🆕

February 2026 Rankings Available

See the latest open source LLM rankings featuring Kimi K2.5 and updated scores.

🔓Updated January 2026

Best Open Source LLMs
January 2026 Rankings

Self-host, fine-tune, and deploy without restrictions. Open source models now match proprietary alternatives—hitting 90% on LiveCodeBench and 97% on AIME 2025.

12Models Ranked
$0License Cost
Fine-tuning Freedom

🏆 Top 3 Open Source LLMs

Why Open Source LLMs?

🔐

Data Privacy

Keep all data on your infrastructure. No API calls to third parties. Critical for healthcare, legal, and enterprise applications.

⚙️

Full Control

Fine-tune for your specific use case. Modify behavior, remove guardrails, or train on proprietary data. No terms of service limitations.

💰

Cost at Scale

At high volumes, self-hosting becomes dramatically cheaper. No per-token fees—just infrastructure costs.

Complete Open Source Rankings

RankModelQualityLiveCodeBenchAIME 2025MMLU-Pro
1

GLM-5 (Reasoning)

Z AI

49.64---
2

Kimi K2.5 (Reasoning)

Kimi

46.7385%96%-
3

MiniMax-M2.5

MiniMax

41.97---
4

GLM-4.7 (Thinking)

Z AI

41.789%95%86%
5

DeepSeek V3.2

DeepSeek

41.286%92%86%
6

Kimi K2 Thinking

Kimi

40.385%95%85%
7

MiniMax-M2.1

MiniMax

39.381%83%88%
8

MiMo-V2-Flash

Xiaomi

3987%96%84%
9

Llama Nemotron Ultra

NVIDIA

3864%64%83%
10

MiniMax-M2

MiniMax

35.783%78%82%
11

DeepSeek V3.2 Speciale

DeepSeek

34.190%97%86%
12

DeepSeek V3.1 Terminus

DeepSeek

33.480%90%85%

Open Source vs Proprietary: The Gap Has Closed

Where Open Source Wins

  • Privacy-first deployment—no data leaves your servers
  • Cost at scale—dramatically cheaper for high-volume use
  • Fine-tuning freedom—train on your proprietary data
  • No vendor lock-in—switch providers or self-host anytime

Where Proprietary Still Leads

  • Ease of use—simple API, no infrastructure required
  • Latest capabilities—newest features ship to APIs first
  • Multimodal breadth—more modalities (audio, video, tools)
  • Safety alignment—more robust RLHF and safeguards

The verdict: For coding tasks, open source models like GLM-5 (Reasoning) now match or exceed proprietary alternatives. The gap has effectively closed for most practical applications.

Getting Started with Open Source LLMs

🚀Easiest Path: API Providers

Use hosted APIs for open models. Get the benefits of open source with the ease of SaaS.

Together.ai — Competitive pricing, great selection

Fireworks.ai — Fast inference, good for production

Groq — Fastest inference available

🖥️Self-Hosting Options

Run models on your own infrastructure for maximum control and privacy.

Ollama — Easiest local setup, great for development

vLLM — Production-grade serving, excellent throughput

Text Generation Inference — HuggingFace's production server

Compare Open Source Models

Use our interactive tool to compare benchmarks, parameters, and provider pricing for all 12 open source models.

Frequently Asked Questions

What is the best open source LLM in January 2026?

As of January 2026, GLM-5 (Reasoning) leads our open source rankings with exceptional performance across coding () and reasoning () benchmarks. It's completely free to download and use under an open license.

What is the best free AI model in 2026?

The best free AI models in 2026 are GLM-5 (Reasoning) (Quality Index 49.64), DeepSeek V3.2 (Quality 57), and Qwen3-235B (Quality 57). All can be self-hosted without licensing fees. For API access without self-hosting, providers like Together.ai and Fireworks.ai offer competitive pricing starting at $0.20-0.50 per million tokens.

Can open source LLMs match GPT-5 or Claude?

Yes, for most tasks. GLM-5 (Reasoning) achieves on LiveCodeBench, matching GPT-5 on coding tasks. The gap has closed dramatically—open models now trail proprietary ones by only 5-7 quality index points on average.

What are the best Ollama models in January 2026?

For Ollama users, we recommend: Qwen2.5-72B for general tasks, DeepSeek-Coder-V2 for coding,Llama-3.3-70B for reasoning, and Mixtral-8x22B for cost-effective performance. All run efficiently with Ollama's quantization.

What hardware do I need to run open source LLMs?

7B-13B models run on consumer GPUs (16GB+ VRAM like RTX 4090).70B+ models need enterprise GPUs (A100/H100) or multi-GPU setups. For cost-effective local deployment, use quantized versions (GGUF, AWQ) which reduce memory 50-75% with minimal quality loss.

What does "open weights" mean vs "open source"?

Open weights means you can download and use the model, but may have license restrictions.Fully open source (like Llama 3.1, Qwen 2.5) includes training code and permissive licenses. All models in our ranking allow commercial use—check specific licenses for fine-tuning and redistribution terms.

Which open source LLM has the largest context window?

Llama 4 Scout supports up to 10 million tokens of context. MiniMax-Text-01 offers 4 million tokens. For practical use, most models like GLM-5 (Reasoning) offer 128K-256K tokens, which handles most real-world document processing needs.

Related Model Rankings

Data sources: Rankings based on the Artificial Analysis Intelligence Index. Explore all models in our interactive explorer or compare models side-by-side.