Best Open Source LLMs 2026
Open-source LLMs provide powerful language capabilities without API costs or data privacy concerns. These models can be self-hosted, fine-tuned, and customised for specific use cases, offering an alternative to proprietary models.
Methodology
How we evaluated
- Model quality
- Licence terms
- Hardware requirements
- Fine-tuning support
- Community ecosystem
Rankings
Our top picks
Llama 3 (Meta)
Meta's latest open-weights LLM family available in 8B, 70B, and 405B parameter sizes. Strong general performance competitive with GPT-4 class models at the 405B tier.
Best for: Organisations wanting a powerful, customisable open model with broad capabilities
Features
- 8B to 405B parameter sizes
- 128K context window
- Multi-language support
- Tool use capability
- Instruction-tuned variants
Pros
- Competitive with proprietary models
- Large community and ecosystem
- Good fine-tuning support
Cons
- 405B requires significant hardware
- Meta community licence has restrictions
Mistral Large / Mixtral
French AI lab Mistral's family of efficient open models. Mixtral uses mixture-of-experts for high performance with lower compute, while Mistral Large competes with frontier models.
Best for: Teams needing efficient, multilingual open models
Features
- Mixture-of-experts architecture
- Efficient inference
- Multi-language strength
- Apache 2.0 licence (Mixtral)
- 32K context window
Pros
- Excellent efficiency via MoE
- Strong European language support
- Apache 2.0 for Mixtral
Cons
- Mistral Large not fully open
- Smaller ecosystem than Llama
Qwen 2.5 (Alibaba)
Alibaba's open-source LLM series with strong performance across benchmarks. Available in sizes from 0.5B to 72B with excellent multilingual and coding capabilities.
Best for: Teams wanting a strong Apache-licensed model with coding and multilingual abilities
Features
- 0.5B to 72B sizes
- Strong coding ability
- Multilingual support
- 128K context window
- Apache 2.0 licence
Pros
- True Apache 2.0 open source
- Excellent coding performance
- Wide size range
Cons
- Less community tooling than Llama
- Newer with fewer integrations
Gemma 2 (Google)
Google's open model family designed for efficiency and responsible AI. Available in 2B, 9B, and 27B sizes with strong performance relative to their size.
Best for: Developers wanting efficient, smaller models with strong safety features
Features
- 2B to 27B sizes
- Efficient architecture
- Safety features built in
- Google ecosystem support
- Research-friendly licence
Pros
- Excellent size-to-performance ratio
- Good safety features
- Google support
Cons
- Smaller sizes limit capability
- Restrictive usage terms for large deployments
Phi-3 (Microsoft)
Microsoft's small language model series designed for efficiency. Achieves impressive performance at small sizes (3.8B), suitable for on-device and resource-constrained deployments.
Best for: Edge deployment and resource-constrained environments
Features
- 3.8B to 14B sizes
- On-device capable
- Strong reasoning
- 128K context option
- MIT licence
Pros
- Remarkable for its size
- True MIT open source
- Runs on devices
Cons
- Limited capability vs large models
- Narrower knowledge
DeepSeek-V3
Chinese AI lab DeepSeek's latest open model with strong reasoning and coding performance. Uses innovative training techniques to achieve competitive performance at lower cost.
Best for: Research teams and developers exploring cost-efficient frontier models
Features
- Strong reasoning
- Excellent coding
- Cost-efficient training
- Open weights
- 671B MoE architecture
Pros
- Competitive with much larger models
- Innovative training approach
- Strong benchmarks
Cons
- Chinese origin raises data concerns for some
- Large model size for MoE
Grove AI
AI Consultancy
Grove AI helps businesses adopt artificial intelligence fast. From strategy to production in weeks, not months.
Compare
Quick comparison
| Tool | Best For | Pricing |
|---|---|---|
| Llama 3 (Meta) | Organisations wanting a powerful, customisable open model with broad capabilities | Free under Llama community licence |
| Mistral Large / Mixtral | Teams needing efficient, multilingual open models | Free (Mixtral), Mistral Large via API |
| Qwen 2.5 (Alibaba) | Teams wanting a strong Apache-licensed model with coding and multilingual abilities | Free under Apache 2.0 |
| Gemma 2 (Google) | Developers wanting efficient, smaller models with strong safety features | Free under Gemma terms |
| Phi-3 (Microsoft) | Edge deployment and resource-constrained environments | Free under MIT licence |
| DeepSeek-V3 | Research teams and developers exploring cost-efficient frontier models | Free under DeepSeek licence |
FAQ
Frequently asked questions
It varies. Some models like Qwen 2.5 use Apache 2.0 (fully permissive). Others like Llama 3 use custom licences that restrict commercial use above certain thresholds. Always check the specific licence terms.
The largest open models (Llama 3 405B, DeepSeek-V3) approach GPT-4 level on many benchmarks. For specific tasks, fine-tuned smaller models can match or exceed GPT-4 in their domain.
Small models (7-8B) run on consumer GPUs with 8GB VRAM. 70B models need 40GB+ VRAM or quantisation. 405B+ models require multi-GPU setups. CPU inference is possible but much slower.
Use frameworks like Axolotl, Unsloth, or Hugging Face TRL. LoRA and QLoRA enable fine-tuning on consumer hardware. You need a dataset of examples relevant to your use case.
Open models can be production-ready with proper safety measures: content filtering, output validation, monitoring, and human oversight. Models like Gemma 2 include built-in safety features.
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