Tools: Which Local LLM is Better? A Deep Dive into Open-Source AI Models in 2026 (Benchmarked)

Why "Best LLM" Is the Wrong Question ## Best Open-Source LLM for Coding: The Competition ## The Benchmark: SWE-bench Verified (Real Software Engineering) ## Kimi K2.5 (Open-Weights) ## DeepSeek V3.2 (Open-Source) ## GLM-4.7 - Best for AI Coding Agents ## Reasoning: Mathematical and Scientific Intelligence ## Subcategory: Mathematical Reasoning (AIME 2025 Benchmark) ## GLM-4.7 (Open-Source) - Mathematical Reasoning Leader ## DeepSeek V3.2 (Open-Source) - Strong Math Performance ## Qwen2.5-Max (Open-Source) - Consumer-Friendly Math Option ## Subcategory: Scientific Reasoning (GPQA Diamond) ## GLM-4.7 (Open-Source) - Best Available for Scientific Reasoning ## Subcategory: General Reasoning (MMLU, HLE) ## DeepSeek V3.2 (Open-Source) - Most Well-Rounded Reasoner ## Summary: Reasoning Category Winners ## Agentic Workflows & Tool Use ## The Benchmark: τ²-Bench (Agent Coordination) ## GLM-4.7 (Open-Source) - Agentic Workflows Leader ## Category Winners: Quick Reference Table ## How to Choose the Right Open-Source LLM: Decision Tree ## START: What's Your Primary Use Case? ## If CODING: ## If MATH/REASONING: ## If AGENTIC/TOOLS: ## License Verification: Are These Really Open-Source? ## Final Recommendations: Best Open-Source LLM for You ## For Most Developers (February 2026): ## The Strategic Approach: Here's the problem: Everyone claims their model is "The Best." No one tells you which specific model to use for which task. I've analyzed every major open-source LLM benchmark from February 2026 to answer one question: Which free AI model actually wins for your specific use case? This isn't about vague claims. This is about hard data from SWE-bench (real GitHub issues), AIME 2025 (olympiad math), and agent benchmarks. Let me show you which open-source alternatives to ChatGPT and Claude actually work. Here's what no one tells you: there is no single "best" AI model. A model that dominates coding benchmarks often fails at math. One that excels at tool use might struggle with pure reasoning. This is why you need to match the local LLM to your specific task. I've broken down the top open-source language models into three categories based on February 2026 benchmarks: Let's see which free AI models win with proof. Forget "write a hello world function." SWE-bench Verified tests 500 real GitHub issues from production Python repositories. The AI model must: Score: 76.8% on SWE-bench Verified - Highest Open-Source Score Why Kimi K2.5 Leads on Coding Benchmarks: Kimi K2.5, released January 27, 2026, achieves the highest open-source score on SWE-bench Verified at 76.8%. It's particularly strong at: Additional Coding Benchmarks: Hardware Requirements: Important Note: Kimi K2.5 uses MIT license with commercial restrictions. Companies with over 100 million monthly active users require special licensing. For most users and businesses, this is fully open-source. When to Use Kimi K2.5: Score: 73.0% on SWE-bench Verified Why DeepSeek V3.2 Is Strong for Coding: DeepSeek V3.2 (the current version as of February 2026) achieves one of the highest scores among open-source AI models on the industry-standard SWE-bench. Only 7–8% behind proprietary models like Claude Opus 4.5 (80.9%). Technical Specs (DeepSeek V3.2): Hardware Requirements for Self-Hosting: Real-World Performance: Score: 73.8% on SWE-bench Verified GLM-4.7 technically scores 0.8% higher than DeepSeek V3.2, but this comes with a caveat: the score may include enhanced scaffolding or agentic frameworks. For direct model comparisons, DeepSeek V3.2 is more consistent. However, GLM-4.7 has a killer feature: it runs on consumer hardware. Technical Specs (GLM-4.7-Flash): Additional Coding Benchmarks: When to Choose GLM-4.7 Over DeepSeek V3.2: Reasoning isn't a single capability. It breaks down into distinct subcategories that test different cognitive abilities. Let's examine how open-source LLMs perform across mathematical and scientific domains. The Benchmark: AIME 2025–30 problems from the American Invitational Mathematics Examination. These are competition-level math problems requiring multiple reasoning steps. The Data (from Artificial Analysis Intelligence Index): Score: 95.7% on AIME 2025 Score: 93.1% on AIME 2025 DeepSeek V3.2 achieves 93.1% on AIME 2025, placing it just behind GLM-4.7's 95.7% but still in frontier territory for open-source models. This is significant: Near-frontier math performance with full MIT licensing and strong versatility across all benchmark categories. Score: 92.3% on AIME 2025 Strong math performance with more accessible hardware requirements than DeepSeek. The Benchmark: GPQA Diamond - 198 PhD-level questions in physics, biology, chemistry. Designed to be "Google-proof" (even experts with web access only score 65–70%). Honest Assessment: Open-source models lag behind proprietary models by 2–4% in this category. Best Open-Source Performance: Score: 85.7% on GPQA Diamond GLM-4.7 leads open-source models on PhD-level scientific reasoning, though proprietary models maintain a 4–5% advantage. The Reality: For PhD-level scientific research requiring the absolute highest accuracy, proprietary models (Gemini 3 Pro, GPT-5) currently have an edge. However, for most scientific applications, the 4–5% gap isn't critical. When Open-Source Works Well: When to Consider Proprietary: Benchmarks: MMLU (general knowledge across 57 subjects), HLE (Humanity's Last Exam - multi-domain expert knowledge) Top Open-Source Models: MMLU and Other General Benchmarks: Competitive with Claude 3.5 Sonnet DeepSeek V3.2 maintains strong general reasoning across diverse benchmarks, making it the most well-rounded open-source AI model for reasoning tasks. Mathematical Reasoning: Scientific Reasoning: This benchmark tests how well AI models guide users through complex troubleshooting while coordinating tool usage in dual-control environments (both agent and user have tools). Most AI models that dominate coding collapse here. This tests real-world agentic capability. Score: 87.4% on τ²-Bench Verified Agent Benchmarks: Why This Matters for AI Agents: Agentic workflows are where AI coding assistants (Claude Code, Cursor, Cline, Continue) operate. Strong tool use means the model can: Technical Specs (GLM-4.7-Flash): Have multiple H100 GPUs or API budget? → DeepSeek V3.2 (73.1% SWE-bench, MIT license, $0.27/M tokens API) Want highest open-source performance? → Kimi K2.5 (76.8% SWE-bench, visual coding capabilities) Have single RTX 4090 (24GB)? → Qwen3-Coder-Next (70.6%, runs locally, Apache 2.0) Building AI coding agents (Cursor, Cline)? → GLM-4.7 (87.4% agent benchmark, 16GB VRAM, MIT) Need highest accuracy? → GLM-4.7 (95.7% AIME, MIT license) Want versatility + math? → DeepSeek V3.2 (93.1% AIME, strong general reasoning, MIT) Need multilingual support? → Qwen2.5-Max (92.3% AIME, 119 languages, Apache 2.0) For AI agents and automation: → GLM-4.7 (87.4% τ²-Bench, 16GB VRAM, MIT) Fully Open-Source (Commercial Use Allowed): Open-Source with Commercial Restrictions: Option 1: Kimi K2.5 (Highest Coding Performance) Option 2: GLM-4.7 (Best All-Rounder for Consumer Hardware) Option 3: DeepSeek V3.2 (Most Well-Rounded) Option 4: Qwen3-Coder-Next (Efficiency Champion) Many professional developers use a hybrid strategy: This gives you the best of both worlds: freedom and control with open-source, reliability where it matters most. About This Analysis: All benchmark data from Artificial Analysis Intelligence Index (AIME 2025), SWE-bench.com official leaderboards, τ²-Bench documentation, and verified model release announcements from DeepSeek, Zhipu AI, and Alibaba Cloud. Hardware requirements from official specifications and community testing. All licenses verified from GitHub/Hugging Face. Information current as of February 14, 2026. - - Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: SWE-bench Verified Leaderboard (February 2026): ✓ Proprietary Models: 1. Claude Opus 4.5: 80.9% 2. Claude Opus 4.6: 80.8% 3. GPT-5.2: 80.0% ⭐ Open-Source Models: 4. Kimi K2.5: 76.8% ← HIGHEST OPEN-SOURCE 5. GLM-4.7: 73.8% 6. DeepSeek V3.2: 73.1% 7. Qwen3-Coder-Next: 70.6% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: SWE-bench Verified Leaderboard (February 2026): ✓ Proprietary Models: 1. Claude Opus 4.5: 80.9% 2. Claude Opus 4.6: 80.8% 3. GPT-5.2: 80.0% ⭐ Open-Source Models: 4. Kimi K2.5: 76.8% ← HIGHEST OPEN-SOURCE 5. GLM-4.7: 73.8% 6. DeepSeek V3.2: 73.1% 7. Qwen3-Coder-Next: 70.6% CODE_BLOCK: SWE-bench Verified Leaderboard (February 2026): ✓ Proprietary Models: 1. Claude Opus 4.5: 80.9% 2. Claude Opus 4.6: 80.8% 3. GPT-5.2: 80.0% ⭐ Open-Source Models: 4. Kimi K2.5: 76.8% ← HIGHEST OPEN-SOURCE 5. GLM-4.7: 73.8% 6. DeepSeek V3.2: 73.1% 7. Qwen3-Coder-Next: 70.6% CODE_BLOCK: Kimi K2.5 Performance: - SWE-bench Verified: 76.8% ← HIGHEST - SWE-bench Multilingual: 73.0% - LiveCodeBench v6: 85.0% - Terminal-Bench 2.0: 40.45% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Kimi K2.5 Performance: - SWE-bench Verified: 76.8% ← HIGHEST - SWE-bench Multilingual: 73.0% - LiveCodeBench v6: 85.0% - Terminal-Bench 2.0: 40.45% CODE_BLOCK: Kimi K2.5 Performance: - SWE-bench Verified: 76.8% ← HIGHEST - SWE-bench Multilingual: 73.0% - LiveCodeBench v6: 85.0% - Terminal-Bench 2.0: 40.45% CODE_BLOCK: SWE-bench Verified Leaderboard (February 2026): ✓ Proprietary Models: 1. Claude Opus 4.5: 80.9% 2. Claude Opus 4.6: 80.8% 3. GPT-5.2: 80.0% ⭐ Open-Source Models: 4. Kimi K2.5: 76.8% ← HIGHEST OPEN-SOURCE 5. GLM-4.7: 73.8% 6. DeepSeek V3.2: 73.1% 7. Qwen3-Coder-Next: 70.6% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: SWE-bench Verified Leaderboard (February 2026): ✓ Proprietary Models: 1. Claude Opus 4.5: 80.9% 2. Claude Opus 4.6: 80.8% 3. GPT-5.2: 80.0% ⭐ Open-Source Models: 4. Kimi K2.5: 76.8% ← HIGHEST OPEN-SOURCE 5. GLM-4.7: 73.8% 6. DeepSeek V3.2: 73.1% 7. Qwen3-Coder-Next: 70.6% CODE_BLOCK: SWE-bench Verified Leaderboard (February 2026): ✓ Proprietary Models: 1. Claude Opus 4.5: 80.9% 2. Claude Opus 4.6: 80.8% 3. GPT-5.2: 80.0% ⭐ Open-Source Models: 4. Kimi K2.5: 76.8% ← HIGHEST OPEN-SOURCE 5. GLM-4.7: 73.8% 6. DeepSeek V3.2: 73.1% 7. Qwen3-Coder-Next: 70.6% CODE_BLOCK: GLM-4.7 Performance: - SWE-bench Multilingual: 66.7% - Terminal-Bench 2.0: 41.0% - LiveCodeBench: 84.9% - Agent tool use (τ²-Bench): 87.4% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: GLM-4.7 Performance: - SWE-bench Multilingual: 66.7% - Terminal-Bench 2.0: 41.0% - LiveCodeBench: 84.9% - Agent tool use (τ²-Bench): 87.4% CODE_BLOCK: GLM-4.7 Performance: - SWE-bench Multilingual: 66.7% - Terminal-Bench 2.0: 41.0% - LiveCodeBench: 84.9% - Agent tool use (τ²-Bench): 87.4% CODE_BLOCK: AIME 2025 Leaderboard (February 2026): ✓ Proprietary Models: 1. GPT-5.2: 99.0% 2. Gemini 2.0 Flash Thinking: 97.0% 3. Gemini 2.0 Pro Thinking: 95.7% ⭐ Open-Source Models: 7. GLM-4.7: 95.7% ← TOP OPEN-SOURCE 8. DeepSeek V3.2: 93.1% 9. Qwen2.5-Max: 92.3% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: AIME 2025 Leaderboard (February 2026): ✓ Proprietary Models: 1. GPT-5.2: 99.0% 2. Gemini 2.0 Flash Thinking: 97.0% 3. Gemini 2.0 Pro Thinking: 95.7% ⭐ Open-Source Models: 7. GLM-4.7: 95.7% ← TOP OPEN-SOURCE 8. DeepSeek V3.2: 93.1% 9. Qwen2.5-Max: 92.3% CODE_BLOCK: AIME 2025 Leaderboard (February 2026): ✓ Proprietary Models: 1. GPT-5.2: 99.0% 2. Gemini 2.0 Flash Thinking: 97.0% 3. Gemini 2.0 Pro Thinking: 95.7% ⭐ Open-Source Models: 7. GLM-4.7: 95.7% ← TOP OPEN-SOURCE 8. DeepSeek V3.2: 93.1% 9. Qwen2.5-Max: 92.3% CODE_BLOCK: GPQA Diamond Scores (February 2026): ✓ Proprietary Models: 1. Gemini 3 Pro: 90.8% 2. GPT-5.2: 90.3% ⭐ Open-Source Models: 1. GLM-4.7: 85.7% 2. DeepSeek V3.2: ~85–88% (estimated) 3. Qwen3 variants: ~84–87% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: GPQA Diamond Scores (February 2026): ✓ Proprietary Models: 1. Gemini 3 Pro: 90.8% 2. GPT-5.2: 90.3% ⭐ Open-Source Models: 1. GLM-4.7: 85.7% 2. DeepSeek V3.2: ~85–88% (estimated) 3. Qwen3 variants: ~84–87% CODE_BLOCK: GPQA Diamond Scores (February 2026): ✓ Proprietary Models: 1. Gemini 3 Pro: 90.8% 2. GPT-5.2: 90.3% ⭐ Open-Source Models: 1. GLM-4.7: 85.7% 2. DeepSeek V3.2: ~85–88% (estimated) 3. Qwen3 variants: ~84–87% CODE_BLOCK: General Reasoning Performance (February 2026): 1. DeepSeek V3.2: Strong across MMLU and expert domains 2. Qwen2.5-Max: MMLU: 84–86% 3. Kimi K2.5: HLE: 50.2% with tools (highest reported) 4. GLM-4.7: HLE: 42.8% with tools Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: General Reasoning Performance (February 2026): 1. DeepSeek V3.2: Strong across MMLU and expert domains 2. Qwen2.5-Max: MMLU: 84–86% 3. Kimi K2.5: HLE: 50.2% with tools (highest reported) 4. GLM-4.7: HLE: 42.8% with tools CODE_BLOCK: General Reasoning Performance (February 2026): 1. DeepSeek V3.2: Strong across MMLU and expert domains 2. Qwen2.5-Max: MMLU: 84–86% 3. Kimi K2.5: HLE: 50.2% with tools (highest reported) 4. GLM-4.7: HLE: 42.8% with tools CODE_BLOCK: GLM-4.7 Agent Performance: - τ²-Bench: 87.4% ← OPEN-SOURCE LEADER - BrowseComp: 67.0 (web task evaluation) - Terminal-Bench 2.0: 41.0% - LiveCodeBench: 84.9% Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: GLM-4.7 Agent Performance: - τ²-Bench: 87.4% ← OPEN-SOURCE LEADER - BrowseComp: 67.0 (web task evaluation) - Terminal-Bench 2.0: 41.0% - LiveCodeBench: 84.9% CODE_BLOCK: GLM-4.7 Agent Performance: - τ²-Bench: 87.4% ← OPEN-SOURCE LEADER - BrowseComp: 67.0 (web task evaluation) - Terminal-Bench 2.0: 41.0% - LiveCodeBench: 84.9% - Coding & Software Engineering - Agentic Workflows & Tool Use - Read the bug report - Navigate the codebase - Generate a working patch - Pass all existing tests This measures actual software engineering capability, not toy problems. - Visual-to-code generation (convert designs/screenshots to functional code) - Front-end development with animations and interactivity - Multi-step debugging workflows - Terminal-based development tasks - 1 trillion parameters (32B active per token) - Native multimodal (text, images, video) - 256K context window - Uses INT4 quantization natively - License: MIT with commercial restrictions (free for companies with under 100M monthly active users) - Agent Swarm: Coordinates up to 100 specialized sub-agents for parallel task execution - Visual Coding: Converts images/videos into functional code - Kimi Code: Open-source terminal tool (rival to Claude Code) - Four modes: Instant, Thinking, Agent, Agent Swarm (beta) - With native INT4: ~240GB VRAM minimum - Practical: Cloud GPU rental or API access - Speed: 44 tokens/second via API - Cost: Competitive pricing with free tier available - Converting UI designs to code - Front-end development with complex animations - Multi-modal coding (working with images/videos) - Agentic coding workflows requiring tool coordination - Projects where visual understanding matters - 671 billion parameters (37B active per token) - Mixture-of-Experts (MoE) architecture - 128K context window - Trained on 14.8 trillion tokens - License: MIT (fully free, commercial use allowed) - Cost: ~$0.27–0.55 per million tokens (API) - 336GB VRAM with 4-bit quantization - Requires 4–5x NVIDIA H100 or H200 GPUs - Practical reality: Most users access via API - Automated bug fixing: Excellent - Code review and refactoring: Strong - Multi-file modifications: Best-in-class for open source - API latency: 20–40 tokens/second - - - MIT License (fully open-source) - Runs on single RTX 4090 (24GB VRAM) using GLM-4.7-Flash variant - Designed specifically for agentic coding (Claude Code, Cursor, Cline) - "Preserved Thinking" architecture maintains reasoning across turns - 30B total parameters, 3B active (efficient!) - 128K context window - Native tool calling - Speed: 25–35 tokens/second on consumer GPU - You have consumer hardware (24GB GPU) - You're building AI coding agents - You need local inference without cloud dependency - You want multi-turn coding sessions with context retention - Highest verified open-source score on AIME 2025 - Matches proprietary Gemini 2.0 Pro Thinking at 95.7% - Strong mathematical reasoning architecture - Mathematical proof generation - Physics problem solving - Quantitative finance modeling - STEM education applications - 671B parameters (37B active via MoE) - Thinking mode available - MIT License - Hardware: Requires cloud GPUs or API access - Trillion-scale MoE architecture - Apache 2.0 License - Supports 119 languages - General scientific questions (undergraduate/Master's level) - Scientific coding and data analysis - Literature review and synthesis - Research assistance (non-critical calculations) - High-stakes research decisions - PhD dissertation-level work - Peer-reviewed publication support - Breakthrough discovery verification - Consistent performance across 57 MMLU subjects - Strong on both academic and practical knowledge - Reliable for general-purpose reasoning applications - - - Champion: GLM-4.7 (95.7% AIME) - MIT License - Strong Alternative: DeepSeek V3.2 (93.1% AIME) - MIT License - Multilingual Option: Qwen2.5-Max (92.3% AIME) - Apache 2.0 - Best Open-Source: GLM-4.7 (85.7% GPQA Diamond) - Reality Check: Proprietary models lead by 4–5% - Most Versatile: DeepSeek V3.2 (strong across all domains) - Tool-Augmented: Kimi K2.5 (50.2% HLE with tools) - Highest verified open-source score on τ²-Bench - Beats many proprietary models on agent coordination - Designed specifically for agentic, tool-heavy workflows - Runs on consumer hardware (16–18GB VRAM) - Call APIs correctly - Use search when needed - Navigate file systems - Execute terminal commands - Coordinate multi-step tasks - 30B total, 3B active parameters - 128K context window - Native tool calling support - MIT License - Hardware: 16–18GB VRAM (RTX 4090) - Speed: 25–35 tokens/second - Building AI coding assistants - Customer service automation - DevOps automation - Multi-tool workflows - Any task requiring extended agent coordination - DeepSeek V3.2: MIT License - No restrictions - GLM-4.7: MIT License - No restrictions - Qwen3-Coder-Next: Apache 2.0 - Attribution required - Kimi K2.5: MIT License - Companies with 100M+ monthly active users require special licensing - All licenses verified from official GitHub/Hugging Face repositories - MIT is most permissive (no attribution needed) - Apache 2.0 requires attribution but allows modification - Kimi K2.5 is effectively fully open-source for the vast majority of users and companies - Highest open-source coding score (76.8% SWE-bench) - Exceptional visual-to-code capabilities - Agent Swarm for complex workflows - MIT license (with 100M MAU restriction) - Best choice for cutting-edge coding performance - Strong coding (73.8% SWE-bench) - Best math reasoning (95.7% AIME) - Best agentic workflows (87.4% τ²-Bench) - Runs on single RTX 4090 (24GB VRAM) - MIT license - Best choice if you have consumer GPU - Excellent coding (73.1%) - Strong math (93.1%) - Best general reasoning - MIT license, API available - Best choice for versatility across tasks - Great efficiency (70.6% with only 3B active) - Runs on single RTX 4090 - Apache 2.0 license - Best choice if hardware-limited - Open-source models for development, testing, and most tasks - Proprietary models (Claude/GPT) for critical production features