Best Open Source Reasoning Models in 2026: Top Picks for Complex Logic

In 2026, the spotlight is firmly on reasoning models. Unlike generic AI chatbots, they tackle multi-step logic, complex math, structured analysis, and architectural coding.

Although closed-source models led the way at first, the open source community has quickly caught up, and then some. Today, the best open source reasoning models are production-ready, competitive, and deployable on your own infrastructure.

This guide compares the leading open-source reasoning models of 2026 to help you find the perfect fit for your project.

The Differentiating Element of Reasoning AI Models

To put it simply, a chat-based LLM is like a calculator, which is fine for quick, easy tasks. In contrast, a reasoning model is more like a spreadsheet. It is designed for complex, multi-step problems, and each step depends on the last.

Reasoning models stand out in these areas.

• Stronger Multi-Step Logic: Reasoning AI models work by breaking a complex problem into smaller, manageable sub-problems. Then, they solve them sequentially and synthesize the results.
• Better Math/Code Reliability: They are optimized to reduce the errors or “hallucinations” that general models often make. Sometimes, these models use internal checks to verify results.
• Structured Analysis: They can follow precise analytical methods, making them well-suited for tasks such as data interpretation and research synthesis.

Quick Model Overview

• Deepseek Reasoner (R1) — Best for RL-powered math and logic
• Qwen 3 Next Thinking — Best for efficient hybrid reasoning
• Llama 4 Maverick — Best for large-scale multi-model reasoning
• GLM 4.7 — Best for multi-step coding
• MiniMax M2.5 — Best for coding, agentic tool use, and office work
• Kim K2 Thinking — Best for long-context analysis
• GPT-OSS 120B — Best for efficient single-GPU reasoning
• Intellect 3 — Best for specialized scientific reasoning
• Mistral Small (3.2) — Best lightweight and deployable option

Deepseek Reasoner (R1)

Deepseek R1 is a top performer in the reasoning category. It was among the first open-weight models to use large-scale reinforcement learning to verify and improve its responses. The model is good at solving math, code, and science puzzles. It packs 671B total parameters (37B active per token) in a MoE setup with multi-head latent attention.

Key Strengths

It excels in clear step-by-step reasoning and advanced mathematical problem-solving. Plus, Deepseek Reasoner performs well on coding challenges that require deep reasoning rather than mere suggestions. The recent update added reduced hallucination rates, improved function calling, and support for structured code generation

Best Use Cases

It is best for academic research, solving complex math problems, scientific reasoning tasks, and code generation.

Benchmarks

Deepseek R1 consistently ranks high on Math-500 (97.3%), MMLU (90.8%), and GPQA Diamond (71.5%).

Limitations

The detailed CoT process consumes a considerable number of tokens per query and slows it down.

Try Deepseek R1 Now!

Qwen 3 Next Thinking

Qwen3-Next by Alibaba is a fantastic all-rounder in the Qwen family. The 80B variant uses a hybrid MoE architecture with only 3 billion active parameters per token. It continuously flips between “thinking” mode for reasoning capabilities and fast mode for quick hits.

Key Strengths

Qwen 3 Next Thinking performs well in agentic pipelines. Users can enable the toggleable thinking mode for deep reasoning and turn it off for quicker, everyday tasks. It also supports smooth tool calling and multi-turn interactions. The model performs well on both math and coding tasks and runs on a range of hardware due to its optimized infrastructure.

Best Use Cases

It is perfect for multi-step workflows, agentic pipelines, and multilingual business applications. Plus, Qwen 3 Next Thinking is a good ‘first try' model for teams new to reasoning AI.

Benchmarks

It has top scores on AIME25 (87.8%), SuperGPQA (60.8%), and LiveCodeBench (68.7%).

Limitations

Its “Next Thinking” approach may not be as thorough on multi-disciplinary problems.

Try Qwen 3 Next Thinking Now!

Llama 4 Maverick

Llama 4 Maverick is the workhorse of the open source world. It is a massive, dense model built for complex reasoning. Unlike earlier releases, Maverick is natively multi-model and can process both text and images. This versatile model (400B parameters, 17B active parameters) uses MoE design with 128 expert pathways.

Key Strengths

Llama 4 is widely used for code generation, long context analysis, visual reasoning, and document understanding. It is also faster than many models of its size, therefore, fit for interactive use. More importantly, it costs around $0.19 per million tokens on distributed setups.

Best Use Cases

Llama 4 Maverick is best for enterprise document analysis, AI-assisted coding, and visual inspection processes.

Benchmark

The model has high ChatQA (90%), MathVista (73.7%), and MMMU (73.4%) scores.

Limitations

Since it is a 400B model, Llama 4 Maverick requires an H100 DGX host for self-hosting.

Try Llama 4 Maverick Now!

GLM 4.7

GLM 4.7 comes from the team at Z.ai, a Chinese AI startup. It excels in long-text reasoning and tasks requiring multi-step deduction without losing track. The 355B model has 32 active params and uses the MoE setup.

Key Strengths

This newer model has stronger programming skills and more reliable multi-step reasoning and execution. It is also better at executing complex agent tasks and responds in a natural conversational tone. Plus, GLM 4.7 follows instructions closely and works well in tool-calling scenarios.

Best Use Cases

It is well-suited for agentic coding, multilingual programming, and end-to-end development. Further, the model is good at frontend and UI generation and creating clean web pages.

Benchmarks

It has consistently scored highly on benchmarks such as LiveCodeBench (84.9%) and SWE-bench Verified (75.8%).

Limitations

GLM 4.7 performs slightly lower in pure math reasoning than R1. Additionally, it is a little less “creative” in its thinking compared to other models.

Try GLM 4.7 Now!

MiniMax M2.5

MiniMax M2.5 is the freshest release on the list. The model is a 230B-parameter MoE with just 10B active parameters per token. It comes with an MIT license, allowing unrestricted commercial use. Its architecture is highly optimized for fast inference, delivering the quickest reasoned responses.

Key Strengths

The M2.5 variant is designed around real-world productivity tasks. It excels at coding agents, office automation, web browsing, and structured data tasks. MiniMax M2.5 has high emotional intelligence in logic tasks and is better at understanding what the user asks.

Best Use Cases

M2.5 is ideal for AI coding assistants who have to solve problems on the fly. Other use cases include on-device reasoning applications, financial analysis workflows, and enterprise office tasks.

Benchmarks

The model scores high on tasks requiring code generation and quick logical analysis.

Limitations

Being a very recent release, it has limited community support and documentation.

Try MiniMax M2.5 Now!

Kim K2 Thinking

Kim K2 Thinking from Moonshot AI, is the champion of long text. The model stands out for its ability to process and reason over massive amounts of information. For instance, entire book series, years of financial reports, and comprehensive codebases.

Kim K2 Thinking uses a 1-trillion-parameter MoE model with 32B active per inference pass.

Key Strengths

The model’s main strength lies in long-horizon agentic execution. Its 256K context window in thinking mode helps it keep track of information for extended tasks. Not to mention, K2 Thinking can handle 200-300 sequential tool calls within a single flow. The model uses a Self-Critique Rubric Reward system to evaluate and improve its responses on open-ended tasks.

Best Use Cases

It can be used for legal documents review, scanning entire code repositories for bugs, and historical data analysis.

Benchmarks

Kim K2 Thinking tops HLE (44.9%), BrowseCamp (60.2%), and Seal-0 (56.3%).

Limitations

The K2 Thinking model currently works with text inputs and outputs. Given its 1-trillion-parameter size, it requires an expensive setup for self-hosting at full precision.

Try Kim K2 Thinking Now!

GPT OSS 120B

GPT OSS 120B is a fully open, community-driven project that competes with proprietary models. The 120B flagship model supports the MoE architecture and Chain-of-Thought process. Even with 116.8B total parameters, it activates only 5.1B per token.

Key Strengths

GPT-OSS 120B achieves some of the best results in math and science reasoning among open models. It supports full tool integration through APIs and supports CoT workflows. More importantly, users can adjust the reasoning effort between low, medium, and high. OSS 120B can be fine-tuned to better match your specific use case.

Best Use Cases

The 120B variant excels in enterprise research, generating synthetic data, and studying AI reasoning.

Benchmarks

On AIME 2025, GPT-OSS 120B reached 97.9%, the highest score on this list.

Limitations

GPT OSS 120B is known to be verbose and produces long, necessary responses.

Try GPT OSS 120B Now!

Intellect 3

Intellect 3 is specifically designed for science and medical domains. It is unlike any other open source AI reasoning model on the list. Technically, it is a 106B-params model built on Z.ai’s GLM 4.5 Air and trained with STF and RL.

Key Strengths

Intellect-3 is the best model of its size for math, coding, science, and reasoning tasks. Since it's not a general reasoning engine, the model has a strong grasp of scientific concepts and is highly skilled in formal mathematics.

Best Use Cases

This is best for PhD-level reasoning in technical fields and assists in writing research papers. It also efficiently solves advanced chemistry and physics problems.

Benchmarks

This AI reasoning model surpasses Deepseek’s frontier models on AIME 2024 and AIME 2025.

Limitations

Intellect 3 is not versatile for general-purpose tasks like creative writing.

Try Intellect 3 Now!

Mistral Small (3.2)

Mistral Small (24B params) is the smallest model on the list, and the only one without the formal reasoning (CoT) mode. It is a tiny, deployable package that runs on minimal infrastructure.

Key Strengths

Mistral Small 3.2 has improved function calling, fewer infinite generation loops, and better instruction following. It delivers quick responses and generates 110 tokens per second with a first-token time of just 0.29 seconds. The model’s added vision capabilities support image reasoning.

Best Use Cases

It is perfect for latency-sensitive applications and handles simpler logic tasks in a larger agentic system.

Benchmarks

The 3.2 variant outperforms commercial models, GPT-4o mini, and Gemma on multiple benchmarks.

Limitations

It is less efficient for deep multi-step logic than similar models with the CoT reasoning process on the list.

Try Mistral Small (3.2) Now!

How to Choose the Right Open Source Reasoning Model

Unfortunately, there is no universal answer to this query. You can not rely on the highest benchmark scores when picking a model. Instead, it heavily depends on what you are building or trying to optimize.

• Task Complexity: If you need to solve complex PhD-level math problems, go for Deepseek R1, Intellect-3, or GLM 4.7. Mistral Small M2.5 and MiniMax M2.5 are smart chatbots capable of basic logic.
• Infrastructure Capacity: Intellect-3 (12B active params), quantized Qwen, and Mistral Small (24B dense) is better suited for teams with limited resources. If you can afford a cluster of H100s, go for Llama 4 (400B) or Kim K2 Thinking (1 trillion parameters).
• Cost Constraints: Cost is directly tied to infrastructure. Understandably, you will burn more money on running larger models like Llama 4. Factor in both the initial setup and the ongoing operational costs per token. Mistral Small (3.2) and MiniMax M2.5 are the most affordable options on this list.
• License Compatibility: Although open source, not all models may be used commercially. DeepSeek R1, Intellect 3, GPT-OSS 120B, and MiniMax M2.5 are all MIT- or Apache-2.0-licensed. This means they are safer for commercial use. Kimi K2 Thinking also allows commercial use, but includes additional rules for very large-scale deployments.
• Latency vs. Accuracy Tradeoffs: Reasoning AI models take time to think. For real-time applications, you may have to trade a tiny bit of accuracy for the speed of MiniMax M2.5. Deepseek R1 is slower but fit for tasks where you can not compromise on accuracy.

Common Reasoning Tasks These Models Excel At

• Advanced Math: A general LLM may give a plausible-sounding wrong answer to a challenging calculus problem. In contrast, a reasoning model works through the problem step by step and checks its logic. Models like Deepseek R1 and Intellect 3 can outline the steps and even catch errors.
  • Example: If asked to prove that √2 is irrational, R1 walks through the reasoning clearly instead of repeating the proof like a chat model.
• Multi-Step Logic: Planning a business strategy or complex software migration means breaking a larger problem into clear, logical steps. Models like Llama 4 Maverick and GLM 4.7 are masters of this.
  • Example: The aforementioned models create a detailed step-by-step plan for migrating a monolithic e-commerce application to a microservices architecture.
• Coding/Debugging: Instead of writing a function, these models can debug by reasoning. Qwen 3 Next Thinking is quite strong for complex debugging.
  • Example: “A Python script throws an index error. Check the loop logic step by step to find the problem, don’t just suggest a fix.”
• Data Analysis: Give them a dataset and a question. These models will identify patterns in spreadsheets that most generic LLMs would miss. Kim K2 Thinking can handle large temporal datasets.
  • Example: Logic puzzles are trivial for these models. However, they demonstrate their ability to track multiple entities and relationships over time.

How to Get Better Outputs From Reasoning Models?

Even the best open-source reasoning models need good prompts to produce hallucination-free results.

• Demand the Thinking Process: For accuracy, explicitly ask the model to “reason step-by-step” or show you CoT.” This guides it to use its internal structure and often produces more accurate results. It is a built-in feature in models like Deepseek R1.
• Give Full Context Upfront: Reasoning models are advanced but they can not read your mind. Typically, they reason better when they have complete context. Include all info related to variables, constraints, and background in your prompt. The more context these models have, the less likely they are to hallucinate information.
• Use Structured Output Format: Request the answer in a specific format, such as JSON or a Markdown table. This encourages the model to be precise.
• Give Examples: You can also add one or two examples in your prompt about the kind of reasoning and output you expect. This way, the model mimics its response to your desired structure and logic.
• Review and Adjust: Do not treat the first response as the final one, instead, it is a starting point. Ask for a follow-up, if the response seems unclear or wrong. This back-and-forth helps produce a more accurate result.

Getting the Maximum Out of the Open-Source Reasoning Models at Okara

Experimenting with all these models is an infrastructure nightmare. Setting up and running these models is real engineering work. Thankfully, you do not have to worry about any of this with Okara.

It gives you access to the world’s best AI open-source models with a single click. You can focus on building your application instead of worrying about CUDA drivers and VRAM usage.

• One-click Deployment: As hard as it is to believe, Okara allows you to access models on this with just a few clicks. This goes for the massive Llama 4, GPT-OSS 120B, and the nimble Mistral Small.
• Unified Interface: The platform gives access to all models through a single interface. You can switch between R1 for deep logic tasks and MiniMax M2.5 for high-speed responses.
• Cost Savings: Okara handles all infrastructure challenges. On the other hand, users can benefit from all these reasoning models with a single subscription.

It is the easiest way to test-drive all the models on the list and compare them side-by-side before picking the right one for the task.

FAQs

What is an open source reasoning model?
It is an AI model with publicly available code and specifically trained to use “Chain-of-Thought” processing to solve multi-step problems.

Do reasoning models cost more to run than general LLMs?
Generally, yes. These models perform complex internal calculations that require more compute power and memory during inference. However, efficient and cost-effective models like Mistral Small (3.2) are closing this gap.

What benchmarks matter for reasoning (and which are misleading)?
Trust benchmarks that measure advanced reasoning and skills such as GSM8K, MATH, HumanEval, and GPQA. Avoid relying too much on simpler tests like MMLU that don't clearly separate performance.

Which open source reasoning model is best for math?
Intellect-3 and Deepseek Reasoner (R1) excel at solving complex mathematical problems. Qwen 3 Next Thinking is an all-rounder and can handle math well.

Which open source reasoning model is best for coding and debugging
Deepseek R1 is great for debugging because you can see its logic. In addition, Qwen 3 Next offers both coding accuracy and speed. Kim K2 Thinking is fit for understanding entire codebases and long-horizon agentic coding.