Best Of
5 Best Open Source LLMs (February 2026)
Open source AI has caught up to closed-source systems. These five large language models (LLMs) deliver enterprise-grade performance without the recurring API costs or vendor lock-in. Each handles different use cases, from on-device reasoning to multilingual support at scale.
This guide breaks down GPT-OSS-120B, DeepSeek-R1, Qwen3-235B, LLaMA 4, and Mixtral-8x22B with specific details on capabilities, costs, and deployment requirements.
Quick Comparison
| Tool | Best For | Starting Price | Key Feature |
|---|---|---|---|
| GPT-OSS-120B | Single-GPU deployment | Free (Apache 2.0) | Runs on 80GB GPU with 120B parameters |
| DeepSeek-R1 | Complex reasoning tasks | Free (MIT) | 671B parameters with transparent thinking |
| Qwen3-235B | Multilingual applications | Free (Apache 2.0) | Supports 119+ languages with hybrid thinking |
| LLaMA 4 | Multimodal processing | Free (custom license) | 10M token context window |
| Mixtral-8x22B | Cost-efficient production | Free (Apache 2.0) | 75% compute savings vs dense models |
1. GPT-OSS-120B
OpenAI released their first open-weight models since GPT-2 in August 2025. GPT-OSS-120B uses a mixture-of-experts architecture with 117 billion total parameters but only 5.1 billion active per token. This sparse design means you can run it on a single 80GB GPU instead of requiring multi-GPU clusters.
The model matches o4-mini performance on core benchmarks. It hits 90% accuracy on MMLU tests and around 80% on GPQA reasoning tasks. Code generation sits at 62% pass@1, competitive with closed-source alternatives. The 128,000-token context window handles comprehensive document analysis without chunking.
OpenAI trained these models using techniques from o3 and other frontier systems. The focus was practical deployment over raw scale. They open-sourced the o200k_harmony tokenizer alongside the models, standardizing how inputs get processed across implementations.
Pros and Cons
- Single 80GB GPU deployment eliminates multi-GPU infrastructure costs
- Native 128K context window processes entire codebases or long documents
- Apache 2.0 license allows unrestricted commercial use and modification
- Reference implementations in PyTorch, Triton, and Metal simplify integration
- 90% MMLU accuracy matches proprietary models at reasoning benchmarks
- English-focused training limits multilingual capabilities compared to alternatives
- 5.1B active parameters may underperform dense models on specialized tasks
- Requires 80GB VRAM minimum excludes consumer-grade GPU deployment
- No distilled variants available yet for resource-constrained environments
- Limited domain specialization compared to fine-tuned alternatives
Pricing: GPT-OSS-120B operates under Apache 2.0 licensing with zero recurring costs. You need hardware capable of running 80GB models (NVIDIA A100 or H100 GPUs). Cloud deployment on AWS, Azure, or GCP costs approximately $3-5 per hour for appropriate instance types. Self-hosted deployment requires one-time GPU purchase (~$10,000-15,000 for used A100).
No subscription fees. No API limits. No vendor lock-in.
2. DeepSeek-R1
DeepSeek-R1 built their model specifically for transparent reasoning. The architecture uses 671 billion total parameters with 37 billion activated per forward pass. Training emphasized reinforcement learning without traditional supervised fine-tuning first, letting reasoning patterns emerge naturally from the RL process.
The model achieves 97% accuracy on MATH-500 evaluations and matches OpenAI’s o1 on complex reasoning tasks. What separates DeepSeek-R1 is you can observe its thinking process. The model shows step-by-step logic instead of just final answers. This transparency matters for applications where you need to verify reasoning, like financial analysis or engineering verification.
DeepSeek released six distilled versions alongside the main model. These range from 1.5B to 70B parameters, running on hardware from high-end consumer GPUs to edge devices. The Qwen-32B distill outperforms o1-mini across benchmarks while requiring fraction of the compute.
Pros and Cons
- 97% MATH-500 accuracy leads open-source models on mathematical reasoning
- Transparent thinking process enables verification and debugging
- 671B parameter scale provides deep analytical capabilities
- Six distilled variants enable deployment across hardware configurations
- MIT license permits unrestricted commercial use
- 671B parameters require substantial infrastructure for full model deployment
- Reasoning mode increases latency compared to direct answer generation
- English-optimized training limits performance in other languages
- Reinforcement learning approach can produce verbose explanations
- Community tooling still maturing compared to more established models
Pricing: DeepSeek-R1 releases under MIT license with no usage fees. Full 671B model requires 8x A100 GPUs minimum (cloud cost: ~$25-30/hour). Distilled models run significantly cheaper: the 32B variant needs single A100 (~$3-5/hour cloud, ~$10,000 hardware purchase). The 7B version runs on consumer RTX 4090 GPUs.
DeepSeek provides free API access with rate limits for testing. Production deployment requires self-hosting or cloud infrastructure.
3. Qwen3-235B
Alibaba’s Qwen3-235B brings hybrid thinking to open-source models. Users control reasoning effort levels (low, medium, high) based on task complexity. Need quick customer service responses? Low thinking mode delivers fast answers. Running complex data analysis? High thinking mode applies methodical reasoning.
The architecture uses 235 billion total parameters with 22 billion activated across 94 layers. Each layer contains 128 experts with 8 activated per token. This expert selection enables efficient processing while maintaining capability. The model trained on 1 billion+ tokens across 119 languages, representing 10x more multilingual data than previous Qwen versions.
Performance sits at 87-88% MMLU accuracy with strong multilingual benchmarks. The model excels on C-Eval and region-specific assessments across Asia, Europe, and other markets. Code generation hits 37% zero-shot but improves significantly when activating thinking mode for complex programming tasks.
Pros and Cons
- 119+ language support enables global deployment without language barriers
- Hybrid thinking control optimizes cost-performance tradeoffs per request
- 128K token context handles extensive document analysis
- Apache 2.0 license permits commercial modification
- 87% MMLU performance competes with leading proprietary systems
- 235B parameters require multi-GPU setup for production deployment
- 37% code generation baseline trails specialized coding models
- Thinking mode selection adds complexity to application logic
- Chinese language bias shows stronger performance on Chinese vs other languages
- Limited community tooling compared to LLaMA ecosystem
Pricing: Qwen3-235B uses Apache 2.0 licensing without fees. Full model requires 4-8 A100 GPUs depending on quantization (cloud: ~$15-30/hour). Alibaba Cloud offers managed endpoints with pay-per-token pricing starting at $0.002/1K tokens for thinking mode, $0.0003/1K for standard mode.
Smaller Qwen3 variants (7B, 14B, 72B) run on consumer hardware. The 7B model works on 24GB consumer GPUs.
4. LLaMA 4
Meta’s LLaMA 4 introduces native multimodal capabilities across text, images, and short video. The Scout variant packs 109 billion total parameters with 17 billion active, while Maverick uses a larger expert pool for specialized tasks. Both process multiple content types through early fusion techniques that integrate modalities into unified representations.
Context handling reached new levels. LLaMA 4 Scout supports up to 10 million tokens for extensive document analysis applications. Standard context sits at 128K tokens, already substantial for most use cases. The models were pre-trained on 30+ trillion tokens, double the LLaMA 3 training mixture.
Performance benchmarks show LLaMA 4 outperforming GPT-4o and Gemini 2.0 Flash across coding, reasoning, and multilingual tests. Meta developed MetaP, a technique for reliably setting hyperparameters across model scales. This enables consistent performance when transferring learned parameters to different configurations.
Pros and Cons
- 10M token context window enables processing entire codebases or datasets
- Native multimodal processing handles text, image, and video inputs
- 30T token training provides comprehensive knowledge coverage
- Multiple size variants from edge deployment to datacenter scale
- Outperforms GPT-4o on coding and reasoning benchmarks
- Custom commercial license requires review for large-scale deployments
- Multimodal fusion adds complexity to deployment pipelines
- 10M context requires substantial memory even with optimizations
- Model size variations create confusion about which variant to use
- Documentation still emerging for newest features
Pricing: LLaMA 4 uses Meta’s custom commercial license (free for most uses, restrictions on services with 700M+ users). Scout variant requires 2-4 H100 GPUs (cloud: ~$10-20/hour). Maverick needs 4-8 H100s (~$20-40/hour). Meta provides free API access through their platform with rate limits.
Smaller LLaMA variants run on consumer hardware. The 8B model works on 16GB GPUs. Enterprise deployments can negotiate direct licensing with Meta.
5. Mixtral-8x22B
Mistral AI’s Mixtral-8x22B achieves 75% computational savings versus equivalent dense models. The mixture-of-experts design contains eight 22-billion-parameter experts totaling 141 billion parameters, but only 39 billion activate during inference. This sparse activation delivers superior performance while running faster than dense 70B models.
The model supports native function calling for sophisticated application development. You can connect natural language interfaces directly to APIs and software systems without custom integration layers. The 64,000-token context window handles extended conversations and comprehensive document analysis.
Multilingual performance stands out across English, French, Italian, German, and Spanish. Mistral trained specifically on European languages, resulting in stronger performance than models with broader but shallower language coverage. Mathematical reasoning hits 90.8% on GSM8K and coding achieves strong results on HumanEval and MBPP benchmarks.
Pros and Cons
- 75% compute reduction versus dense models lowers infrastructure costs
- Native function calling simplifies API integration
- Strong European language support for multilingual applications
- 90.8% GSM8K accuracy delivers solid mathematical reasoning
- Apache 2.0 license permits unrestricted commercial use
- 64K context shorter than competitors offering 128K+ windows
- European language focus means weaker performance on Asian languages
- 39B active parameters may limit capability on complex reasoning tasks
- Expert routing logic adds deployment complexity
- Smaller community compared to LLaMA ecosystem
Pricing: Mixtral-8x22B operates under Apache 2.0 licensing with no fees. Requires 2-4 A100 GPUs for production (cloud: ~$10-15/hour). Mistral offers managed API access at $2 per million tokens for input, $6 per million for output. Self-hosting eliminates per-token costs after initial hardware investment.
Quantized versions run on single A100 with acceptable performance degradation. The model’s efficiency makes it cost-effective for high-volume production workloads.
Which Model Should You Choose?
Your hardware dictates immediate options. GPT-OSS-120B fits single 80GB GPUs, making it accessible if you’re already running A100 infrastructure. DeepSeek-R1’s distilled variants handle resource constraints—the 7B model runs on consumer hardware while maintaining strong reasoning.
Multilingual requirements point toward Qwen3-235B for broad language coverage or Mixtral-8x22B for European languages specifically. LLaMA 4 makes sense when you need multimodal capabilities or extended context windows beyond 128K tokens.
Cost-conscious deployments favor Mixtral-8x22B for production workloads. The 75% compute savings compound quickly at scale. Research and development benefit from DeepSeek-R1’s transparent reasoning, especially when you need to verify decision logic.
All five models operate under permissive licenses. No recurring API costs. No vendor dependencies. You control deployment, data privacy, and model modifications. The open-source AI landscape reached parity with closed systems. These tools deliver enterprise capabilities without enterprise restrictions.
FAQs
What hardware do I need to run these open source LLMs?
Minimum requirements vary by model. GPT-OSS-120B needs a single 80GB GPU (A100 or H100). DeepSeek-R1’s full version requires 8x A100s, but distilled variants run on consumer RTX 4090s. Qwen3-235B and LLaMA 4 require 2-8 GPUs depending on quantization. Mixtral-8x22B runs efficiently on 2-4 A100s. Cloud deployment costs $3-40/hour based on model size.
Can these models match GPT-4 or Claude performance?
Yes, on specific benchmarks. DeepSeek-R1 matches OpenAI o1 on reasoning tasks with 97% MATH-500 accuracy. LLaMA 4 outperforms GPT-4o on coding benchmarks. GPT-OSS-120B achieves 90% MMLU accuracy, comparable to proprietary systems. However, closed-source models may excel in specialized areas like creative writing or nuanced conversation.
Which model handles multiple languages best?
Qwen3-235B supports 119+ languages with 10x more multilingual training data than competitors. It excels on Asian language benchmarks and cultural knowledge tests. Mixtral-8x22B leads for European languages (French, German, Spanish, Italian) with specialized training. Other models provide varying multilingual support but optimize primarily for English.
Are there usage costs beyond hardware?
No recurring fees for self-hosted deployments under Apache 2.0 or MIT licenses. LLaMA 4 uses a custom commercial license that’s free for most uses (restrictions apply to services with 700M+ users). Cloud hosting costs vary by provider and instance type. Managed API access from providers like Mistral starts at $2 per million input tokens.
What’s the difference between mixture-of-experts and dense models?
Mixture-of-experts architectures activate only a subset of parameters per input, achieving efficiency without sacrificing capability. GPT-OSS-120B uses 5.1B of 117B parameters per token. Dense models activate all parameters for every input. MoE models deliver 70-75% compute savings while matching or exceeding dense model performance at similar scales.
