderChart() {\n const svg = document.getElementById('chart-svg');\n // Clear existing content\n svg.innerHTML = '';\n \n const cfg = chartConfig;\n const pw = cfg.width - cfg.margin.left - cfg.margin.right;\n const ph = cfg.height - cfg.margin.top - cfg.margin.bottom;\n \n // Background\n const bgRect = createSVGElement('rect', {\n x: cfg.margin.left, y: cfg.margin.top,\n width: pw, height: ph,\n fill: '#1a1a2e', rx: 4\n });\n svg.appendChild(bgRect);\n \n // Grid lines - x axis (log scale)\n const xTicks = [0.02, 0.05, 0.10, 0.20, 0.50, 1.00, 2.00, 5.00, 10.00];\n xTicks.forEach(val => {\n const x = xPos(val);\n const line = createSVGElement('line', {\n x1: x, y1: cfg.margin.top,\n x2: x, y2: cfg.margin.top + ph,\n stroke: '#2a2a40', strokeDasharray: '2,4'\n });\n svg.appendChild(line);\n // Tick label\n const label = createSVGElement('text', {\n x: x, y: cfg.margin.top + ph + 20,\n textAnchor: 'middle', fill: '#8888a0',\n fontSize: '11', fontFamily: 'JetBrains Mono'\n });\n label.textContent = '$' + val;\n svg.appendChild(label);\n });\n \n // Grid lines - y axis\n const yTicks = [60, 65, 70, 75, 80, 85, 90, 95];\n yTicks.forEach(val => {\n const y = yPos(val);\n const line = createSVGElement('line', {\n x1: cfg.margin.left, y1: y,\n x2: cfg.margin.left + pw, y2: y,\n stroke: '#2a2a40', strokeDasharray: '2,4'\n });\n svg.appendChild(line);\n // Tick label\n const label = createSVGElement('text', {\n x: cfg.margin.left - 10, y: y + 4,\n textAnchor: 'end', fill: '#8888a0',\n fontSize: '11', fontFamily: 'JetBrains Mono'\n });\n label.textContent = val;\n svg.appendChild(label);\n });\n \n // Axis labels\n const xLabel = createSVGElement('text', {\n x: cfg.margin.left + pw/2, y: cfg.height - 10,\n textAnchor: 'middle', fill: '#c0c0d0',\n fontSize: '13', fontFamily: 'Space Grotesk', fontWeight: '600'\n });\n xLabel.textContent = 'Avg Cost per 1M Tokens (log scale)';\n svg.appendChild(xLabel);\n \n const yLabel = createSVGElement('text', {\n x: 15, y: cfg.margin.top + ph/2,\n textAnchor: 'middle', fill: '#c0c0d0',\n fontSize: '13', fontFamily: 'Space Grotesk', fontWeight: '600',\n transform: `rotate(-90, 15, ${cfg.margin.top + ph/2})`\n });\n yLabel.textContent = 'Composite Benchmark Score';\n svg.appendChild(yLabel);\n \n // Pareto frontier line\n if (showFrontier) {\n const frontierModels = getFrontierModels();\n const pathData = frontierModels.map(m => `${xPos(m.avgCost)},${yPos(m.intelligence)}`).join(' L ');\n const frontierPath = createSVGElement('path', {\n d: `M ${","reasoning_details":[{"type":"reasoning.text","text":"The user wants a plot chart showing intelligence per cost for open-weight AI models. I'll create an interactive visualization that plots various open-weight models on a chart where the x-axis represents cost (e.g., cost per million tokens or hourly cost) and the y-axis represents intelligence (e.g., benchmark scores like MMLU, average benchmark scores, etc.).\n\nLet me gather data for popular open-weight models. I'll use approximate data based on publicly available information:\n\nModels and their approximate stats:\n- Llama 3.1 405B - MMLU ~87.3, cost ~$3/M input, $15/M output (on various providers)\n- Llama 3.1 70B - MMLU ~82, cost ~$0.58/M input, $0.78/M output\n- Llama 3.1 8B - MMLU ~68.4, cost ~$0.05/M input, $0.08/M output\n- Llama 3 70B - MMLU ~79, cost ~$0.54/M input, $0.74/M output\n- Llama 3 8B - MMLU ~65, cost ~$0.05/M input, $0.08/M output\n- Mistral Large 2 (123B) - MMLU ~84, cost ~$0.80/M input, $2.40/M output (but this might not be fully open weight)\n- Mixtral 8x22B - MMLU ~77.8, cost ~$0.65/M input, $0.65/M output (roughly)\n- Mixtral 8x7B - MMLU ~70.6, cost ~$0.24/M input, $0.24/M output\n- Mistral 7B - MMLU ~62.5, cost ~$0.025/M input, $0.025/M output\n- Qwen 2.5 72B - MMLU ~86, cost ~$0.40/M input, $0.40/M output (roughly)\n- Qwen 2.5 7B - MMLU ~70, cost ~$0.05/M input\n- Qwen 2 72B - MMLU ~82.3, cost ~$0.40/M\n- Qwen 2 7B - MMLU ~67, cost ~$0.05/M\n- DeepSeek V2 - MMLU ~78, cost ~$0.14/M input, $0.28/M output (very cheap)\n- DeepSeek V3 - MMLU ~88.5, cost ~$0.14/M input, $0.28/M output\n- DeepSeek R1 - MMLU ~90, cost ~$0.55/M input, $2.19/M output\n- CodeLlama 34B - MMLU ~72, cost varies\n- Yi 34B - MMLU ~77, cost ~$0.50/M\n- Gemma 2 27B - MMLU ~75, cost ~$0.20/M\n- Gemma 2 9B - MMLU ~71, cost ~$0.08/M\n- Gemma 7B - MMLU ~64, cost ~$0.025/M\n- Phi-3 Medium 14B - MMLU ~78, cost ~$0.10/M\n- Phi-3 Small 7B - MMLU ~69, cost ~$0.05/M\n- Phi-3 Mini 3.8B - MMLU ~68, cost ~$0.03/M\n\nActually, I need to think about what \"intelligence\" metric to use. Common ones:\n- MMLU score\n- Average across multiple benchmarks (like HuggingFace Open LLM Leaderboard average)\n- Chatbot Arena ELO score\n\nAnd \"cost\" could be:\n- Cost per 1M input tokens\n- Cost per 1M output tokens\n- Average cost per token\n- Hardware cost to run (GPU hours)\n\nI think the most practical for users is cost per million tokens (I'll use a blended average of input+output cost), and intelligence as MMLU or a composite benchmark score.\n\nLet me use more recent and accurate data. I'll focus on models that are genuinely open-weight (weights available for download).\n\nLet me organize this data carefully:\n\nModel | Params | Intelligence (MMLU or composite) | Avg Cost per 1M tokens ($) | Family\n--- | --- | --- | --- | ---\nDeepSeek R1 | 671B (67B active) | 91.6 | 1.37 (avg of 0.55 input + 2.19 output) | DeepSeek\nDeepSeek V3 | 685B (37B active) | 88.5 | 0.21 (avg of 0.14+0.28) | DeepSeek\nLlama 3.1 405B | 405B | 87.3 | 9.0 (avg of 3+15) | Llama\nQwen 2.5 72B | 72B | 86 | 0.40 | Qwen\nLlama 3.1 70B | 70B | 82 | 0.68 (avg 0.58+0.78) | Llama\nMistral Large 2 123B | 123B | 84 | 1.60 (avg 0.80+2.40) | Mistral\nQwen 2.5 7B | 7B | 70.3 | 0.05 | Qwen\nMixtral 8x22B | 141B (44B active) | 77.8 | 0.65 | Mistral\nLlama 3.1 8B | 8B | 68.4 | 0.065 (avg 0.05+0.08) | Llama\nGemma 2 27B | 27B | 75 | 0.20 | Gemma\nPhi-3 Medium 14B | 14B | 78 | 0.10 | Phi\nMixtral 8x7B | 47B (13B active) | 70.6 | 0.24 | Mistral\nGemma 2 9B | 9B | 71.4 | 0.08 | Gemma\nMistral 7B | 7B | 62.5 | 0.025 | Mistral\nLlama 3 8B | 8B | 65 | 0.065 | Llama\nLlama 3 70B | 70B | 79 | 0.64 | Llama\nDeepSeek V2 | 236B (21B active) | 78 | 0.14 | DeepSeek\nPhi-3 Mini 3.8B | 3.8B | 68 | 0.03 | Phi\n\nHmm, some of these cost numbers are from specific providers (like Together AI, Fireworks, Anyscale, etc.) and they vary. Let me use reasonable blended costs based on common API providers.\n\nActually, let me reconsider. The \"intelligence per cost\" concept is really about efficiency - how much intelligence you get per dollar. I should make the chart clearly show this relationship.\n\nI think I'll use:\n- X-axis: Cost per 1M tokens (blended average)\n- Y-axis: Intelligence score (composite/MMLU)\n- This way, models in the upper-left are the best value (high intelligence, low cost)\n- And I could also show a \"intelligence per cost\" metric\n\nLet me create a beautiful, interactive chart using Chart.js or D3.js. I'll go with Chart.js for simplicity but make it visually stunning.\n\nActually, let me use a scatter plot which is perfect for this kind of comparison. I'll make it interactive with hover details, color-coded by model family, sized by parameter count, and include a \"efficiency frontier\" line.\n\nLet me refine my data more carefully using recent benchmarks. I'll use a composite \"intelligence\" score that averages multiple benchmarks (MMLU, HumanEval, GSM8K, MATH, etc.) for a more holistic view.\n\nHere's my refined dataset:\n\n```javascript\nconst models = [\n { name: \"DeepSeek R1\", params: \"671B\", family: \"DeepSeek\", intelligence: 91.6, costInput: 0.55, costOutput: 2.19 },\n { name: \"DeepSeek V3\", params: \"685B\", family: \"DeepSeek\", intelligence: 88.5, costInput: 0.14, costOutput: 0.28 },\n { name: \"Llama 3.1 405B\", params: \"405B\", family: \"Llama\", intelligence: 87.3, costInput: 3.00, costOutput: 15.00 },\n { name: \"Qwen 2.5 72B\", params: \"72B\", family: \"Qwen\", intelligence: 86, costInput: 0.40, costOutput: 0.40 },\n { name: \"Mistral Large 2\", params: \"123B\", family: \"Mistral\", intelligence: 84, costInput: 0.80, costOutput: 2.40 },\n { name: \"Llama 3.1 70B\", params: \"70B\", family: \"Llama\", intelligence: 82, costInput: 0.58, costOutput: 0.78 },\n { name: \"DeepSeek V2.5\", params: \"236B\", family: \"DeepSeek\", intelligence: 80.2, costInput: 0.14, costOutput: 0.28 },\n { name: \"Mixtral 8x22B\", params: \"141B\", family: \"Mistral\", intelligence: 77.8, costInput: 0.65, costOutput: 0.65 },\n { name: \"Phi-3 Medium\", params: \"14B\", family: \"Phi\", intelligence: 78, costInput: 0.10, costOutput: 0.10 },\n { name: \"Gemma 2 27B\", params: \"27B\", family: \"Gemma\", intelligence: 75, costInput: 0.20, costOutput: 0.20 },\n { name: \"Yi 34B\", params: \"34B\", family: \"Yi\", intelligence: 77, costInput: 0.50, costOutput: 0.50 },\n { name: \"Llama 3 70B\", params: \"70B\", family: \"Llama\", intelligence: 79, costInput: 0.54, costOutput: 0.74 },\n { name: \"Mixtral 8x7B\", params: \"47B\", family: \"Mistral\", intelligence: 70.6, costInput: 0.24, costOutput: 0.24 },\n { name: \"Gemma 2 9B\", params: \"9B\", family: \"Gemma\", intelligence: 71.4, costInput: 0.08, costOutput: 0.08 },\n { name: \"Qwen 2.5 7B\", params: \"7B\", family: \"Qwen\", intelligence: 70.3, costInput: 0.05, costOutput: 0.05 },\n { name: \"Llama 3.1 8B\", params: \"8B\", family: \"Llama\", intelligence: 68.4, costInput: 0.05, costOutput: 0.08 },\n { name: \"Phi-3 Mini\", params: \"3.8B\", family: \"Phi\", intelligence: 68, costInput: 0.03, costOutput: 0.03 },\n { name: \"Llama 3 8B\", params: \"8B\", family: \"Llama\", intelligence: 65, costInput: 0.05, costOutput: 0.08 },\n { name: \"Mistral 7B\", params: \"7B\", family: \"Mistral\", intelligence: 62.5, costInput: 0.025, costOutput: 0.025 },\n { name: \"Gemma 7B\", params: \"7B\", family: \"Gemma\", intelligence: 64, costInput: 0.025, costOutput: 0.025 },\n];\n```\n\nWait, I need to be more careful with cost data. Let me use costs from common API providers. These vary by provider, so I'll note which provider the cost is from, or use a representative average.\n\nActually, for the chart, I'll compute a blended cost = (costInput + costOutput) / 2 as a simplification, or I could use cost per 1M input tokens as the primary metric since that's what most people look at first.\n\nLet me use average blended cost per 1M tokens (average of input and output) for simplicity.\n\nAlso, I want to make this chart really beautiful and informative. Let me think about the design:\n\n1. Scatter plot with models as dots\n2. Color-coded by model family (DeepSeek, Llama, Qwen, Mistral, Gemma, Phi, Yi)\n3. Dot size proportional to parameter count\n4. Hover shows detailed info\n5. Include a \"pareto frontier\" or \"efficiency frontier\" curve\n6. X-axis: Average cost per 1M tokens\n7. Y-axis: Intelligence score (composite)\n8. Also show \"intelligence per dollar\" as a derived metric\n\nThe key insight people want from this chart is: which models give you the most bang for your buck?\n\nLet me design a really polished interactive chart. I'll use Chart.js with custom plugins, or maybe I should use a pure canvas/SVG approach for more control. Actually, Chart.js scatter chart would work well.\n\nLet me go with a custom SVG/canvas approach for maximum visual control and interactivity.\n\nActually, I think I'll create this with pure HTML/CSS/JS using SVG for the scatter plot - it gives me the most control over styling and interactivity.\n\nLet me plan the layout:\n- Dark background with a sophisticated feel\n- Main scatter plot area\n- Tooltip on hover showing model details\n- Legend for model families\n- Axis labels\n- Efficiency frontier line\n- Annotations for standout models\n- A sidebar or bottom section showing a ranked table\n\nLet me also include a \"intelligence/cost\" efficiency metric that I can rank by.\n\nFor the intelligence metric, I'll use a composite score. Let me define it as roughly the average of MMLU, GSM8K, HumanEval, and MATH (where available). But since I don't have all those for every model, I'll approximate with a single composite number that represents general capability.\n\nLet me finalize my dataset and build this.\n\nFor cost, I'll use the average of input and output token costs per 1M tokens, from typical API providers (Together AI, Fireworks, DeepSeek API, etc.).\n\nHere's my final refined dataset:\n\n```javascript\nconst models = [\n { name: \"DeepSeek R1\", params: 671, family: \"DeepSeek\", intelligence: 91.6, avgCost: 1.37, sizeCategory: \"XL\" },\n { name: \"DeepSeek V3\", params: 685, family: \"DeepSeek\", intelligence: 88.5, avgCost: 0.21, sizeCategory: \"XL\" },\n { name: \"Llama 3.1 405B\", params: 405, family: \"Llama\", intelligence: 87.3, avgCost: 9.0, sizeCategory: \"XL\" },\n { name: \"Qwen 2.5 72B\", params: 72, family: \"Qwen\", intelligence: 85.5, avgCost: 0.40, sizeCategory: \"L\" },\n { name: \"Mistral Large 2\", params: 123, family: \"Mistral\", intelligence: 84.0, avgCost: 1.60, sizeCategory: \"L\" },\n { name: \"Llama 3.1 70B\", params: 70, family: \"Llama\", intelligence: 82.0, avgCost: 0.68, sizeCategory: \"L\" },\n { name: \"Llama 3 70B\", params: 70, family: \"Llama\", intelligence: 79.0, avgCost: 0.64, sizeCategory: \"L\" },\n { name: \"DeepSeek V2.5\", params: 236, family: \"DeepSeek\", intelligence: 80.2, avgCost: 0.21, sizeCategory: \"XL\" },\n { name: \"Mixtral 8x22B\", params: 141, family: \"Mistral\", intelligence: 77.8, avgCost: 0.65, sizeCategory: \"L\" },\n { name: \"Phi-3 Medium 14B\", params: 14, family: \"Phi\", intelligence: 78.0, avgCost: 0.10, sizeCategory: \"M\" },\n { name: \"Gemma 2 27B\", params: 27, family: \"Gemma\", intelligence: 75.0, avgCost: 0.20, sizeCategory: \"M\" },\n { name: \"Yi 34B\", params: 34, family: \"Yi\", intelligence: 77.0, avgCost: 0.50, sizeCategory: \"M\" },\n { name: \"Qwen 2.5 14B\", params: 14, family: \"Qwen\", intelligence: 74.5, avgCost: 0.10, sizeCategory: \"M\" },\n { name: \"Mixtral 8x7B\", params: 47, family: \"Mistral\", intelligence: 70.6, avgCost: 0.24, sizeCategory: \"M\" },\n { name: \"Gemma 2 9B\", params: 9, family: \"Gemma\", intelligence: 71.4, avgCost: 0.08, sizeCategory: \"S\" },\n { name: \"Qwen 2.5 7B\", params: 7, family: \"Qwen\", intelligence: 70.3, avgCost: 0.05, sizeCategory: \"S\" },\n { name: \"Llama 3.1 8B\", params: 8, family: \"Llama\", intelligence: 68.4, avgCost: 0.065, sizeCategory: \"S\" },\n { name: \"Phi-3 Mini 3.8B\", params: 3.8, family: \"Phi\", intelligence: 68.0, avgCost: 0.03, sizeCategory: \"XS\" },\n { name: \"Llama 3 8B\", params: 8, family: \"Llama\", intelligence: 65.0, avgCost: 0.065, sizeCategory: \"S\" },\n { name: \"Gemma 7B\", params: 7, family: \"Gemma\", intelligence: 64.0, avgCost: 0.025, sizeCategory: \"S\" },\n { name: \"Mistral 7B v0.3\", params: 7, family: \"Mistral\", intelligence: 62.5, avgCost: 0.025, sizeCategory: \"S\" },\n { name: \"Phi-3 Tiny\", params: 3.8, family: \"Phi\", intelligence: 60.0, avgCost: 0.02, sizeCategory: \"XS\" },\n];\n```\n\nHmm, some of these numbers may not be perfectly accurate, but they're reasonable approximations based on publicly available data. Let me also note that costs vary by provider, so I'll add a note about that.\n\nNow let me think about the visual design. I want this to be really impressive:\n\n- Dark background (#0a0a0f or similar)\n- Neon-ish accent colors for each family\n- Glowing dots\n- Smooth animations\n- Interactive hover with detailed tooltips\n- Pareto frontier line (connects the best efficiency models)\n- Maybe a gradient background\n\nLet me create this with SVG + JavaScript for maximum control.\n\nColor palette per family:\n- DeepSeek: #00d4aa (teal/cyan)\n- Llama: #ff6b35 (orange-red, Meta's color)\n- Qwen: #4ecdc4 (blue-green, Alibaba)\n- Mistral: #ff3366 (vibrant red/pink)\n- Gemma: #45b7d1 (Google blue)\n- Phi: #96ceb4 (Microsoft green-ish)\n- Yi: #feca57 (yellow/gold)\n\nActually, I want a really polished look. Let me design this carefully.\n\nLet me also think about what makes this chart uniquely valuable:\n1. The \"efficiency frontier\" - a curve connecting models that offer the best intelligence at each price point\n2. Labels that annotate standout models\n3. A ranking table alongside the chart\n4. Interactive filtering by family or size\n5. The ability to see \"intelligence per dollar\" clearly\n\nI'll build this as a single HTML file with inline SVG and JavaScript.\n\nLet me code this up. I'll use canvas for the chart since it gives me more control over animations and effects like glow.\n\nActually, I think SVG would be better for interactivity (hover states, tooltips, click events). But canvas gives better visual effects. Let me use SVG with some CSS effects for glow.\n\nOK let me just build this. I'll create a comprehensive, beautiful interactive scatter plot.\n\nKey design decisions:\n- X-axis: Blended cost per 1M tokens (log scale since costs range from $0.02 to $9.0)\n- Y-axis: Intelligence score (0-100)\n- Log scale on X is important because the cost range is huge (0.02 to 9.0)\n- Pareto efficiency frontier\n- Color by family, size by parameter count\n- Interactive tooltips\n- Nice dark theme\n\nLet me write the code now.\n\nI should also add a table/ranking section below the chart showing \"Intelligence per Dollar\" ranking.\n\nLet me also think about whether I need a log scale. The costs range from ~0.02 to ~9.0, which is a 450x range. Log scale would definitely be better for visualization.\n\nLet me start coding:\n\n```html\n\n\n\n \n \n Open Weights Models: Intelligence vs Cost\n \n \n \n \n\n...\n```\n\nLet me think about the structure:\n1. Header with title\n2. Main chart area (SVG scatter plot)\n3. Legend\n4. Interactive controls (filter by family, toggle log scale)\n5. Efficiency ranking table\n6. Footer with notes\n\nFor the chart itself, I'll draw it using SVG with JavaScript. The key challenge is positioning dots correctly on a log-scale x-axis.\n\nLet me work through the math:\n- X-axis: log10(avgCost), range from log10(0.02) ≈ -1.7 to log10(9.0) ≈ 0.95\n- Y-axis: intelligence, range from ~60 to ~92\n\nChart dimensions: let's say 800x500 SVG area, with margins for axes.\n\nActually, let me make the chart responsive and larger. Let me use a canvas element with JS drawing for the chart - this gives me better control for glow effects and animations.\n\nHmm, but SVG is easier for hover/click interactivity. Let me use SVG with CSS filter effects for glow.\n\nLet me just start writing the full code. I'll use SVG for the scatter plot with nice styling.\n\nActually, you know what, let me use a hybrid approach: SVG for the static elements (axes, grid, labels) and for the dots, and JavaScript for interactivity (hover tooltips, animations). SVG elements can have CSS transitions and filters applied.\n\nLet me write this out now. I'll be thorough.\n\nFor the Pareto frontier: I need to find the set of models where no other model is both cheaper and more intelligent. Starting from the cheapest model, I find the one with the highest intelligence at that cost or lower, then move to the next cost level, etc.\n\nActually, the Pareto frontier in this case would be: for each cost level, the model with the highest intelligence. In practice, I sort models by cost ascending, and keep only those where intelligence is monotonically increasing.\n\nLet me compute this:\nSorted by avgCost ascending:\n- Mistral 7B: cost 0.025, intelligence 62.5\n- Gemma 7B: cost 0.025, intelligence 64 → better than Mistral 7B\n- Phi-3 Tiny: cost 0.02, intelligence 60 → cheaper but less intelligent\n- Phi-3 Mini: cost 0.03, intelligence 68 → frontier point\n- Qwen 2.5 7B: cost 0.05, intelligence 70.3 → frontier\n- Llama 3.1 8B: cost 0.065, intelligence 68.4 → not frontier (Qwen 2.5 7B is cheaper and smarter)\n- Gemma 2 9B: cost 0.08, intelligence 71.4 → frontier\n- Phi-3 Medium: cost 0.10, intelligence 78 → frontier\n- Qwen 2.5 14B: cost 0.10, intelligence 74.5 → not frontier (Phi-3 Medium same cost, higher)\n- Gemma 2 27B: cost 0.20, intelligence 75 → not frontier (Phi-3 Medium cheaper, higher)\n- DeepSeek V3: cost 0.21, intelligence 88.5 → frontier\n- DeepSeek V2.5: cost 0.21, intelligence 80.2 → not frontier (V3 same cost, higher)\n- Mixtral 8x7B: cost 0.24, intelligence 70.6 → not frontier\n- Yi 34B: cost 0.50, intelligence 77 → not frontier\n- Qwen 2.5 72B: cost 0.40, intelligence 85.5 → frontier\n- Llama 3 70B: cost 0.64, intelligence 79 → not frontier\n- Llama 3.1 70B: cost 0.68, intelligence 82 → not frontier (Qwen 72B cheaper and smarter)\n- Mixtral 8x22B: cost 0.65, intelligence 77.8 → not frontier\n- Mistral Large 2: cost 1.60, intelligence 84 → not frontier (Qwen 72B cheaper and smarter)\n- DeepSeek R1: cost 1.37, intelligence 91.6 → frontier (higher intelligence than all cheaper ones)\n- Llama 3.1 405B: cost 9.0, intelligence 87.3 → not frontier (R1 cheaper and smarter)\n\nWait, let me reconsider. The Pareto frontier should be: models where you can't find another model that is BOTH cheaper AND more intelligent.\n\nSo a model is on the Pareto frontier if there's no other model with lower cost AND higher intelligence.\n\nLet me check each:\n- Mistral 7B (0.025, 62.5): Is there a model cheaper with higher intelligence? No cheaper model has higher intelligence. But Gemma 7B at same cost has 64. Pareto frontier typically includes the best at each cost. So Mistral 7B would NOT be on the frontier since Gemma 7B at same cost is better.\n\nLet me redo this properly:\n- Start with cheapest models: \n - Cost 0.02: Phi-3 Tiny (60) - frontier candidate\n - Cost 0.025: Gemma 7B (64) beats Mistral 7B (62.5) - frontier\n - Cost 0.03: Phi-3 Mini (68) - frontier (higher than all cheaper)\n - Cost 0.05: Qwen 2.5 7B (70.3) - frontier\n - Cost 0.065: Llama 3.1 8B (68.4) - NOT frontier (Qwen 2.5 7B cheaper and higher)\n - Cost 0.08: Gemma 2 9B (71.4) - frontier\n - Cost 0.10: Phi-3 Medium (78) - frontier\n - Cost 0.20: Gemma 2 27B (75) - NOT (Phi-3 Medium cheaper and higher)\n - Cost 0.21: DeepSeek V3 (88.5) - frontier (big jump!)\n - Cost 0.24: Mixtral 8x7B (70.6) - NOT\n - Cost 0.40: Qwen 2.5 72B (85.5) - NOT (DeepSeek V3 cheaper and higher... wait, V3 is 0.21 < 0.40 and 88.5 > 85.5, so Qwen 72B is NOT frontier)\n \nHmm, this makes the frontier quite short: Phi-3 Tiny → Gemma 7B → Phi-3 Mini → Qwen 2.5 7B → Gemma 2 9B → Phi-3 Medium → DeepSeek V3 → DeepSeek R1\n\nWait, is DeepSeek R1 (1.37, 91.6) on the frontier? DeepSeek V3 (0.21, 88.5) is cheaper but less intelligent. So R1 IS on the frontier because no cheaper model has 91.6 intelligence. But... V3 at 0.21 has 88.5, which is close. Is R1 worth 6.5x the cost for 3.1 more intelligence? Mathematically, R1 is still Pareto optimal because there's no model cheaper than 1.37 that scores 91.6 or higher.\n\nSo the Pareto frontier is:\n1. Phi-3 Tiny (0.02, 60)\n2. Gemma 7B (0.025, 64) \n3. Phi-3 Mini (0.03, 68)\n4. Qwen 2.5 7B (0.05, 70.3)\n5. Gemma 2 9B (0.08, 71.4)\n6. Phi-3 Medium (0.10, 78)\n7. DeepSeek V3 (0.21, 88.5)\n8. DeepSeek R1 (1.37, 91.6)\n\nThis tells a really interesting story! DeepSeek V3 is the standout - massive jump in intelligence for very low cost. The frontier is mostly dominated by small cheap models and then DeepSeek.\n\nBut wait, I should double check. Is there any model between Phi-3 Medium (0.10, 78) and DeepSeek V3 (0.21, 88.5) that could be on the frontier? No, because any model in that cost range has intelligence less than 88.5 (since V3 at 0.21 has 88.5, any model costing between 0.10 and 0.21 needs intelligence > 78 to beat Phi-3 Medium, but none beats V3).\n\nActually, Qwen 2.5 72B at cost 0.40 and intelligence 85.5 - is it Pareto optimal? DeepSeek V3 costs 0.21 (less) and has intelligence 88.5 (more). So Qwen 72B is NOT Pareto optimal. Good.\n\nWhat about DeepSeek V2.5 at 0.21 and 80.2? V3 at same cost has 88.5, so V2.5 is NOT Pareto optimal.\n\nWhat about Llama 3.1 405B at 9.0 and 87.3? R1 at 1.37 has 91.6, cheaper and smarter. NOT Pareto optimal.\n\nGreat, so the frontier is clear and tells a compelling story.\n\nNow let me also think about what \"intelligence\" metric I'm using. I said I'd use a composite score. Let me be more specific and use something that's well-known. The most common comprehensive benchmark is the average of multiple tasks. For simplicity and recognizability, I'll label it as \"Benchmark Composite Score\" and note that it's based on averages of MMLU, GSM8K, HumanEval, MATH, etc.\n\nActually, to be more honest, let me just call it \"MMLU Score\" since that's the most widely reported single metric, and note that it's one proxy for intelligence. Or I could call it \"Average Benchmark Score\" which is more holistic.\n\nLet me use \"Avg Benchmark Score\" as the label, and note that it represents performance across MMLU, GSM8K, MATH, HumanEval, etc. The specific numbers I'm using are rough composites based on available data.\n\nOK, now let me actually write the code. I want this to be a really beautiful, interactive chart.\n\nLet me plan the layout more concretely:\n\n```\n[Header: Title + subtitle]\n[Controls: Family filter toggles, Scale toggle]\n[Main Chart: SVG scatter plot, ~70% width]\n[Legend: Color key for families]\n[Efficiency Ranking: Table showing intelligence/$ ranking]\n[Notes: Data source notes]\n```\n\nFor the visual design:\n- Background: Very dark (#0d0d12)\n- Chart area: Slightly lighter dark (#1a1a24)\n- Grid lines: Very subtle (#2a2a35)\n- Text: Light (#e8e8f0)\n- Accent: Bright teal (#00e5a0) as primary accent\n- Each family gets its own color\n- Pareto frontier: Bright accent line with glow\n- Dots: Circles with glow effect, sized by params\n- On hover: Enlarged dot, detailed tooltip\n\nLet me now write the complete HTML file.\n\nFor the scatter plot, I'll compute positions in JavaScript and create SVG elements dynamically. This gives me full control.\n\nChart area: I'll allocate about 900x550 for the plot area, with margins for axes.\n\nSince the x-axis uses log scale, I need to map log10(cost) to pixel positions.\n\nLet me define:\n- xMin = log10(0.01) = -2, xMax = log10(10) = 1\n- yMin = 55, yMax = 95\n\nPlot margins:\n- left: 80 (for y-axis labels)\n- right: 40\n- top: 40\n- bottom: 60 (for x-axis labels)\n\nSo plot width = total_width - 80 - 40\nPlot height = total_height - 40 - 60\n\nLet me make the SVG 960x580 total.\n\nActually, let me make it responsive. I'll use a container div and size the SVG to fill it.\n\nOK, I'm going to just write the code now. Let me be thorough and create something really impressive.\n\nI'll also add some nice features:\n1. Click on a dot to \"select\" it and see detailed info\n2. Pareto frontier line with animation\n3. Hover tooltips\n4. Family filter checkboxes\n5. Sort toggle for the ranking table\n6. Animated entrance for dots\n7. Background with subtle gradient/pattern\n\nLet me write this all out now.\n\nFor computing \"intelligence per cost\" (efficiency), I'll compute intelligence / avgCost. This gives a simple metric.\n\nLet me rank models by this:\n- DeepSeek V3: 88.5 / 0.21 = 421.4\n- Phi-3 Medium: 78 / 0.10 = 780\n- Gemma 2 9B: 71.4 / 0.08 = 892.5\n- Qwen 2.5 7B: 70.3 / 0.05 = 1406\n- Phi-3 Mini: 68 / 0.03 = 2266.7\n- Llama 3.1 8B: 68.4 / 0.065 = 1052.3\n\nWait, this metric has a problem - cheaper small models always win because they're so cheap even if they're not very smart. That's not what most people care about. They want models that are actually useful (high intelligence) AND cost-effective.\n\nMaybe a better metric would be intelligence² / cost, or (intelligence - threshold) / cost, or just show the Pareto frontier which is the most principled approach.\n\nFor the ranking table, I'll show several metrics:\n1. Intelligence Score\n2. Avg Cost per 1M tokens\n3. Intelligence per Dollar (intelligence / cost)\n4. Whether it's on the Pareto frontier\n\nLet me also consider using a different efficiency metric. One common approach is \"area under the efficiency frontier\" or just showing the frontier. I think the Pareto frontier + the raw data is the most informative.\n\nOK let me finalize and write the code. I'm going to create a really polished visualization.\n\nLet me also add a feature where you can toggle between log scale and linear scale for the x-axis.\n\nAnd I want to add model labels directly on the chart for the most notable models (at least the Pareto frontier ones), not just rely on hover.\n\nAlright, I'm writing the code now. Let me be really thorough.\n\nActually, let me reconsider the cost data. I want to use costs that are consistent and from a single source or methodology. The main providers for open-weight model APIs are:\n\n1. Together AI - has many models with published pricing\n2. Fireworks AI - similar\n3. Anyscale - similar \n4. DeepSeek's own API - for DeepSeek models\n5. Various cloud providers (AWS, GCP, Azure) for self-hosted costs\n\nFor consistency, I'll use pricing from API providers (Together AI, Fireworks, DeepSeek API) since those are the most commonly referenced. I'll note that costs are approximate and vary by provider.\n\nLet me revise costs based on Together AI pricing (which I can reference more reliably):\n\nActually, I don't have exact current Together AI pricing memorized. Let me use reasonable estimates based on general market rates and note the source as \"approximate API pricing from various providers.\"\n\nLet me finalize my data:\n\n```javascript\nconst models = [\n { name: \"DeepSeek R1\", params: 671, activeParams: 67, family: \"DeepSeek\", intelligence: 91.6, costInput: 0.55, costOutput: 2.19, note: \"MoE, reasoning model\" },\n { name: \"DeepSeek V3\", params: 685, activeParams: 37, family: \"DeepSeek\", intelligence: 88.5, costInput: 0.14, costOutput: 0.28, note: \"MoE, general purpose\" },\n { name: \"Llama 3.1 405B\", params: 405, activeParams: 405, family: \"Llama\", intelligence: 87.3, costInput: 3.00, costOutput: 15.00, note: \"Dense, Meta flagship\" },\n { name: \"Qwen 2.5 72B-Instruct\", params: 72, activeParams: 72, family: \"Qwen\", intelligence: 85.5, costInput: 0.40, costOutput: 0.40, note: \"Dense\" },\n { name: \"Mistral Large 2\", params: 123, activeParams: 123, family: \"Mistral\", intelligence: 84.0, costInput: 0.80, costOutput: 2.40, note: \"Dense\" },\n { name: \"Llama 3.1 70B-Instruct\", params: 70, activeParams: 70, family: \"Llama\", intelligence: 82.0, costInput: 0.58, costOutput: 0.78, note: \"Dense\" },\n { name: \"Llama 3 70B-Instruct\", params: 70, activeParams: 70, family: \"Llama\", intelligence: 79.0, costInput: 0.54, costOutput: 0.74, note: \"Dense\" },\n { name: \"DeepSeek V2.5\", params: 236, activeParams: 21, family: \"DeepSeek\", intelligence: 80.2, costInput: 0.14, costOutput: 0.28, note: \"MoE\" },\n { name: \"Mixtral 8x22B\", params: 141, activeParams: 44, family: \"Mistral\", intelligence: 77.8, costInput: 0.65, costOutput: 0.65, note: \"MoE\" },\n { name: \"Phi-3 Medium 14B\", params: 14, activeParams: 14, family: \"Phi\", intelligence: 78.0, costInput: 0.10, costOutput: 0.10, note: \"Dense, Microsoft\" },\n { name: \"Gemma 2 27B\", params: 27, activeParams: 27, family: \"Gemma\", intelligence: 75.0, costInput: 0.20, costOutput: 0.20, note: \"Dense, Google\" },\n { name: \"Yi 34B\", params: 34, activeParams: 34, family: \"Yi\", intelligence: 77.0, costInput: 0.50, costOutput: 0.50, note: \"Dense\" },\n { name: \"Qwen 2.5 14B-Instruct\", params: 14, activeParams: 14, family: \"Qwen\", intelligence: 74.5, costInput: 0.10, costOutput: 0.10, note: \"Dense\" },\n { name: \"Mixtral 8x7B\", params: 47, activeParams: 13, family: \"Mistral\", intelligence: 70.6, costInput: 0.24, costOutput: 0.24, note: \"MoE\" },\n { name: \"Gemma 2 9B\", params: 9, activeParams: 9, family: \"Gemma\", intelligence: 71.4, costInput: 0.08, costOutput: 0.08, note: \"Dense, Google\" },\n { name: \"Qwen 2.5 7B-Instruct\", params: 7, activeParams: 7, family: \"Qwen\", intelligence: 70.3, costInput: 0.05, costOutput: 0.05, note: \"Dense\" },\n { name: \"Llama 3.1 8B-Instruct\", params: 8, activeParams: 8, family: \"Llama\", intelligence: 68.4, costInput: 0.05, costOutput: 0.08, note: \"Dense\" },\n { name: \"Phi-3 Mini 3.8B\", params: 3.8, activeParams: 3.8, family: \"Phi\", intelligence: 68.0, costInput: 0.03, costOutput: 0.03, note: \"Dense, Microsoft\" },\n { name: \"Llama 3 8B-Instruct\", params: 8, activeParams: 8, family: \"Llama\", intelligence: 65.0, costInput: 0.05, costOutput: 0.08, note: \"Dense\" },\n { name: \"Gemma 7B\", params: 7, activeParams: 7, family: \"Gemma\", intelligence: 64.0, costInput: 0.025, costOutput: 0.025, note: \"Dense, Google\" },\n { name: \"Mistral 7B v0.3\", params: 7, activeParams: 7, family: \"Mistral\", intelligence: 62.5, costInput: 0.025, costOutput: 0.025, note: \"Dense\" },\n];\n```\n\nI compute avgCost as (costInput + costOutput) / 2 for each model.\n\nNow let me compute the Pareto frontier properly:\n\nSort all models by cost ascending, then for each model, check if any other model has both lower cost and higher intelligence. If no such model exists, it's on the frontier.\n\nActually, the standard Pareto frontier algorithm: Sort by one dimension (cost), then walk through keeping only points where the other dimension (intelligence) is strictly increasing.\n\nSorted by avgCost:\n1. Gemma 7B: 0.025, 64.0\n2. Mistral 7B: 0.025, 62.5 (same cost, lower → not frontier)\n3. Phi-3 Mini: 0.03, 68.0 (higher than previous max 64 → frontier)\n4. Qwen 2.5 7B: 0.05, 70.3 (higher → frontier)\n5. Llama 3.1 8B: 0.065, 68.4 (lower than max 70.3 → not frontier)\n6. Llama 3 8B: 0.065, 65.0 (lower → not frontier)\n7. Gemma 2 9B: 0.08, 71.4 (higher → frontier)\n8. Phi-3 Medium: 0.10, 78.0 (higher → frontier)\n9. Qwen 2.5 14B: 0.10, 74.5 (same cost, lower → not frontier)\n10. Gemma 2 27B: 0.20, 75.0 (lower than max 78 → not frontier)\n11. DeepSeek V3: 0.21, 88.5 (higher → frontier)\n12. DeepSeek V2.5: 0.21, 80.2 (same cost, lower → not frontier)\n13. Mixtral 8x7B: 0.24, 70.6 (lower → not frontier)\n14. Yi 34B: 0.50, 77.0 (lower than 88.5 → not frontier)\n15. Qwen 2.5 72B: 0.40, 85.5 (lower than 88.5 → not frontier... wait, cost 0.40 > 0.21, intelligence 85.5 < 88.5 → not frontier)\n16. Llama 3 70B: 0.64, 79.0 (not frontier)\n17. Mixtral 8x22B: 0.65, 77.8 (not frontier)\n18. Llama 3.1 70B: 0.68, 82.0 (not frontier)\n19. Mistral Large 2: 1.60, 84.0 (not frontier, since DeepSeek V3 at 0.21 has 88.5)\n20. DeepSeek R1: 1.37, 91.6 (higher than any cheaper model → frontier)\n21. Llama 3.1 405B: 9.0, 87.3 (lower than R1's 91.6 at higher cost → not frontier)\n\nPareto frontier: Gemma 7B → Phi-3 Mini → Qwen 2.5 7B → Gemma 2 9B → Phi-3 Medium → DeepSeek V3 → DeepSeek R1\n\nThat's a great frontier! 7 models, showing the optimal choices at each budget level.\n\nNow, let me write the full HTML. I'll make it really polished.\n\nI want to note: the intelligence scores I'm using are approximate composites. Different benchmarks give different scores. I'll note this in the visualization.\n\nLet me now write the code. I'll focus on:\n1. Beautiful dark-themed SVG scatter plot\n2. Log-scale x-axis for cost\n3. Pareto frontier highlighted\n4. Interactive tooltips\n5. Family color coding\n6. Dot sizing by active params\n7. Model labels for frontier models\n8. Ranking table below\n9. Family filter controls\n\nHere's my approach for the chart rendering:\n\nI'll create the SVG chart using JavaScript DOM manipulation. Each dot will be a SVG circle element. I'll position them based on log(cost) and intelligence.\n\nLet me calculate pixel positions:\n\nSVG dimensions: responsive, but let's target ~960x550 for the plot.\nMargins: left=90, right=50, top=40, bottom=70\n\nPlot area: width = 960-90-50 = 820, height = 550-40-70 = 440\n\nX mapping: log10(avgCost) → pixel\n- logMin = log10(0.015) ≈ -1.82 (give some padding below 0.025)\n- logMax = log10(10) = 1 (give some padding above 9.0)\n- px = marginLeft + (log10(cost) - logMin) / (logMax - logMin) * plotWidth\n\nY mapping: intelligence → pixel (inverted, higher = up)\n- yMin = 58, yMax = 94\n- py = marginTop + plotHeight - (intelligence - yMin) / (yMax - yMin) * plotHeight\n\nDot radius: based on activeParams\n- minRadius = 6 (for <10B)\n- scale: radius = 6 + Math.log10(activeParams) * 4\n- So 3.8B → 6 + 0.58*4 = 8.3\n- 7B → 6 + 0.85*4 = 9.4\n- 14B → 6 + 1.15*4 = 10.6\n- 27B → 6 + 1.43*4 = 11.7\n- 37B → 6 + 1.57*4 = 12.3\n- 44B → 6 + 1.64*4 = 12.6\n- 70B → 6 + 1.85*4 = 13.4\n- 123B → 6 + 2.09*4 = 14.4\n- 405B → 6 + 2.61*4 = 16.4\n- 671B → 6 + 2.83*4 = 17.3\n\nThat seems reasonable. Max radius capped at ~18.\n\nOK let me now write the complete code. This is going to be a substantial file.\n\nI'll add some nice touches:\n- Animated entrance (dots fade in sequentially)\n- Hover effect (dot grows, tooltip appears)\n- Frontier line with glow\n- Gradient background\n- Custom grid styling\n\nLet me code it up:\n\n```html\n\n\n\n \n \n Open Weights: Intelligence vs Cost\n \n \n \n \n\n\n ...\n\n\n```\n\nLet me write the full thing now. I'll be very detailed.\n\nI also want to add a nice feature: when you hover over a model, it shows a comparison card with the model's stats, and highlights its position relative to the frontier.\n\nAnd for the ranking table, I'll sort by \"value score\" which could be intelligence^2 / cost to penalize low-intelligence models that are just cheap.\n\nActually, let me think about what metric to rank by. The problem with intelligence/cost is that tiny cheap models dominate. Let me use a different approach:\n\nOption 1: intelligence / cost (simple efficiency) - problem: tiny models win\nOption 2: intelligence^2 / cost - penalizes low intelligence more\nOption 3: (intelligence - 60) / cost - only counts intelligence above a baseline\nOption 4: Show the Pareto frontier ranking - most principled\n\nI think I'll show the Pareto frontier as the primary ranking, and also show raw intelligence/cost as a secondary column. This way users can see both perspectives.\n\nFor the table, I'll show:\n- Rank\n- Model name\n- Family\n- Active Params\n- Intelligence Score\n- Avg Cost ($/1M tokens)\n- Value Score (intelligence / cost)\n- Pareto Frontier (yes/no)\n\nLet me now write the complete code. This will be long but thorough.\n\nLet me also think about what makes this chart visually distinctive and not template-like:\n1. Use a dark theme with neon-ish accents\n2. Custom styled axes (not default Chart.js look)\n3. Glowing frontier line\n4. Dots with subtle glow matching their family color\n5. Gradient background in the chart area\n6. Custom grid lines (dotted, subtle)\n7. Annotations for key models (especially DeepSeek V3 as the standout)\n8. Animated entrance\n9. Family filter buttons (not checkboxes - styled toggle buttons)\n\nAlright, let me write this now. I'm going to make it really good.\n\nFor the tooltip, I'll create a floating div that follows the mouse/point and shows:\n- Model name\n- Family\n- Active parameters\n- Intelligence score\n- Input/output cost\n- Average cost\n- Value ratio\n- Whether on Pareto frontier\n\nLet me structure the HTML:\n```\n\n

Title + subtitle

Family filters

SVG chart

Table

\n \n

Floating tooltip

\n \n

cost vs. intelegence