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Mistral Large 2 in April 2026
Mistral Large 2 with 123 billion parameters represents the strongest offering from the European AI company Mistral AI. Licensed under Apache 2.0 with full commercial use rights, it appeals to teams that want a capable large model without the licensing complexity of Meta’s community license. This April 2026 performance report captures real-world data from GigaGPU dedicated servers.
Mistral Large 2 positions itself between LLaMA 3.1 70B (smaller, faster) and DeepSeek V3 (larger, higher quality), offering strong instruction following and multilingual capabilities. See the full LLM rankings for comparative positioning.
Throughput Benchmarks by GPU
Tested via vLLM at 10 concurrent users:
| GPU Configuration | Precision | Total tok/s | First Token | VRAM Used |
|---|---|---|---|---|
| 2x RTX 5090 | Q4 (AWQ) | 55 | 180 ms | 38 GB |
| 1x RTX 5090 | Q4 (AWQ) | 42 | 225 ms | 22 GB* |
| 1x RTX 6000 Pro | Q4 (AWQ) | 38 | 245 ms | 38 GB |
| 1x RTX 6000 Pro 96 GB | FP16 | 68 | 135 ms | 72 GB |
| 2x RTX 5090 | FP16 | 62 | 155 ms | 46 GB |
*Single RTX 5090 requires aggressive quantisation with some KV cache offloading, which reduces throughput. Dual GPUs provide a more comfortable deployment. At 123B parameters, Mistral Large 2 is notably larger than LLaMA 70B, requiring more VRAM for equivalent quality settings.
Quality Benchmark Scores
| Benchmark | Mistral Large 2 | LLaMA 3.1 70B | Qwen 2.5 72B |
|---|---|---|---|
| MMLU | 84.2 | 82.0 | 85.8 |
| HumanEval | 76.1 | 72.5 | 79.4 |
| MT-Bench | 8.6 | 8.4 | 8.7 |
| IFEval | 85.5 | 82.1 | 83.8 |
Mistral Large 2 scores above LLaMA 3.1 70B on most benchmarks, with particularly strong instruction following (IFEval). However, Qwen 2.5 72B matches or exceeds it on academic benchmarks with a smaller model that is easier to deploy.
Multilingual Performance
Mistral Large 2 was designed with European languages as a priority. It performs notably well on French, German, Spanish, and Italian text compared to models primarily trained on English data. For teams serving multilingual European audiences, this is a differentiating factor.
For Asian language support (Chinese, Japanese, Korean), Qwen 2.5 is generally stronger. For English-only deployments, LLaMA 3.1 70B offers similar quality at lower resource cost. The best open source LLMs guide covers language-specific recommendations.
Deployment Configurations
| Configuration | Hardware | Use Case |
|---|---|---|
| Budget deployment | 1x RTX 5090, Q4 | Low concurrency, European languages |
| Production | 2x RTX 5090, Q4 | Multi-user serving |
| High quality | RTX 6000 Pro 96 GB, FP16 | Maximum accuracy |
Deploy via vLLM for production serving with continuous batching. The model loads in 35-50 seconds on NVMe storage. For quick testing, Ollama supports Mistral Large 2 in GGUF format.
Deploy Mistral Large 2 on Dedicated Hardware
Apache 2.0 licensed, strong European language support, and full commercial use rights on your own GPU server.
Browse GPU ServersPerformance Verdict
Mistral Large 2 is the right choice for teams that need strong European language capabilities, fully permissive Apache 2.0 licensing, and quality above LLaMA 3.1 70B. Its 123B parameter count means higher resource requirements, so teams should verify that the quality uplift justifies the additional hardware cost for their specific use case.
For English-only deployments where resource efficiency matters more, LLaMA 3.1 70B offers better value. For maximum quality, DeepSeek V3 surpasses Mistral Large 2 on most benchmarks. Use the cost per million tokens calculator and the throughput benchmark to model the economics for your workload.
