Zhipu AI shipped GLM-5 on June 16, 2026, posting the full model weights to Hugging Face and the training code to GitHub. The release is under the MIT license, which means anyone can download, modify, fine-tune, and even use GLM-5 in a commercial product without paying fees or sharing changes. GLM-5 uses a mixture of experts design with 1.4 trillion total parameters and 55 billion active parameters per token. It supports a 256,000 token context window, long enough to process entire codebases, books, or legal documents in one pass. This is the biggest free MIT licensed model of 2026 so far.

Zhipu AI, the Chinese lab behind the GLM series, released GLM-5 through its Z.ai platform alongside the Hugging Face model card. The model card lists benchmark scores that put GLM-5 ahead of GPT-5.5 Turbo and Claude Opus 4.8 on several public tests. GLM-5 scores 89.2 on MMLU-Pro, 78.4 on GPQA Diamond, and 96.3 on HumanEval. Those numbers matter because closed model vendors have been pushing free users toward lighter models and paid tiers in 2026. An MIT licensed model that matches flagship closed performance changes the calculus for developers and small teams, as we noted in our coverage of major AI model tier changes in 2026.

The release lands during a messy period for free AI access. Google cut Gemini free tier quotas, Anthropic reset Claude rate limits and introduced credit pools, and coding tools like GitHub Copilot moved to usage based billing. GLM-5 does not solve hosted API costs, but it gives developers a way to run a frontier class model on their own hardware or through cheap cloud GPUs. Because the weights are open and the license is permissive, teams can avoid per token fees, avoid usage limits, and avoid vendor lock in. That is the core story behind GLM-5 as the top free MIT open source AI model of 2026. For broader context on free tier cuts, see Gemini free tier cuts in 2026.

There are caveats. GLM-5 requires significant GPU memory to run at full precision. A quantized version can run on a single 80GB GPU, but full 1.4T experts need multi-GPU setups. Zhipu AI also released GLM-5-Flash, a 52 billion parameter dense variant, for developers who want lower resource usage. We cover the benchmarks, license terms, how to run GLM-5 locally, and how it compares to Llama 4, DeepSeek V4, and Mistral Large 3 below. For more no cost options, check our guide to the best free AI models in 2026.

How Do the Top Options Compare?

Model License Parameters Context Window MMLU-Pro Best For
GLM-5 MIT 1.4T (55B active) 256K 89.2 Commercial use without restrictions
Llama 4 Llama Community License 800B (40B active) 1M 84.7 Research and Meta ecosystem
DeepSeek V4 MIT 1.6T (60B active) 128K 88.9 Math and reasoning
Qwen 3.5 Apache 2.0 510B (30B active) 256K 86.1 Multilingual and agentic tasks
Mistral Large 3 Apache 2.0 460B (32B active) 256K 85.5 European compliance and tool use

Benchmark scores are from public leaderboards as of June 2026. GLM-5-Flash, a 52B dense variant, is also available under MIT license but scores lower on MMLU-Pro.

1. GLM-5 , Best overall free MIT open source model

GLM-5 is a mixture of experts model released by Zhipu AI on June 16, 2026 under the MIT license. The model has 1.4 trillion total parameters but only activates 55 billion per token, which keeps inference costs lower than dense models of similar quality. It ships with a 256,000 token context window and achieves an 89.2 on MMLU-Pro, a 78.4 on GPQA Diamond, and a 96.3 on HumanEval. You can download the weights from Hugging Face and find the training and inference code on GitHub.

Compared with closed models in 2026, GLM-5 matches or beats GPT-5.5 Turbo on reasoning and coding while remaining free for commercial use. The MIT license has no attribution requirement, no share alike clause, and no use restrictions. This matters because many open models like Llama 4 still use a custom community license that limits users above 700 million monthly active users. GLM-5 removes that ceiling for startups and enterprises. See our best free AI models for 2026 for more no cost options.

Key strengths:

  • ✅ MIT license allows commercial use, modification, and redistribution without restrictions
  • ✅ 1.4T MoE with 55B active parameters delivers frontier level benchmark scores
  • ✅ 256K context handles large codebases and long documents in one pass
  • ✅ Full weights and training code are available on Hugging Face and GitHub
  • ✅ GLM-5-Flash variant offers a lower resource option for single GPU setups
  • ❌ Full precision inference requires substantial GPU memory and multi GPU hardware
  • ❌ Quantized versions reduce quality, especially on long context reasoning
  • ❌ Support and fine tuning resources are less mature than Meta or Mistral ecosystems

Who it’s for: Developers and startups that need a frontier class model without per token fees or license restrictions.

2. Llama 4 , Best for Meta ecosystem and long context research

Meta AI released Llama 4 earlier in 2026 with an 800 billion parameter mixture of experts design and a 1 million token context window. The model scores 84.7 on MMLU-Pro, which is strong but below GLM-5. Llama 4 uses a custom community license, not MIT, and restricts use for companies with more than 700 million monthly active users. You can read Meta’s announcement on Meta AI.

Llama 4 excels at very long context tasks and has deep integration with torch, vLLM, and the Meta ecosystem. However, the license and the smaller active parameter count make it less attractive for startups that need total freedom. Our coverage of major AI model tier changes in 2026 explains why open licenses now drive developer choice.

Key strengths:

  • ✅ 1M token context is the longest among mainstream open models
  • ✅ 800B total parameters with 40B active parameters is efficient
  • ✅ Strong integration with PyTorch and Meta’s open source stack
  • ❌ Llama Community License restricts very large commercial deployments
  • ❌ Benchmark scores trail GLM-5 on MMLU-Pro and GPQA Diamond
  • ❌ Multi GPU setup is recommended for full precision use

Who it’s for: Researchers and Meta ecosystem developers who need extreme context length and accept license limits.

3. DeepSeek V4 , Best for math and reasoning at low cost

DeepSeek released V4 under an MIT license in early 2026 with 1.6 trillion total parameters and 60 billion active parameters. Its 128K context window is shorter than GLM-5 but its math and reasoning scores are close: 88.9 on MMLU-Pro and 93.8 on MATH. The model is available on GitHub under the same permissive terms as GLM-5. DeepSeek V4 is a strong alternative if you care about math and code generation over long document processing.

DeepSeek V4 has a smaller context window and slightly lower GPQA Diamond score than GLM-5. It also requires more VRAM for full precision because of the 60B active parameters. Our article on free AI pricing changes in June 2026 shows why open weights are becoming the only stable free option for serious use.

Key strengths:

  • ✅ MIT license, no commercial restrictions
  • ✅ 1.6T total parameters, 60B active, delivers top math scores
  • ✅ Strong community support and many fine tunes already available
  • ❌ 128K context is half of GLM-5
  • ❌ Higher active parameter count means more VRAM per token
  • ❌ Fewer official safety tuned chat variants than GLM-5

Who it’s for: Developers focused on math, code, and reasoning who want a proven MIT licensed model.

4. Qwen 3.5 , Best for multilingual and agentic workflows

Alibaba’s Qwen 3.5 uses a 510 billion parameter mixture of experts model with 30 billion active parameters and a 256K context window. It scores 86.1 on MMLU-Pro and supports over 100 languages. Qwen 3.5 is licensed under Apache 2.0, which is permissive but not identical to MIT. The model has strong tool calling and agentic abilities, making it a good fit for autonomous coding and research agents.

Qwen 3.5 trails GLM-5 on raw reasoning benchmarks but often wins on multilingual tasks and function calling reliability. If your app serves non English users or relies on agentic tool use, Qwen 3.5 is worth testing. The open source release trend we track in AI updates today June 2026 shows Qwen and GLM competing hard on usability, not just raw scores.

Key strengths:

  • ✅ Apache 2.0 license with explicit patent grant
  • ✅ 256K context matches GLM-5
  • ✅ Best in class for multilingual prompts and agentic tool calling
  • ✅ 30B active parameters is efficient for single GPU inference
  • ❌ Overall reasoning scores below GLM-5 and DeepSeek V4
  • ❌ Chinese and English are strongest; some low resource languages still weak
  • ❌ Less transparent training data documentation than Zhipu AI

Who it’s for: Teams building multilingual agents or products that need strong function calling.

5. Mistral Large 3 , Best for European compliance and tool use

Mistral AI released Large 3 with 460 billion total parameters, 32 billion active, and a 256K context window under Apache 2.0. The model scores 85.5 on MMLU-Pro and performs well on tool use and function calling. Mistral emphasizes data privacy and European AI sovereignty, which matters for companies that need GDPR compliant self hosting. You can find the model on Hugging Face and read more on Mistral AI.

Mistral Large 3 is not the top raw benchmark performer, but it has the most polished fine tuning for enterprise tool use and low latency deployment. The license is permissive, but Apache 2.0 includes patent and trademark clauses that some legal teams prefer over MIT. Check our note on Anthropic ending the agent subsidy to see why self hosted EU compliant models are gaining traction.

Key strengths:

  • ✅ Apache 2.0 license with clear patent grant
  • ✅ 256K context and 32B active parameters balance quality and speed
  • ✅ Strong enterprise support for tool calling and privacy
  • ✅ Smaller active parameter count runs on affordable hardware
  • ❌ Benchmark scores trail GLM-5 by several points on MMLU-Pro
  • ❌ Fewer community fine tunes compared with Qwen and DeepSeek
  • ❌ Less open about training data than Zhipu AI’s GLM-5

Who it’s for: European companies and regulated industries that need self hosted, privacy compliant models.

Frequently Asked Questions

Is GLM-5 actually free for commercial use?

Yes. GLM-5 is released under the MIT license. You can download, modify, fine tune, and use it in commercial products without paying fees or sharing your changes.

What hardware do I need to run GLM-5?

Full precision requires multiple GPUs with high VRAM. A 4 bit quantized GLM-5-Flash can run on a single 80GB GPU, while the full 1.4T model needs around 320GB of GPU memory for long context inference.

How does GLM-5 compare to Llama 4?

GLM-5 uses MIT license and scores higher on MMLU-Pro and GPQA Diamond. Llama 4 has a longer 1M token context window but uses a restrictive community license.

Where can I download GLM-5?

The weights are on Hugging Face and the training code is on GitHub. The model card includes instruction tuned and base checkpoints.

Does GLM-5 have API access?

Zhipu AI offers a hosted API through Z.ai, but the open weights mean you can also self host. API pricing is separate from the open source release.

Can I fine tune GLM-5 on my own data?

Yes. The MIT license permits fine tuning and redistribution. Zhipu AI released training scripts and recommended hyperparameters.

What Should You Remember?

  • MIT license: GLM-5 is free for commercial use with no attribution or share alike requirements.
  • 1.4T parameters: The mixture of experts model activates 55B parameters per token, balancing quality and speed.
  • 256K context: GLM-5 handles long documents and codebases in a single pass, unlike many paid models.
  • Benchmarks: GLM-5 scores 89.2 on MMLU-Pro and 96.3 on HumanEval, beating GPT-5.5 Turbo and Claude Opus 4.8.
  • Run it yourself: Download weights from Hugging Face and use vLLM or SGLang for local inference.
  • Flash variant: GLM-5-Flash, a 52B dense model, fits on a single 80GB GPU for smaller teams.
  • License shift: Open MIT models like GLM-5 undercut paid API free tier limits and usage based billing in 2026.

Free AI News is an independent editorial publication. Information about AI pricing, free-tier limits, and features changes frequently and may become outdated. Always verify current details through the vendor’s official pages. Affiliate links may earn a commission at no cost to you, and never affect our reporting.