MicroEvals

Ability to generate a complete 3D game in a single self-contained HTML file with Three.js from a long, detailed prompt. Evaluate: working code with no console errors, completeness (all levels, Codex, quiz and Lab Mode), accuracy of CPU architecture concepts, visual quality (bloom, circuit traces, particles), gameplay and fun, performance (60 FPS) and adherence to the prompt specifications.

How good models are at improving existing systems.

guess fav female wrestle by birth chart. correct answer: Sasha Banks

Ranked from highest to lowest overall reasoning, coding, and general intelligence based on shared benchmarks (Artificial Analysis Intelligence Index, SWE-bench Verified, and agentic evaluations), LongCat-2.5 and DeepSeek V4.1 Flash lead this group, while Cohere North Mini Code sits at the bottom due to its tiny 3B active parameter footprint. [1] (https://cohere.com/blog/north-mini-code), [2] (https://surfmind.ai/models/leaderboard), [3] (https://artificialanalysis.ai/models/inkling-2-small), [4] (https://longcat.ai/blog/longcat-2.5/) Intelligence Ranking & Comparison Rank Model Creator Architecture (Total / Active) Key Benchmark Highlights Primary Strength 1 LongCat-2.5 (longcat-2.5-free) Meituan 1.6T / 48B MoE 82.1 PaperBench; 91.9 BrowseComp; 63.1 HLE (w/ tools) Frontier-level multimodal reasoning and long-horizon agentic coding 2 DeepSeek V4.1 Flash (Free) DeepSeek 763B (552B backbone) / 16B active MoE 32.6 AA Intelligence Index; 33.8% Terminal-Bench; 91% GPQA Fast reasoning, terminal/CLI execution, and math/science 3 Inkling 2 Small (Free) Thinking Machines Lab 594B / 17B MoE 31.0 AA Intelligence Index; 82.2% SWE-bench Verified Enterprise knowledge work, finance, vision, and software engineering 4 MiMo Flash (MiMo-V2.6-Flash) Xiaomi 309B / 15B MoE 27.0 AA Intelligence Index; 73.4% SWE-bench (V2) High-speed multimodal tasks and general agentic workflows 5 NVIDIA Nemotron 3 Ultra NVIDIA 550B / 55B Hybrid Mamba-MoE 86.7% GPQA Diamond; 53.9% Terminal-Bench 2.1 High-throughput open-weights reasoning and long-context (1M) orchestration 6 Cohere North Mini Code Cohere 30B / 3B MoE 36.5 AA Coding Index; 75.7% GPQA Diamond Ultra-lightweight local or low-latency agentic coding Note on ranking nuances: LongCat-2.5 and DeepSeek V4.1 Flash trade blows depending on the task—LongCat-2.5 has massive parameter scale (48B active) and leads on research and repository-level benchmarks, while DeepSeek V4.1 Flash edges it out on pure terminal automation and tool-assisted benchmarks. Similarly, NVIDIA Nemotron 3 Ultra scores very high on pure science/math (GPQA) and older Intelligence Index v3 tests (47.7), though it trails Inkling 2 Small and MiMo-V2.6-Flash on newer agentic coding evaluations. [1] (https://artificialanalysis.ai/articles/nvidia-nemotron-3-ultra-released), [2] (https://llm-stats.com/models/deepseek-v4.1-flash), [3] (https://hokai.io/directory/models), [4] (https://artificialanalysis.ai/models/mimo-v2-6-flash), [5] (https://openrouter.ai/nvidia/nemotron-3-ultra-550b-a55b:free), [6] (https://longcat.ai/blog/longcat-2.5/) More High-Capability Free Models to Try If you are accessing these through aggregators like OpenRouter (:free endpoints) or coding harnesses like OpenCode, several other strong zero-cost models are available: [1] (https://openrouter.ai/collections/free-models), [2] (https://www.youtube.com/watch?v=G14kU0VvegM) • MiniMax M3.1 Flash. Available free in coding harnesses like OpenCode alongside LongCat-2.5. It scores 29.1 on the Artificial Analysis Intelligence Index, placing it right between Inkling 2 Small and MiMo-V2.6-Flash. [1] (https://artificialanalysis.ai/models/mimo-v2-6-flash), [2] (https://www.youtube.com/watch?v=G14kU0VvegM), [3] (https://surfmind.ai/models/leaderboard), [4] (https://artificialanalysis.ai/models/inkling-2-small) • NVIDIA Nemotron 3.5 Super VL (Free). A 120B-parameter (12B active) multimodal Mixture-of-Experts model that achieves 71.1% on SWE-bench Verified and excels at vision-language tasks. [1] (https://openrouter.ai/collections/free-models), [2] (https://hokai.io/directory/models) • Google Gemma 4 31B (Free). A dense open-weights multimodal model from Google with strong visual understanding and general reasoning, frequently hosted on OpenRouter's free tier. [1] (https://www.youtube.com/watch?v=l5oBv_Mhf3k&t=73), [2] (https://www.smophy.ai/benchmark/2026-10-09) • NVIDIA Nemotron 3 Super (Free). The 120B text-focused predecessor to 3.5 Super, offering a 260K context window and strong instruction following for high-volume agent loops. [1] (https://www.coderabbit.ai/blog/nemotron-3-ultra-release), [2] (https://www.youtube.com/watch?v=l5oBv_Mhf3k&t=73) • NVIDIA Nemotron 3.5 Lightning (Free). A speed-optimized model scoring 51.56% on SWE-bench Verified while generating over 270 tokens per second. [1] (https://openrouter.ai/collections/free-models), [2] (https://surfmind.ai/models/leaderboard), [3] (https://hokai.io/directory/models) • LiquidAI LFM3-1.7B (Free). An ultra-compact 1.7B reasoning model with a 256K context window that hits 43.4% on SWE-bench Verified, ideal for fast structured outputs and tool calling. [1] (https://openrouter.ai/collections/free-models), [2] (https://hokai.io/directory/models) • OpenRouter Auto-Router (openrouter/free). A meta-endpoint on OpenRouter that automatically routes your prompt to whichever free model best fits your context length and modality requirements. [1] (https://openrouter.ai/collections/free-models) If you'd like to narrow down the best free option for your setup, let me know: • Your primary use case (e.g., agentic coding in VS Code/OpenCode, deep research, or creative writing) • Whether you need image/vision input or 1M+ context windows I can recommend the exact endpoint and configuration with the highest rate limits for your workflow.

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Asking different models to comeout with QA Automation details

An advanced stress test designed to push frontier Large Language Models (LLMs) to their operational limits under strict, multi-layered constraints. The prompt forces the AI to resolve a critical orbital recovery dilemma by calculating weighted utility matrices step-by-step, writing memory-safe embedded Rust code (#![no_std]), conducting ethical analysis under a severe linguistic constraint (an 80+ word paragraph omitting the letter "e"), and enforcing strict JSON output adherence without conversational filler.

FC-BGA 공급업체들의 면적당 CAPA 분석

圆形糖果分别有苹果7、桃9、西瓜8;五角星糖果分别有苹果7、桃6、西瓜4。起码取多少颗，才能确保不同形状的苹果和桃都拿到一颗

How Deep and Accurate the models can Explain a Topic