Open source LLMs have closed the gap with proprietary models dramatically in 2026. Llama 3 (Meta), Mistral, Qwen 2.5 (Alibaba), and Gemma 3 (Google) all offer competitive performance at a fraction of the API cost. But choosing between them involves more than benchmark numbers — licensing, hardware requirements, fine-tuning ecosystem, and multimodal capabilities vary significantly. This comparison helps you pick the right model for your use case.
Quick Comparison
| Feature | Llama 3.1 (Meta) | Mistral Large 2 | Qwen 2.5 (Alibaba) | Gemma 3 (Google) |
|---|---|---|---|---|
| Sizes Available | 8B, 70B, 405B | 7B, 8x7B (MoE), 123B | 0.5B, 1.8B, 7B, 14B, 32B, 72B | 1B, 4B, 12B, 27B |
| Context Window | 128K (8B/70B), 128K (405B) | 128K (123B), 32K (others) | 128K (all sizes), 1M (Turbo variant) | 8K (free), 32K (commercial) |
| License | Llama 3.1 Community (open, with restrictions for 405B) | Apache 2.0 (open), Research (Large) | Apache 2.0 (most variants) | Gemma License (open, with usage restrictions) |
| Commercial Use | Yes (with limitations at 700M+ MAU) | Yes (Apache 2.0 models) | Yes | Yes (with attribution) |
| Hardware (8B inference) | RTX 4090 (24GB) — 4-bit quantized | RTX 4090 (24GB) — 4-bit quantized | RTX 3060 (12GB) — 4-bit quantized | RTX 4090 (24GB) |
| Multimodal | Llama 3.2 Vision (11B, 90B) | Pixtral (12B, vision) | Qwen-VL, Qwen-Audio | Gemma 3 Vision |
| Code Generation | Excellent (top-tier for open models) | Excellent (Codestral variant) | Very Good (CodeQwen variant) | Good |
| Fine-Tuning | LoRA/QLoRA, FSDP, Megatron ecosystem | LoRA/QLoRA, active community | LoRA/QLoRA, QLoRA-friendly | LoRA (Keras + JAX) |
Coding Benchmarks
| Benchmark | Llama 3.1 70B | Mistral Large 2 | Qwen 2.5 72B | Gemma 3 27B |
|---|---|---|---|---|
| HumanEval (Python) | 88.4% | 92.1% | 86.7% | 79.2% |
| MBPP | 87.2% | 89.5% | 85.9% | 76.5% |
| MultiPL-E (avg across 7 langs) | 75.8% | 78.3% | 72.1% | 65.4% |
| SWE-bench Verified | 34.6% | 40.2% | 29.8% | 22.1% |
When to Choose Each Model
Llama 3.1 — Best for: The safest open source choice — largest ecosystem, best documentation, most community support. The 8B model runs on a laptop, the 70B rivals GPT-4o on many tasks. Weak spot: The 405B model is impractical for most teams (requires 8x H100s); licensing restrictions at 700M+ MAU may concern large companies.
Mistral Large 2 — Best for: Coding tasks and European companies that value the French-based, privacy-conscious approach. Mistral's models punch above their weight class — the 123B often outperforms Llama 405B on reasoning. Weak spot: Smaller model ecosystem; the flagship Mistral Large 2 has a research license (not Apache 2.0).
Qwen 2.5 — Best for: Asian-language applications (Chinese, Japanese, Korean), budget-constrained deployments (the 7B runs on modest GPUs), and teams that need massive context (1M token variant). Weak spot: Smaller Western community; English benchmarks slightly behind Llama/Mistral.
Gemma 3 — Best for: Google Cloud/GCP shops, JAX/Keras ecosystem users, and teams that want a lightweight model with strong safety alignment. Weak spot: Smaller context window (32K); licensing has use restrictions that are stricter than Apache 2.0.
Bottom line: Llama 3.1 70B is the default open source choice — best ecosystem, solid benchmarks, and runs on 2x consumer GPUs. Mistral Large 2 is the best for coding. Qwen 2.5 wins on cost-efficiency and context length. Gemma 3 is great for Google-integrated stacks. See also: Best LLMs for Coding and Fine-Tuning Open Source LLMs.