What is the best open weight reasoning model?

As of October 2026, if you mean the strongest generally available open-weight model for reasoning, my pick is DeepSeek-V4-Pro.

My ranking

1. DeepSeek-V4-Pro - best overall

   • 1.6T total parameters, ~49B active (MoE).
   • 1M-token context.
   • DeepSeek reports it as beating other open models on math, STEM, and coding while approaching top closed models. DeepSeek | DeepSeek-V4 Preview: Entering the Era of Affordable Million-Token Context
   • Excellent choice if you have serious inference hardware.

2. Kimi K3 - best alternative / possibly best on some hard benchmarks

   • Current independent open-model leaderboards put Kimi K3 at the top on Humanity's Last Exam, ahead of GLM-5.3-Flash and DeepSeek V4 variants. Open Source LLM Leaderboard 2026 - Compare Open-Weight Models
   • Particularly interesting for difficult reasoning and agentic workloads.

3. GLM-5.3-Flash / GLM-5.2 - excellent efficiency

   • Very competitive with the above while generally being more practical to deploy than enormous frontier models.
   • Recent independent comparisons put GLM models near the very top of open-weight performance. Open Source LLM Leaderboard 2026 - Compare Open-Weight Models

4. OpenAI gpt-oss-120b - best if you want a relatively practical Western open-weight model

   • Designed specifically for reasoning and can run on a single high-end GPU according to OpenAI. OpenAI releases open-weight reasoning models optimized for running on laptops
   • However, it's not currently the raw reasoning leader; newer Chinese open-weight models have moved considerably ahead.

5. DeepSeek-R1 / R1-0528 - still an excellent classic

   • MIT-licensed and genuinely open-weight.
   • R1-0528 substantially improved math, coding, and reasoning over the original R1. DeepSeek-R1-0528 Release | DeepSeek API Docs
   • But in 2026, I'd choose the newer DeepSeek V4 family for maximum capability.

Bottom line

If money/hardware aren't constraints: DeepSeek-V4-Pro.

If you want the absolute highest benchmark scores: I'd also test Kimi K3 alongside V4-Pro; current independent rankings actually put K3 slightly ahead on HLE. Open Source LLM Leaderboard 2026 - Compare Open-Weight Models

If you're running locally: the answer changes dramatically based on your GPU/RAM. A smaller model with good quantization can be a much better choice than a 1.6T-parameter model.

If you tell me your GPU(s), VRAM, and whether you're optimizing for coding, math, agents, or general reasoning, I can give you the best model + quantization + inference stack for your hardware.

The "best" open-weight reasoning model depends heavily on your hardware availability, context requirements, and licensing needs. However, the market is anchored by a few standout models that excel in complex logic, mathematics, and coding:

1. The Benchmark Pioneer: DeepSeek-R1

• Best For: Heavy-duty, OpenAI-o1-level reasoning, math, and advanced coding. Ultimate Guide - The Best OpenAI Open Source Models in 2026

• Overview: DeepSeek-R1 revolutionized open-source reasoning by utilizing advanced reinforcement learning (RL) to develop explicit chain-of-thought (CoT) capabilities. The full model uses a massive 671B Mixture-of-Experts (MoE) architecture. Ultimate Guide - The Best OpenAI Open Source Models in 2026Ultimate Guide - The Best OpenAI Open Source Models in 2026

• The Distill Advantage: If you cannot self-host the massive 671B version, DeepSeek-R1's smaller distilled variants (built on top of Qwen and Llama architectures in sizes like 7B, 14B, 32B, and 70B) offer fantastic reasoning performance locally.

2. Best for Permissive Commercial Use: Qwen 3 / Qwen 3.5 Series

• Best For: General reasoning, multi-turn chat, strong tool-use, and flexible deployment.

• Overview: The Qwen family offers a wide size ladder under the business-friendly Apache 2.0 license. Their reasoning checkpoints excel at balancing general assistant behavior with rigorous logical deduction, making them a go-to choice for production software. Best Open-Weight LLMs 2026: Self-Hosting Guide - Context Studios

3. Best for Long-Context & High Efficiency: MiniMax-M1

• Best For: Processing massive documents alongside complex, extended reasoning. Ultimate Guide - The Best Open Source LLMs for Reasoning in 2026

• Overview: MiniMax-M1 is a high-efficiency MoE reasoning model designed to tackle long-input scenarios. It natively supports a 1-million-token context window while achieving major structural computational savings (up to 75% FLOPs reduction compared to standard models at scale), making it ideal for deep document analysis. Ultimate Guide - The Best Open Source LLMs for Reasoning in 2026

4. Best for Agentic & Complex Coding Reasoning: GLM-5.2

• Best For: Long-horizon software engineering, multi-step agent tasks, and repository-scale analysis. 5 Best Open-Weight Coding Models for Developers in 2026

• Overview: If your reasoning tasks involve autonomous coding agents that need to parse massive directories, maintain context over extended sessions, and self-debug, GLM-5.2 stands out with strong permissive terms (MIT) and a 1-million-token context window. Kilo - Best Open-Weight AI Models for Coding (2026)

Summary Recommendation: Which one should you pick?

• Choose DeepSeek-R1 (or its smaller distilled versions) if you want raw, uncompromised reasoning matching proprietary frontier models. Ultimate Guide - The Best Open Source LLMs for Reasoning in 2026

• Choose Qwen 3.5 if you need a flexible, highly capable model with an Apache 2.0 license for commercial deployment. Best Open-Weight LLMs 2026: Self-Hosting Guide - Context Studios

• Choose MiniMax-M1 if your reasoning workload requires massive context windows (1M+ tokens). Ultimate Guide - The Best Open Source LLMs for Reasoning in 2026

Determining the "best" open-weight reasoning model depends heavily on your hardware constraints, context-length needs, and specific tasks (like math, coding, or deep logic). The open-weight reasoning landscape is dominated by frontier systems featuring native reinforcement-learning (RL) chains of thought. deepseek-ai/DeepSeek-R1 - Hugging Face

The top options break down by category:

1. DeepSeek-R1 (and its distilled variants)
   • Strengths: Widely credited with changing the open-weight paradigm by proving that pure large-scale RL can trigger advanced reasoning behaviors competitive with closed-source frontier models.
   • Best For: General math, logic, and coding reasoning. If you have limited hardware, DeepSeek’s distilled variants (like DeepSeek-R1-Distill-Qwen-32B) punch well above their weight class and outperform many larger models. Watch Out OpenAI, Anthropic: DeepSeek, Moonshot Raise Fresh Capital As Chinese Open-Weight AI Models Gain US Users
2. Alibaba Qwen series (e.g., Qwen3 "Thinking" / Max variants)
   • Strengths: Incredible performance on complex benchmarks (like AIME and LiveCodeBench) combined with extensive multilingual support. The Mixture-of-Experts (MoE) reasoning configurations offer some of the highest reasoning accuracy available in an open-weights format.
   • Best For: Multilingual logic, production agentic workflows, and heavy-duty coding. Top 9 Large Language Models as of October 2026
3. Zhipu AI GLM series (e.g., GLM-5.2 / 5.3)
   • Strengths: Exceptional performance on long-context processing and agentic reasoning benchmarks, with massive context windows (up to 1M+ tokens).
   • Best For: Repository-scale analysis, codebase reasoning, and complex multi-step agent actions. Open Source LLM Leaderboard 2026 - Compare Open-Weight Models7 best open weight AI models I've tested in 20265 Best Open
4. Moonshot AI Kimi series (e.g., Kimi K3)
   • Strengths: Dominates several complex reasoning and browsing benchmarks (such as GPQA Diamond and BrowseComp), showing elite competence in synthesized research and tool use.
   • Best For: Advanced information retrieval, web-agent reasoning, and hard science/academic benchmarks. [www.vellum.ai]

If you want to narrow this down, tell me:

• • What kind of hardware or budget you are working with (local GPU vs. API/cloud hosting)

• What your primary use case is (coding, long documents, math/logic, or agent automation)

I can recommend the exact model size and variant that fits your setup.

MiMo-V2.6-Pro is ranked the best open-weight model in the October 2026 reasoning-model ranking cited here. If you mean a model with especially strong math and STEM reasoning, DeepSeek V4 is another leading choice.(benchlm.ai)(telnyx.com)

🏆 Best Open-Weight Reasoning Models (2025–2026)

The open-weight ecosystem has exploded in 2025, with several models reaching or surpassing proprietary systems like GPT-4o in reasoning, math, and coding. Here are the top contenders:

🔹 DeepSeek R1 / R1-Zero

• Architecture: 671B Mixture-of-Experts (MoE), ~37B active per token

• Strengths:

  • State-of-the-art reasoning and math (MATH benchmark ~97%)

  • Matches/exceeds GPT-4o on complex reasoning and coding tasks

  • Trained with Group Relative Policy Optimization (GRPO) for chain-of-thought reasoning

• License: MIT (very permissive)

• Drawback: Requires massive GPU resources for full-scale deployment Open Source LLM Leaderboard 2025–2026: Llama, Mistral, DeepSeek, Qwen | CodeSOTA | CodeSOTATop Open Source AI Models 2025

🔹 Llama 4 (Meta)

• Architecture: 400B parameters, 17B active (MoE with 128 experts)

• Strengths:

  • Extremely long context (up to 10M tokens)

  • Strong multilingual and multimodal support (text + images)

  • Community license allows commercial use (with some redistribution limits)

• Best For: Long-context reasoning, enterprise-scale deployments Where Open-Weight Has Caught Closed: Llama 4, Qwen 3, DeepSeek, and Magistral Compared - AI Pulled News

🔹 Qwen 3 (Alibaba)

• Architecture: Up to 235B parameters, 22B active (MoE)

• Strengths:

  • Excellent multilingual support (119 languages)

  • Hybrid “fast vs. deliberate” reasoning mode toggle

  • Apache 2.0 license (very permissive for commercial use)

• Best For: Multilingual reasoning, developer-friendly open deployments Where Open-Weight Has Caught Closed: Llama 4, Qwen 3, DeepSeek, and Magistral Compared - AI Pulled NewsMistral 3 vs Kimi K2 vs Qwen 3 - Which is the best open-weight model? - Bind AI

🔹 Mistral 3

• Architecture: Family of dense + MoE models, flagship 675B total / 41B active

• Strengths:

  • Apache 2.0 license (open and permissive)

  • Strong reasoning + multilingual capabilities

  • Flexible deployment (small dense models for lighter hardware, large MoE for enterprise)

• Best For: Balanced performance across reasoning, multilingual, and multimodal tasks Mistral 3 vs Kimi K2 vs Qwen 3 - Which is the best open-weight model? - Bind AI

📊 Quick Comparison

✅ Verdict

• Best overall reasoning model: DeepSeek R1-Zero (highest benchmark scores, strongest chain-of-thought reasoning).

• Best for long-context reasoning: Llama 4 Scout (10M tokens).

• Best for multilingual + permissive licensing: Qwen 3.

• Best balance of openness + performance: Mistral 3 Large.

Would you like me to break this down into recommendations by use case (e.g., research, enterprise, coding, multilingual applications)? That way you’ll know which “best” model fits your exact needs.