Open Source LLM Comparison 2026: Llama, Qwen, Mistral, DeepSeek Benchmarked
The open-source LLM landscape in 2026 is genuinely different from what it was 18 months ago. The performance gap between open and closed models has narrowed significantly, the licensing landscape has gotten more complex (and in some cases more permissive), and the hardware requirements have gotten more accessible. This is a real comparison of the four models that come up most often in production decisions.
One note before the comparison: "open source" is used loosely here and in the industry. True open source means openly licensed weights, architecture, and training data. Most models in this space release weights with custom licenses (not OSI-approved) and don't release training data. I'll note where licenses create commercial use restrictions.
The four models
Meta Llama 3.3 70B: Meta's third-generation Llama series, 70B parameter version released in late 2024. Uses a grouped query attention architecture. Available in 8B, 70B, and the discontinued 405B variants.
Qwen 2.5 (72B and 7B): Alibaba's Qwen series, with the 2.5 generation released through late 2024. Strong multilingual capabilities, particularly for Chinese-English tasks. Available in sizes from 0.5B to 72B, plus specialized code and math variants.
Mistral Large 2 (123B): Mistral AI's flagship model, released mid-2024. French company with strong European enterprise positioning. 123B parameters, multilingual, function calling support. Available through Mistral's API and for self-hosting.
DeepSeek-V3: Chinese AI lab DeepSeek's latest model, released in late 2024. 685B parameters total with a Mixture of Experts architecture that activates 37B parameters per token. Strong code and math performance. Open weights with a custom license.
Benchmark performance
Benchmarks are imperfect but they're the only systematic comparison tool we have. Here are the numbers that matter for production use cases:
MMLU (general knowledge, reasoning)
| Model | MMLU Score |
|---|---|
| DeepSeek-V3 | 88.5% |
| Mistral Large 2 | 84.0% |
| Qwen 2.5 72B | 86.1% |
| Llama 3.3 70B | 86.0% |
For general-purpose tasks, all four are in a narrow band. The differences here aren't large enough to drive deployment decisions.
HumanEval (code generation)
| Model | HumanEval Score |
|---|---|
| DeepSeek-V3 | 89.1% |
| Qwen 2.5 72B | 86.9% |
| Mistral Large 2 | 81.2% |
| Llama 3.3 70B | 80.1% |
For coding tasks, DeepSeek-V3 and Qwen 2.5 have a meaningful edge. If your use case is code generation, this gap matters.
GSM8K (grade school math, reasoning)
DeepSeek-V3 and Qwen 2.5 again lead here, both above 90% on this benchmark, with Llama 3.3 and Mistral Large 2 slightly behind. Math-focused reasoning tasks should go to one of these two.
These are numbers from published technical reports. In practice, benchmark performance correlates with but doesn't perfectly predict quality on your specific tasks. Always run your actual production prompts against any model you're considering.
License terms: where things get complicated
This is the section that should drive more deployment decisions than it does.
Meta Llama 3.3: The Llama 3 Community License is permissive for most commercial use cases, with a notable restriction: if your product or service has more than 700 million monthly active users, you need a separate license from Meta. For every company not at Facebook/TikTok scale, you're effectively free to use it commercially. Meta requires attribution in your product and prohibits using Llama to train other models that compete with Llama or Meta's commercial products.
Qwen 2.5: Released under the Apache 2.0 license for the 72B and smaller models. Apache 2.0 is a proper open-source license that permits commercial use, modification, and distribution with minimal requirements (attribution and including the license). This is the most commercially friendly license in this group.
Mistral Large 2: This is where it gets restrictive. Mistral Large 2 is released under the Mistral Research License, which explicitly prohibits commercial use without a Mistral commercial license. You can experiment with it for research and personal use, but if you're building a product or service, you need to either use Mistral through their API (which is fine for API-based deployments) or sign a commercial self-hosting agreement. The open weights are research weights, not production weights.
DeepSeek-V3: The DeepSeek Model License permits commercial use, but with restrictions. You cannot use DeepSeek models to build competing AI model products, you cannot misrepresent outputs as human-generated, and there are geographic restrictions that have become more complex following geopolitical developments in 2025. For US companies, consult legal counsel about current applicable restrictions before deploying DeepSeek in production.
The practical summary: Qwen 2.5 under Apache 2.0 is the cleanest commercial license. Llama 3.3 is permissive for virtually all companies below hyperscale. Mistral Large 2 requires either their API or a separate commercial agreement. DeepSeek-V3 needs legal review for US deployments.
Hardware requirements for self-hosting
This section matters if you're considering running these models on your own infrastructure rather than through an API.
Llama 3.3 70B:
- Full precision (fp16): requires approximately 140GB VRAM. That's two A100 80GB GPUs at minimum, or one H100 80GB in some configurations with optimization.
- 4-bit quantized (GGUF Q4_K_M): runs on approximately 40GB VRAM. Fits on a single A100 40GB or an RTX 4090 24GB with careful memory management.
- 8-bit quantized: approximately 70GB VRAM.
- Cloud cost for 2xA100 80GB: roughly $6-10/hour on AWS or Azure.
Qwen 2.5 72B: Similar requirements to Llama 3.3 70B due to comparable parameter count. The 7B variant runs on consumer GPUs (RTX 4080 or better) and is worth considering for latency-sensitive use cases that don't need 72B-level capability.
Mistral Large 2 (123B):
- Full precision: requires approximately 246GB VRAM. Multiple high-end GPUs required.
- 4-bit quantized: approximately 65-70GB VRAM. Two 40GB GPUs or one 80GB GPU.
- This is the heaviest hardware requirement in the group.
DeepSeek-V3 (685B MoE, 37B active):
- The MoE architecture means compute during inference is equivalent to a ~37B dense model, but parameter storage requires loading more. Full model requires substantial multi-GPU setups.
- Quantized versions: the community has produced Q4 quantizations that fit in 80-120GB VRAM, though quality degradation at this quantization level is noticeable on complex tasks.
- For most teams, running DeepSeek-V3 locally is not practical. Use the API.
The hardware cost question matters most for teams processing high volumes where API costs would exceed self-hosting costs. The rough break-even for a 70B class model: if you're doing more than 10 million tokens per day on a sustained basis, self-hosting a Llama 3.3 70B instance (or similar) can be cheaper than API access. Below that volume, the API is almost always more economical.
Where each model actually wins
Llama 3.3 70B: Best choice for teams that want broad ecosystem support. Llama has the largest fine-tuning ecosystem, the most tooling support (Ollama, llama.cpp, vLLM, Text Generation Inference), and the most deployment flexibility. If you need a community, documentation, and pre-built fine-tuned variants (Llama 3.3 Instruct, code-specialized variants), Llama is where that exists. Not the strongest benchmarker, but the most practical for organizations building on top of open models.
Qwen 2.5 72B: Best choice for code and math-heavy applications, and the only one in this group with a fully permissive Apache 2.0 license. If your use case involves code generation, technical content, or numeric reasoning, Qwen 2.5 outperforms Llama 3.3 at the same parameter size. The multilingual strength (particularly Chinese-English) makes it the obvious choice for global applications.
Mistral Large 2: Best choice for European enterprises with data residency requirements. Mistral is a French company, and their commercial offerings include EU data processing guarantees that US providers can't match. The model quality is strong. The license complexity for self-hosting is the trade-off. Use via their API for the most straightforward path.
DeepSeek-V3: Best raw performance per compute-required-at-inference in this group. For organizations that can use it (after legal review for US deployments), it's competitive with GPT-4o and Claude 3.5 Sonnet on coding and math tasks. The political/legal risk and hardware complexity for self-hosting are real limitations.
The context that benchmarks miss
Latency is not well-represented in benchmark comparisons, but it matters enormously for user-facing applications. A 70B model takes significantly longer to generate a response than a 7B model, even with the same hardware. If your application requires sub-2-second response times, you're probably looking at 7B models (Llama 3.3 8B, Qwen 2.5 7B) rather than 70B+.
Fine-tuning is the other consideration benchmarks ignore. For specialized use cases (a model that knows your product documentation, your legal ontology, your coding standards), fine-tuning a smaller model often outperforms a larger base model on your specific task. Llama 3.3 has the strongest fine-tuning ecosystem. Qwen 2.5 is a close second. For teams considering fine-tuning, these two are the practical options.
The honest bottom line: if you need Apache 2.0 commercial freedom and strong benchmark performance, Qwen 2.5 72B is the pick. If you need the biggest ecosystem and most tooling support, Llama 3.3 70B. If you're in Europe and data residency matters, Mistral via their API. DeepSeek-V3 is worth watching but needs careful legal review before production deployment in the US.