# Mistral Large 4

> Source: https://aiwiki.ai/wiki/mistral_large_4
> Updated: 2026-10-11
> Fact-checked: 2026-10-06
> Categories: AI Models, Large Language Models, Mixture of Experts, Multimodal AI
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "Mistral Large 4." aiwiki.ai, 11 Oct 2026. https://aiwiki.ai/wiki/mistral_large_4
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

Mistral Large 4 is a general-purpose [multimodal model](https://aiwiki.ai/wiki/vision_language_model) developed by [Mistral AI](https://aiwiki.ai/wiki/mistral_ai). It entered public preview on October 6, 2026, with a [mixture-of-experts](https://aiwiki.ai/wiki/mixture_of_experts) architecture and API access. The initial release preceded the planned distribution of its weights.[1] It belongs to the [Mistral Large](https://aiwiki.ai/wiki/mistral_large) family, but its preview specifications and availability should not be confused with those of [Mistral Large 3](https://aiwiki.ai/wiki/mistral_large_3).

## Release and availability

Mistral called the model ML4 and "le Chonk", offered a Studio preview API, and announced weights for the end of October 2026.[2]

On October 12, 2026, the [model card](https://aiwiki.ai/wiki/model_card) still listed weights and their license as forthcoming, not an available checkpoint with published licensing terms.[3]

Mistral's official [Hugging Face](https://aiwiki.ai/wiki/hugging_face) release preview listed October 31, 2026 as the current estimated release date.[17]

Public preview is a distinct stage in Mistral's model lifecycle. Mistral permits silent updates during this stage and does not guarantee that a preview will reach general availability. Results obtained from the preview can therefore describe a changing service rather than a permanently fixed checkpoint.[6]

## Architecture and specifications

The specifications below describe the October 2026 preview.[3]

| Property | Published value |
|---|---|
| Architecture | Granular [mixture of experts](https://aiwiki.ai/wiki/mixture_of_experts)[3] |
| Total parameters | 1.05 trillion in the [model card](https://aiwiki.ai/wiki/model_card)[3] |
| [Vision encoder](https://aiwiki.ai/wiki/vision_encoder) | 1.6 billion parameters[3] |
| Active parameters | 52 billion in the [model card](https://aiwiki.ai/wiki/model_card) and current launch announcement[3][2] |
| Mistral [context window](https://aiwiki.ai/wiki/context_window) | 1 million tokens in the [model card](https://aiwiki.ai/wiki/model_card)[3] |
| [OpenRouter](https://aiwiki.ai/wiki/openrouter) context limit | 512K tokens, displayed as 524K in its summary[4] |
| [OpenRouter](https://aiwiki.ai/wiki/openrouter) maximum output | 256K tokens[4] |
| Input and output | Text and image input; text output on [OpenRouter](https://aiwiki.ai/wiki/openrouter)[4] |

Mistral's official release preview explains the two active-parameter counts: 49B per token, or 52B including embeddings and [output layer](https://aiwiki.ai/wiki/output_layer)s.[17] The model card and current launch article both list 52B.[3][2]

OpenRouter's documented route limit differs from the 1-million-token model specification, so the latter should not be assumed to apply to every serving route.[4]

Mistral reports training on 3,800 NVIDIA Grace Blackwell GPUs in its European [data center](https://aiwiki.ai/wiki/data_center)s, which serve the preview, and training data spanning more than 160 languages.[2]

## API access and pricing

Mistral's Chat Completions example uses `mistral-large-4` with `reasoning_effort="high"`.[3]

[OpenRouter](https://aiwiki.ai/wiki/openrouter) identifies its route as `mistralai/mistral-large-4-0`.[4] These are provider-specific identifiers, not interchangeable request strings.

The official pricing page displayed the following standard-tier API rates on October 12, 2026, with regional inference off, in US dollars per million tokens.[5] Mistral's changelog describes the launch offer as a 50% discount for two weeks.[1]

| Token type | Listed standard rate | Launch sale rate |
|---|---:|---:|
| Input | $1.36 | $0.68[5] |
| Cached input | $0.14 | $0.07[5] |
| Output | $4.18 | $2.09[5] |

These figures describe the launch pricing snapshot, not a permanent discounted rate. Mistral's pricing page separates standard, batch, and priority tiers.[5]

## Tool use and document workflows

The card lists chat completions, [structured output](https://aiwiki.ai/wiki/structured_output)s, function calling, Document QnA, prefix completion, batching, Agents and Conversations, and built-in tools.[3]

[Function calling](https://aiwiki.ai/wiki/function_calling) lets an application provide tool definitions and receive a selected function with generated arguments. In the ordinary Chat Completions workflow, the application executes that function and supplies the result back to the model. Mistral separately offers server-side tools through its Agents and Conversations APIs. Tool-calling support does not mean that an ordinary completion has unrestricted access to an application's systems.[7]

For structured output, Mistral provides JSON mode and custom schemas. Its documentation recommends custom structured outputs when a specific format is required; JSON mode still requires the prompt to request JSON and describe the intended format.[8]

Document QnA combines an OCR stage, which extracts document text and structure, with a [language model](https://aiwiki.ai/wiki/language_model) that answers questions about the extracted content. The documented workflow covers information extraction, summarization, and comparisons across documents.[9]

Agents store a model selection, instructions, tools, and completion settings. Conversations record interactions and can be started with either an agent or a model directly. Mistral's built-in tool types include web search, a code interpreter, image generation, and a document library.[10] In particular, a tool for image generation is a platform capability, not evidence that the model's own output modality includes generated images.

## Evaluations

### Launch results reported by Mistral

The launch announcement reports these preview results.[2]

| Evaluation | Reported result |
|---|---:|
| DeepSWE v1.1 | 61.7%[2] |
| Terminal-Bench 4 | 28.3%[2] |
| Cybench | 93%[2] |
| Lakera B3 attack resistance | 93.3%[2] |

The original [Cybench](https://aiwiki.ai/wiki/cybench) research describes 40 capture-the-flag tasks from four competitions, with command-execution environments and optional subtask guidance. It also studies different agent scaffolds.[15] Its original experiments predate Mistral Large 4; the paper defines the benchmark rather than independently validating this model's launch score.

### Independent evaluator snapshots

[Artificial Analysis](https://aiwiki.ai/wiki/artificial_analysis) listed Mistral Large 4 Preview with an Intelligence Index score of 38 on October 7, 2026. Its page identifies the index version as 4.3.2, combining ten evaluations, and reports 116.1 output [tokens per second](https://aiwiki.ai/wiki/tokens_per_second) for Mistral's API. It labels the preview proprietary because the weights are not publicly available, in contrast to Mistral's description of the planned release as open-weight.[11]

[Vals AI](https://aiwiki.ai/wiki/vals_ai) separately published the following results for Mistral Large 4. The displayed uncertainty values are retained with the scores.[12]

| Vals evaluation | Score |
|---|---:|
| Vals Index | 48.05% +/- 1.11[12] |
| Finance Agent v2 | 54.68% +/- 0.58[12] |
| Harvey's Legal Agent Benchmark | 15.83% +/- 2.96[12] |
| Terminal-Bench 4.0 | 22.73% +/- 0.88[12] |

Vals' methodology uses controlled evaluation harnesses, private [test set](https://aiwiki.ai/wiki/test_set)s for proprietary benchmarks, and published standard errors. For benchmarks with multiple runs, including Terminal-Bench and Finance Agent v2, it calculates uncertainty over run-level scores. Such error bars do not capture every change in prompts or deployment settings.[13] Its Terminal-Bench result is separate from Mistral's reported 28.3%; the two figures should not be collapsed into one score.

Harvey's Legal Agent Benchmark evaluates legal deliverables using file-system tools and skills for documents, presentations, and spreadsheets. Vals reports results on a held-out task set and distinguishes whole-task success from satisfying individual criteria.[14] A task pass rate on this benchmark is not a general measure of legal accuracy or an authorization to rely on generated legal advice.

## Safety evaluation scope

Mistral's launch report discusses resistance to indirect [prompt injection](https://aiwiki.ai/wiki/prompt_injection) and refusals of malicious cybersecurity requests, including tests drawn from [JailbreakBench](https://aiwiki.ai/wiki/jailbreakbench), [StrongREJECT](https://aiwiki.ai/wiki/strongreject), and AgentHarm.[2] These evaluations address specified attack or misuse settings.

[AgentHarm](https://aiwiki.ai/wiki/agentharm) contains 110 malicious agent tasks, expanded to 440 through augmentations, across 11 harm categories. Its research evaluates both refusals and whether an attacked agent can complete a harmful multistep workflow.[16] A refusal measurement and an attack-resistance measurement test different behavior; neither constitutes a guarantee that an agent is safe in every deployment.

## References

1. Mistral AI. ["Changelog," October 6, 2026 entry](https://docs.mistral.ai/resources/changelogs). Accessed October 12, 2026.
2. Mistral AI. ["Introducing Mistral Large 4"](https://mistral.ai/news/mistral-large-4/). October 6, 2026. Accessed October 12, 2026.
3. Mistral AI. ["Mistral Large 4" model card, including Weights and Usage tabs](https://docs.mistral.ai/models/mistral-large-4-0). October 6, 2026. Accessed October 12, 2026.
4. OpenRouter. ["Mistral: Mistral Large 4"](https://openrouter.ai/mistralai/mistral-large-4-0). Accessed October 7, 2026.
5. Mistral AI. ["Pricing"](https://docs.mistral.ai/inference/pricing). Accessed October 12, 2026.
6. Mistral AI. ["Model lifecycle policy"](https://docs.mistral.ai/inference/model-lifecycle). Accessed October 7, 2026.
7. Mistral AI. ["Function Calling"](https://docs.mistral.ai/studio/conversations/function-calling). Accessed October 7, 2026.
8. Mistral AI. ["Structured Outputs"](https://docs.mistral.ai/studio/conversations/structured-output). Accessed October 7, 2026.
9. Mistral AI. ["Document AI QnA"](https://docs.mistral.ai/studio/document-processing/document_qna). Accessed October 7, 2026.
10. Mistral AI. ["Agents & Conversations"](https://docs.mistral.ai/studio/agents/agents-api). Accessed October 7, 2026.
11. Artificial Analysis. ["Mistral Large 4 Preview: Intelligence, Performance & Price Analysis"](https://artificialanalysis.ai/models/mistral-large-4). Accessed October 7, 2026.
12. Vals AI. ["Mistral Large 4 Benchmarks, Cost and Capabilities"](https://www.vals.ai/models/mistralai_mistral-large-4). Accessed October 7, 2026.
13. Vals AI. ["Methodology"](https://www.vals.ai/methodology). Accessed October 7, 2026.
14. Vals AI. ["Harvey's Legal Agent Benchmark"](https://www.vals.ai/benchmarks/hlab). Updated October 6, 2026. Accessed October 7, 2026.
15. Zhang, Andy K., et al. ["Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models"](https://arxiv.org/abs/2408.08926). arXiv:2408.08926, submitted August 15, 2024; revised April 12, 2025. ICLR 2025.
16. Andriushchenko, Maksym, et al. ["AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents"](https://arxiv.org/abs/2410.09024). arXiv:2410.09024, submitted October 11, 2024; revised April 18, 2025. ICLR 2025.
17. Mistral AI. ["Mistral-Large-4-1T-A52B" upcoming release](https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B). Hugging Face. Current ETA October 31, 2026. Accessed October 12, 2026.