When most developers use AI coding assistants, they rely on cloud tools like GitHub Copilot, Claude Code or Cursor. These platforms are powerful, but they all share one major problem. Your code has to be uploaded to someone else’s servers before the model can respond. That means every API key, internal file and sensitive function is being processed outside your own machine. Even with privacy promises, many teams cannot risk exposing important code.
This is why open source, locally run coding models are becoming so popular. They keep your work fully private because nothing leaves your device. They remove the need to trust third-party servers. And if you already have strong hardware, you can build AI coding tools without paying high subscription or API fees.
Below are some of the best open source AI coding models today. These models perform extremely well on coding benchmarks and are quickly becoming real competitors to proprietary systems.
*1. Kimi-K2-Thinking by Moonshot AI
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Kimi-K2-Thinking is built for long and stable reasoning. It works like a tool-using agent that can chain together 200 to 300 steps without drifting off-task. This makes it great for complex research, deep coding sessions and multi-step problem solving.
The model uses a huge mixture-of-experts system with 1 trillion parameters, but only 32 billion are active at a time. It supports a massive 256K context window, which helps when working with large codebases.
Performance highlights:
• SWE-bench Verified: 71.3
• LiveCodeBench V6: 83.1
• Strong multilingual and long-form coding results
Developers choose K2 when they need long, stable reasoning and tool-based workflows.
*2. MiniMax-M2 by MiniMaxAI
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MiniMax-M2 focuses on speed and efficiency. It uses a 230B parameter MoE design but activates only 10B parameters per token. This keeps latency low while still delivering strong coding performance.
It is especially good for agent tasks that follow plan → act → verify loops. Because of its small active footprint, it runs quickly even during heavy tool use.
Key benchmark results:
• SWE-bench: 69.4
• SWE-bench Multilingual: 56.5
• Terminal-Bench: 46.3
• Strong scores in agent benchmarks like GAIA and xbench-DeepSearch
If you need a fast AI model for interactive coding agents, this is one of the top choices.
*3. GPT-OSS-120B by OpenAI
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GPT-OSS-120B is OpenAI’s open-weight model designed for general-purpose reasoning and coding. Despite having 117B parameters in total, only 5.1B are active per token, which lets it run on a single 80GB GPU.
It supports function calling, browsing, Python tools and structured outputs. Developers can fine-tune it, making it suitable for production environments.
Standout strengths:
• One of the highest-ranking models on the Artificial Analysis Intelligence Index
• Matches or beats o4-mini and o3-mini on many coding tasks
• Very strong in math, reasoning and tool-based coding
It is a solid option for teams who want a balanced, high-reasoning local model.
*4. DeepSeek-V3.2-Exp by DeepSeek AI
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DeepSeek-V3.2-Exp is an experimental upgrade built to test DeepSeek’s new sparse-attention system. It improves efficiency for long context tasks without changing the overall behavior of the model.
It performs similarly to V3.1 but with better long-range memory. This helps when reading long files, multi-module projects or extended logs.
Benchmark notes:
• MMLU-Pro: 85.0
• LiveCodeBench: ~74
• AIME 2025: 89.3
• Better Codeforces score than V3.1
If you want strong performance with more efficient long-context handling, this version is worth trying.
*5. GLM-4.6 by Z.ai
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GLM-4.6 expands its context window to 200K tokens, making it one of the best models for large projects that need long memory. It scores higher than the previous GLM-4.5 in coding and overall reasoning.
It also integrates better tool-use abilities, which improves the way it works in coding environments like Claude Code, Roo Code and Kilo Code.
Why developers like it:
• Better front-end code generation
• Stronger reasoning during inference
• Competitive with many leading models in its range
This model is perfect for big coding tasks, long prompts and structured agent workflows.
*6. Qwen3-235B-A22B-Instruct-2507 by Alibaba Cloud
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This version of Qwen3 focuses on delivering direct answers instead of revealing chain-of-thought steps. It provides strong improvements in logic, mathematics, coding and general problem solving.
It also performs well in multilingual tasks, making it useful for global teams or international projects.
Benchmark insights:
• Stronger than earlier Qwen versions
• Competitive with major models like Kimi-K2 and DeepSeek versions
• Great for instruction-following and tool-assisted coding
It is a dependable choice for developers who want high-quality output without reasoning traces.
*7. Apriel-1.5-15B-Thinker by ServiceNow AI
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Apriel-1.5-Thinker is a compact but powerful model with multimodal abilities. It can reason over images and text despite being only 15B parameters, making it lightweight enough to run on a single GPU.
It has a large 131K context window and aims to deliver performance close to much larger models.
Scores to note:
• Artificial Analysis Index: 52
• Tau2 Bench Telecom: 68
• IFBench: 62
This model is ideal for enterprise workflows where efficiency and multimodal reasoning matter.
*Final Thoughts
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The rise of open source AI coding models is giving developers more control than ever. There is no need to send private code to the cloud. You can run powerful models locally, save money and keep everything fully secure.
From long-range reasoning models like Kimi-K2 to efficient MoE systems like MiniMax-M2 and balanced all-rounders like GPT-OSS-120B, the options today are stronger than ever.
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