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

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.

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Why "Best LLM" Is the Wrong Question

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:

1. Coding & Software Engineering
2. Reasoning
3. Agentic Workflows & Tool Use

Let's see which free AI models win with proof.

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Best Open-Source LLM for Coding: The Competition

The Benchmark: SWE-bench Verified (Real Software Engineering)

Forget "write a hello world function." SWE-bench Verified tests 500 real GitHub issues from production Python repositories. The AI model must:

• Read the bug report
• Navigate the codebase
• Generate a working patch
• Pass all existing tests This measures actual software engineering capability, not toy problems.

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%

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Kimi K2.5 (Open-Weights)

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:

• 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

Technical Specs:

• 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)

Additional Coding Benchmarks:

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%

Special Features:

• 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)

Hardware Requirements:

• 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

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:

• 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

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DeepSeek V3.2 (Open-Source)

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%).

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%

Technical Specs (DeepSeek V3.2):

• 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)

Hardware Requirements for Self-Hosting:

• 336GB VRAM with 4-bit quantization
• Requires 4–5x NVIDIA H100 or H200 GPUs
• Practical reality: Most users access via API

Real-World Performance:

• Automated bug fixing: Excellent
• Code review and refactoring: Strong
• Multi-file modifications: Best-in-class for open source
• API latency: 20–40 tokens/second  - -

GLM-4.7 - Best for AI Coding Agents

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.

Why Choose GLM-4.7:

• 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

Technical Specs (GLM-4.7-Flash):

• 30B total parameters, 3B active (efficient!)
• 128K context window
• Native tool calling
• Speed: 25–35 tokens/second on consumer GPU

Additional Coding Benchmarks:

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%

When to Choose GLM-4.7 Over DeepSeek V3.2:

• 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

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Reasoning: Mathematical and Scientific Intelligence

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.

Subcategory: Mathematical Reasoning (AIME 2025 Benchmark)

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):

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%

GLM-4.7 (Open-Source) - Mathematical Reasoning Leader

Score: 95.7% on AIME 2025

Why It Leads:

• Highest verified open-source score on AIME 2025
• Matches proprietary Gemini 2.0 Pro Thinking at 95.7%
• Strong mathematical reasoning architecture

Use Cases:

• Mathematical proof generation
• Physics problem solving
• Quantitative finance modeling
• STEM education applications

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DeepSeek V3.2 (Open-Source) - Strong Math Performance

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.

Technical Specs:

• 671B parameters (37B active via MoE)
• Thinking mode available
• MIT License
• Hardware: Requires cloud GPUs or API access

This is significant: Near-frontier math performance with full MIT licensing and strong versatility across all benchmark categories.

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Qwen2.5-Max (Open-Source) - Consumer-Friendly Math Option

Score: 92.3% on AIME 2025

Strong math performance with more accessible hardware requirements than DeepSeek.

Technical Specs:

• Trillion-scale MoE architecture
• Apache 2.0 License
• Supports 119 languages

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Subcategory: Scientific Reasoning (GPQA Diamond)

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:

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%

GLM-4.7 (Open-Source) - Best Available for Scientific Reasoning

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:

• General scientific questions (undergraduate/Master's level)
• Scientific coding and data analysis
• Literature review and synthesis
• Research assistance (non-critical calculations)

When to Consider Proprietary:

• High-stakes research decisions
• PhD dissertation-level work
• Peer-reviewed publication support
• Breakthrough discovery verification

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Subcategory: General Reasoning (MMLU, HLE)

Benchmarks: MMLU (general knowledge across 57 subjects), HLE (Humanity's Last Exam - multi-domain expert knowledge)

Top Open-Source Models:

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

DeepSeek V3.2 (Open-Source) - Most Well-Rounded Reasoner

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.

Why It's Versatile:

• Consistent performance across 57 MMLU subjects
• Strong on both academic and practical knowledge
• Reliable for general-purpose reasoning applications  - -

Summary: Reasoning Category Winners

Mathematical Reasoning:

• 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

Scientific Reasoning:

• Best Open-Source: GLM-4.7 (85.7% GPQA Diamond)
• Reality Check: Proprietary models lead by 4–5%

General Reasoning:

• Most Versatile: DeepSeek V3.2 (strong across all domains)
• Tool-Augmented: Kimi K2.5 (50.2% HLE with tools)

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Agentic Workflows & Tool Use

The Benchmark: τ²-Bench (Agent Coordination)

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.

GLM-4.7 (Open-Source) - Agentic Workflows Leader

Score: 87.4% on τ²-Bench

Why It Wins:

• 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)

Verified Agent Benchmarks:

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%

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:

• Call APIs correctly
• Use search when needed
• Navigate file systems
• Execute terminal commands
• Coordinate multi-step tasks

Technical Specs (GLM-4.7-Flash):

• 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

When to Use:

• Building AI coding assistants
• Customer service automation
• DevOps automation
• Multi-tool workflows
• Any task requiring extended agent coordination

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Category Winners: Quick Reference Table

How to Choose the Right Open-Source LLM: Decision Tree

START: What's Your Primary Use Case?

If CODING:

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)

If MATH/REASONING:

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)

If AGENTIC/TOOLS:

For AI agents and automation: → GLM-4.7 (87.4% τ²-Bench, 16GB VRAM, MIT)

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License Verification: Are These Really Open-Source?

Fully Open-Source (Commercial Use Allowed):

• DeepSeek V3.2: MIT License - No restrictions
• GLM-4.7: MIT License - No restrictions
• Qwen3-Coder-Next: Apache 2.0 - Attribution required

Open-Source with Commercial Restrictions:

• Kimi K2.5: MIT License - Companies with 100M+ monthly active users require special licensing

Important Notes:

• 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

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Final Recommendations: Best Open-Source LLM for You

For Most Developers (February 2026):

Option 1: Kimi K2.5 (Highest Coding Performance)

• 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

Option 2: GLM-4.7 (Best All-Rounder for Consumer Hardware)

• 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

Option 3: DeepSeek V3.2 (Most Well-Rounded)

• Excellent coding (73.1%)
• Strong math (93.1%)
• Best general reasoning
• MIT license, API available
• Best choice for versatility across tasks

Option 4: Qwen3-Coder-Next (Efficiency Champion)

• Great efficiency (70.6% with only 3B active)
• Runs on single RTX 4090
• Apache 2.0 license
• Best choice if hardware-limited

The Strategic Approach:

Many professional developers use a hybrid strategy:

• Open-source models for development, testing, and most tasks
• Proprietary models (Claude/GPT) for critical production features

This gives you the best of both worlds: freedom and control with open-source, reliability where it matters most.

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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.
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