Open-source LLM releases accelerated in June 2026 when four major models landed on Hugging Face within six weeks. DeepSeek AI shipped V4 on June 9 with 1.6 trillion total parameters, 40 billion active parameters, a 256,000 token context window, and an MIT license. Meta released Llama 4 Maverick on May 28 with 780 billion total parameters, 52 billion active, a 1 million token context, and a Llama Community License. Mistral AI shipped Large 3 on June 2 with 540 billion total parameters, 32 billion active, a 512,000 token context, and Apache 2.0. Alibaba released Qwen 3.5 Max on May 15 with 420 billion dense parameters, a 1 million token context, and Apache 2.0. All models are open-weight and downloadable from their respective repositories.
Why this matters is simple. These models now match or beat closed systems on many coding and agentic benchmarks. DeepSeek V4 posts a 92.4 on HumanEval and 78.1 on SWE-bench Verified, within three points of GPT-5.5. Llama 4 Maverick reaches 88.7 on HumanEval with a context window five times larger than most closed chat models. Mistral Large 3 scores 86 on multilingual MMLU, and Qwen 3.5 Max leads open models on function calling. The cost story is also different. You can run quantized versions on local hardware and avoid per token API fees. Free AI News covered that local stack in best free AI models 2026.
License changes separate these releases from earlier open model waves. DeepSeek V4 uses MIT. Mistral Large 3 and Qwen 3.5 Max use Apache 2.0 with no usage cap. Llama 4 Maverick uses a community license with a 700 million monthly active user limit, which still allows most startups and researchers. That spread gives teams real options. The open-source tier is no longer just a research toy. It is a production choice. But the tradeoffs are real. Larger models need expensive GPUs for full precision, and quantization can degrade long agent traces. The broader paid tier shift is covered in major AI model tier changes.
This guide compares DeepSeek V4, Llama 4 Maverick, Mistral Large 3, and Qwen 3.5 Max across coding benchmarks, local hardware requirements, agentic tool calling, and license conditions. We link directly to model cards on Hugging Face and GitHub repos. We also flag which model fits a solo developer on a Mac Studio versus an enterprise team running RAG at scale. The goal is not to crown a single winner. The goal is to show which open model fits your workload and budget in 2026 without paying per token fees or surrendering your data to a closed API.
How Do the Top Options Compare?
| Model | Parameters | Context Window | License | Coding Benchmark | Best For |
|---|---|---|---|---|---|
| DeepSeek V4 | 1.6T MoE, 40B active | 256K | MIT | HumanEval 92.4, SWE-bench 78.1 | Agentic coding and reasoning |
| Llama 4 Maverick | 780B MoE, 52B active | 1M | Llama Community License | HumanEval 88.7, SWE-bench 69.3 | Local agentic workflows |
| Mistral Large 3 | 540B MoE, 32B active | 512K | Apache 2.0 | HumanEval 90.1, Multilingual MMLU 86 | Multilingual and enterprise RAG |
| Qwen 3.5 Max | 420B dense | 1M | Apache 2.0 | HumanEval 89.2, SWE-bench 71.5 | Permissive local fine-tuning |
Benchmark scores are from vendor releases and the Open LLM Leaderboard as of June 2026. Active parameter counts shown for mixture of experts models. Full precision memory estimates assume 16-bit weights.
1. DeepSeek V4 , Best for Agentic Coding and Reasoning
DeepSeek AI shipped V4 on Hugging Face on June 9, 2026. The model uses a 1.6 trillion parameter mixture of experts design with 40 billion active parameters. It carries an MIT license and a 256,000 token context window. The release includes 4-bit and 8-bit quantized checkpoints plus full precision weights. This launch came four months after V3.5 and closed most of the gap to closed frontier models. V4 leads the open leaderboard on coding and agentic tasks. It posts a 92.4 on HumanEval and 78.1 on SWE-bench Verified. Those scores put it within three points of GPT-5.5 on the same tests. The 40 billion active parameters keep generation costs low while the full 1.6 trillion weights store broad knowledge. Developers can run the 4-bit version on a pair of 80GB GPUs. The GitHub repo includes vLLM and SGLang configs. Free AI News covered the larger shift in AI updates today June 2026.
Key strengths:
- ✅ Leads open leaderboard on SWE-bench Verified with 78.1
- ✅ MIT license permits commercial fine-tuning
- ✅ 256K context handles large codebases
- ✅ Active parameter efficiency lowers serving cost
- ✅ Available in 4-bit for 80GB GPUs
- ❌ 1.6T total weights require 800GB for full precision
- ❌ MoE routing can be finicky for long agent traces
- ❌ No safety alignment for some high-risk tasks
Who it’s for: Developers building coding agents or reasoning pipelines who need top open benchmarks and commercial freedom.
2. Llama 4 Maverick , Best for Local Agentic Work on Consumer Hardware
Meta AI released Llama 4 Maverick on Hugging Face on May 28, 2026. It is a 780 billion parameter mixture of experts model with 52 billion active parameters and a 1 million token context window. The license is the Llama Community License with a 700 million monthly active user cap. Meta AI hosts the official announcement and model card. Maverick targets local agentic workflows. The 4-bit quantized version runs on a 64GB Apple Mac Studio or dual 24GB consumer GPUs. It handles whole repository analysis and long tool calling traces thanks to the 1M context. The model scored 88.7 on HumanEval and 69.3 on SWE-bench Verified, trailing DeepSeek V4 on coding but leading on long context retrieval. The GitHub repo includes Llama Stack tool calling examples. For teams avoiding API fees, this model pairs well with the free local stack described in best free AI models 2026.
Key strengths:
- ✅ 1M token context supports whole repo analysis
- ✅ Runs on a 64GB Mac Studio in 4-bit
- ✅ Strong tool calling with Llama Stack
- ✅ Community license allows most commercial use under 700M MAU
- ✅ Large ecosystem of GGUF and EXL2 quants
- ❌ 700 million monthly active user cap before special license
- ❌ Slower than dense models on single GPU
- ❌ Benchmark scores trail DeepSeek V4 on agentic tasks
Who it’s for: Local AI tinkerers and small startups that need long context agentic models without API fees.
3. Mistral Large 3 , Best Multilingual Open-Weight Model for Enterprise RAG
Mistral AI released Large 3 on Hugging Face on June 2, 2026. It is a 540 billion parameter mixture of experts model with 32 billion active parameters. The context window is 512,000 tokens and the license is Apache 2.0 with no commercial restrictions. Mistral AI published the model card and benchmark scores. Large 3 is the strongest open model for multilingual enterprise retrieval augmented generation. It scores 90.1 on HumanEval and 86 on multilingual MMLU. The 32 billion active parameters keep serving cost low for high volume RAG pipelines. Native function calling and a stable JSON mode make it practical for production. The model ships with vLLM and TensorRT-LLM support on day one. Mistral also offers a hosted version through Le Chat, but the open weights avoid per token fees. See Mistral Vibe Le Chat free tier for the hosted option.
Key strengths:
- ✅ Apache 2.0 with no user cap
- ✅ 32B active lowers server cost
- ✅ Multilingual MMLU 86 outperforms Llama 4
- ✅ Native function calling for RAG
- ✅ Available via vLLM and TensorRT-LLM day one
- ❌ Smaller active parameter count limits complex math
- ❌ Coding benchmarks below DeepSeek V4
- ❌ Requires 200GB for full precision
Who it’s for: Enterprises in regulated industries that need permissive licensing and multilingual retrieval.
4. Qwen 3.5 Max , Best Permissive Model for Fine-Tuning and Edge Deployment
Alibaba released Qwen 3.5 Max on Hugging Face on May 15, 2026. It is a 420 billion parameter dense model with a 1 million token context window and Apache 2.0 license. The release also includes distilled 72B and 14B checkpoints. GitHub hosts fine-tuning and inference code. Qwen 3.5 Max is the top choice for teams that need to fine-tune a permissive model. The dense architecture works well with LoRA and QLoRA. The 14B distilled variant runs on a single 16GB laptop. The base model scores 89.2 on HumanEval and 71.5 on SWE-bench Verified. It also leads the Berkeley Function Calling Leaderboard for open models. For developers watching budget shifts, Qwen’s Apache license removes the usage caps found in some competitor licenses. Free AI News compared these pricing and access changes in AI subscription tiers compared.
Key strengths:
- ✅ Apache 2.0 allows unrestricted commercial use
- ✅ Dense architecture easier to fine-tune with LoRA
- ✅ Distilled 14B runs on 16GB RAM
- ✅ Strong agentic benchmark on Berkeley Function Calling Leaderboard
- ✅ 1M context in base model
- ❌ 420B dense full precision requires 840GB
- ❌ Dense model slower per token than MoE peers
- ❌ English benchmarks slightly behind DeepSeek V4
Who it’s for: ML teams fine-tuning domain-specific agents or edge deployments that need a permissive stack.
Frequently Asked Questions
What is the best open-source LLM for coding in 2026?
DeepSeek V4 leads with HumanEval 92.4 and SWE-bench Verified 78.1 as of June 2026. For local coding on weaker hardware, Qwen 3.5 Max 14B distilled is a strong fallback.
Which open-source LLM has the most permissive license in 2026?
Mistral Large 3 and Qwen 3.5 Max use Apache 2.0 with no commercial restrictions. DeepSeek V4 uses MIT. Llama 4 Maverick uses a community license with a 700 million monthly active user cap.
Can I run these open LLMs locally?
Yes, quantized versions run on consumer hardware. Llama 4 Maverick 4-bit fits a 64GB Mac Studio. Qwen 3.5 Max 14B fits 16GB RAM. DeepSeek V4 4-bit needs about 80GB GPU memory.
What is the best open-source LLM for agentic AI?
DeepSeek V4 and Llama 4 Maverick tie for top agentic tool calling, but DeepSeek V4 has better SWE-bench results. Llama 4 Maverick offers longer context for long agent traces.
Are open-source LLMs in 2026 as good as closed models like GPT-5.5?
They are close. DeepSeek V4 sits within three points of GPT-5.5 on coding benchmarks and beats it on some reasoning tasks, but closed models still lead on safety and multimodal.
Where can I download these models?
Hugging Face hosts all four model cards and weights. GitHub repos include inference and fine-tuning code.
What Should You Remember?
- DeepSeek V4 leads open coding benchmarks with 92.4 HumanEval and 78.1 SWE-bench.
- Llama 4 Maverick offers the longest 1M context for local agentic use under a community license.
- Mistral Large 3 is the best Apache 2.0 model for multilingual enterprise RAG.
- Qwen 3.5 Max makes fine-tuning easy with a dense 420B and distilled 14B variant.
- Licenses vary: MIT, Apache 2.0, and Llama Community License each have different commercial limits.
- Local deployment works with 4-bit quantization on 16GB to 80GB depending on model size.
- Benchmarks matter but test your own agentic workflows because leaderboard scores do not capture tool use reliability.
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