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mistral-large-4

Mistral AI SAS/🇪🇺 EU/chat50% off

Mistral Large 4 is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 49B active parameters and 1.05T total parameters, and a 1.6B vision encoder. Public preview, 1M context.More
All models →

Which id to call

mistral/mistral-large-4

This exact deployment on Mistral AI SAS, with no routing and no failover. Send it as the model field.

Input /1M

$0.68

$1.36 list

$0.07 cached

Output /1M

$2.09

$4.18 list

3.1x input

Context

524K

tokens

Added

Oct 2026

chat

Capabilities 5/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What mistral-large-4 costs

Provider prices per 1M tokens, updated October 8, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

50% off

This endpoint is discounted. List is $1.36 per 1M input and $4.18 per 1M output, and the rates below are what you pay. The discount applies to every request on this endpoint, with nothing to claim or enter.

Input /1M

$0.68

$1.36 list

Output /1M

$2.09

$4.18 list

Cache write /1M

-

Cache read /1M

$0.07

What a workload costs

100K input + 10K output
$0.0889
1M input + 100K output
$0.89
10M input + 1M output
$8.89

At the rates above, before caching. A cache read costs $0.07 per 1M, so repeated context lands under these figures.

model=

Which id to call

One base url, https://router.requesty.ai/v1, and one key for every id here.

mistral/mistral-large-4

This exact deployment on Mistral AI SAS, with no routing and no failover. Send it as the model field.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to any id on the left. Existing OpenAI SDK code needs no other edit.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="mistral/mistral-large-4",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window524K tokens
Max output-
API typechat
AddedOct 2026
Model id
Data retentionNone
Used for trainingNo
Served from🇪🇺 EU
Privacy policyMistral Privacy Policy →

Released 2026-10-06

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

Intelligence Indexreasoning
38.4%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

More from Mistral AI SAS

Newest first, on the same provider and the same key.

glm-5-2

1M ctx/$1.54/1M

leanstral-1-5

262K ctx/free/1M

devstral-latest

256K ctx/$0.44/1M

mistral-medium-3-5

262K ctx/$1.65/1M

mistral-medium-latest

131K ctx/$0.44/1M

mistral-small-latest

256K ctx/$0.17/1M

Reference

mistral-large-4 questions

How much does mistral-large-4 cost?

mistral-large-4 is priced at $0.68 per million input tokens and $2.09 per million output tokens when accessed via Requesty. Those figures include a 50% discount on this endpoint, off a list rate of $1.36 per million input tokens and $4.18 per million output tokens. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of mistral-large-4?

mistral-large-4 has a context window of 524K tokens. That's roughly 699 words of input you can fit in a single prompt.

How does mistral-large-4 perform on benchmarks?

mistral-large-4 scores 54.2% on SciCode, 38.4% on Intelligence Index, 35.0% on Humanity's Last Exam. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.

What can mistral-large-4 do?

mistral-large-4 supports vision input, tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use mistral-large-4 with the OpenAI SDK?

Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "mistral/mistral-large-4". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run mistral-large-4 through Requesty?

Yes. mistral-large-4 runs through Requesty's OpenAI-compatible API, served from Mistral AI SAS. You do not host the model yourself: point base_url at Requesty, set the model to "mistral/mistral-large-4", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call mistral-large-4 through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.

All Mistral AI SAS models