Mistral Large 4 model profile

Profile facts

Profile type

Model research profile

Provider or publisher

Mistral AI

Context window

1,048,576 tokens

Snapshot date

2026-10-13

Knowledge cutoff

Not disclosed

Reference catalog ID

mistralai/mistral-large-4-0

When to pick Mistral Large 4

Mistral Large 4 is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Each condition below is derived from this model's own values in the 2026-10-13 snapshot, compared only against models publishing the same field.

• Reach for it when a whole corpus has to fit in one prompt. The 1.05M-token context window is in the top quarter of the 167 models publishing a limit here, so long documents can go in whole rather than chunked.
• Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
• Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
• Reach for it when you want to trade depth against cost per call. Reasoning effort is selectable across high, none, so one route can serve both cheap and deep work.
• Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.

Context, modalities, and identity

Mistral Large 4 accepts text and image and returns text. The snapshot records prompt caching, reasoning controls, structured output, text generation, tool calling, and vision as capabilities or interfaces.

Provider or publisher

Mistral AI

Context window

1,048,576 tokens

Maximum output

262,144 tokens

Input modalities

text and image

Output modalities

text

Knowledge cutoff

Not disclosed

Model overview

Mistral Large 4 is a frontier multimodal (text and image input) model from Mistral AI built for reasoning, coding, and agentic workloads. It offers a 1M-token context window with up to 256K output tokens, and supports tool calling and structured outputs.

Reference model ID

mistralai/mistral-large-4-0

Canonical version

mistralai/mistral-large-4-0-20261006

Hugging Face ID

Not separately documented

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters

frequency_penalty, include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, reasoning_effort, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p

• prompt caching
• reasoning controls
• structured output
• text generation
• tool calling
• vision

Dated pricing snapshot

The dated reference snapshot lists $0.68 input and $2.09 output per million tokens. Cached input is $0.07 per million tokens.

These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.

Price date

2026-10-13

Input

$0.68 per 1M tokens

Cached input

$0.07 per 1M tokens

Output

$2.09 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 3 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints

3

Best p50 latency

1.21 s

Best p50 throughput

63 tok/s

Availability with routing

98.67% over the sampled window

Availability without routing

98.23% over the sampled window

Performance date

2026-10-13

Benchmark evidence and limitations

The 2026-10-13 snapshot includes 8 Design Arena categories. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Popularity and market context

Mistral Large 4 ranked #70 in the cited trailing-7-day third-party catalog usage dataset as of 2026-10-13. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Third-party catalog rank

#70

Global market share

Not inferred

Observation date

2026-10-13

Frequently asked questions

Sources and evidence dates

1. Mistral Large 4 third-party catalog record (catalog, checked 2026-10-13)
2. Third-party model catalog methodology (methodology, checked 2026-10-13)
3. Mistral AI official model documentation (primary, checked 2026-10-13)
4. Design Arena leaderboard and methodology (benchmark, checked 2026-10-13)