# Mistral Large 3: pricing, crowd verdict, pros and cons (2026)

> Open-weight 675B MoE flagship (41B active) with 256K context, vision input, and $0.50/$1.50 per 1M pricing.

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/tool/mistral-large-3
Arena: llm-models · Crowd score: 50% recommend (0 votes, Bayesian-smoothed) · Price: $1.50/1M out

Mistral AI's open-weight flagship released December 2, 2025: a sparse mixture-of-experts model with 675B total and 41B active parameters, 256K context, and text plus image input under Apache 2.0. Priced at $0.50/$1.50 per 1M tokens on La Plateforme, it debuted #2 among open-source non-reasoning models on LMArena.

## Key facts

- provider: Mistral AI
- contextWindow: 256K tokens
- priceIn: $0.50/1M in
- priceOut: $1.50/1M out
- modalities: text, vision (image input), text output
- openWeights: yes

## Ratings (editorial, 1-5)

- reasoning: 3/5
- coding: 3/5
- writing: 4/5
- speed: 3.5/5
- valueForMoney: 4/5

## Pros

- Apache 2.0 open weights with single-node deployment via FP8/NVFP4 quantization, despite 675B total parameters
- 256K context window, at the upper end for open-weight models, well suited to long-document RAG
- Aggressive flagship pricing at $0.50 in / $1.50 out per 1M tokens, roughly 3-4x cheaper than Western proprietary flagships
- Debuted #2 among open-source non-reasoning models on LMArena (Elo ~1418)
- Native multimodality (2.5B-parameter vision encoder) and 40+ native languages
- Developers on HN praise its strict formatting and instruction following plus production reliability

## Cons

- Weak deep reasoning: GPQA Diamond ~44% vs high-70s for DeepSeek V3.2 and Kimi K2 Thinking; no reasoning variant at launch
- Trails GLM-4.6, Kimi K2 and DeepSeek on modern coding benchmarks (middling LiveCodeBench v6); HN devs place it a 'different weight class' below Gemini 3, GPT-5.1 and Claude Opus 4.5
- Hallucination-prone on factual QA (SimpleQA ~24%) with weak abstention tuning
- Measured output speed ~49 tok/s on Artificial Analysis, below the ~58 tok/s median for comparable models
- HN criticism that the architecture closely mirrors DeepSeek V3, raising doubts about original R&D

## Best for

- multilingual RAG
- long-document analysis
- EU data sovereignty and self-hosting
- cost-efficient production pipelines

## Verdict

Choose Mistral Large 3 if you want an open-weight, EU-governed flagship for multilingual RAG, long-document work, or self-hosted deployments: versus Mistral Large 2 (dense 123B, restrictive research license, $2/$6 API pricing) it is a clear upgrade in context, multimodality, licensing, and cost. Avoid it as your primary coding or deep-reasoning engine; DeepSeek V3.2, GLM-4.6, or proprietary frontier models score materially higher there. Treat it as a cheap, reliable workhorse rather than a frontier performer.

## Pricing note

Single API tier on La Plateforme ($0.50 in / $1.50 out per 1M tokens), with a 50% discount via the batch API; Apache 2.0 weights allow free self-hosting, and third-party host pricing may differ.

## Alternatives and head-to-heads

- [Claude Haiku 4.5](https://glad-ia-tor.com/tool/claude-haiku-4-5) (crowd score 67%) · [Mistral Large 3 vs Claude Haiku 4.5](https://glad-ia-tor.com/vs/mistral-large-3-vs-claude-haiku-4-5)
- [Claude Fable 5](https://glad-ia-tor.com/tool/claude-fable-5) (crowd score 63%) · [Mistral Large 3 vs Claude Fable 5](https://glad-ia-tor.com/vs/mistral-large-3-vs-claude-fable-5)
- [Claude Opus 4.7](https://glad-ia-tor.com/tool/claude-opus-4-7) (crowd score 57%) · [Mistral Large 3 vs Claude Opus 4.7](https://glad-ia-tor.com/vs/mistral-large-3-vs-claude-opus-4-7)
- [Claude Sonnet 5](https://glad-ia-tor.com/tool/claude-sonnet-5) (crowd score 57%) · [Mistral Large 3 vs Claude Sonnet 5](https://glad-ia-tor.com/vs/mistral-large-3-vs-claude-sonnet-5)

Full ranking: https://glad-ia-tor.com/hall-of-fame/llm-models

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