MicroEvals

How good models are at improving existing systems.

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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