Mistral Large 3

Mistral Large 3 is Mistral's open-weight, general-purpose multimodal model, released under Apache 2.0 for chat, document question-answering, and agentic workloads.

input · usd / 1m tok

$0.50

output · usd / 1m tok

$1.50

context window

128ktokens

modalities · in → out

takes text and image in, gives text out

pricing

base api ratesusd per 1m tokens

input
$0.50
output
$1.50

providers

Azure AI Foundry hosts Mistral Large 3. Impossibl routes your request intelligently and handles failover automatically when a provider is down.

  • Azure AI Foundry

specifications

takes text and image in, gives text outmistral/mistral-large-3

  • creatorMistral
  • model typeText generation
  • context window128,000 tokens
  • maximum outputnot published
  • input

    takes text and image in, gives text out

  • outputtext
  • tool callingnot verified
  • releaseddec 2, 2025
  • endpoint/v1/chat/completions

unknown means the public catalog does not establish that fact. a model's documented capabilities can differ from what a particular api supports. see the model documentation for integration details.

try it · your prompt, this model, live

# responses API
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["IMPOSSIBL_API_KEY"],
    base_url="https://api.impossibl.com/v1",
)

resp = client.responses.create(
    model="mistral/mistral-large-3",
    input="",
)
print(resp.output_text)

edit the prompt, then run

benchmarks

intelligence index

9.7#81 of 90 models on the gateway

  1. coding index20.1#55 of 60
  2. agentic index2.4#48 of 49
  3. omniscience-39.6#73 of 86
  4. gpqa68.0%#76 of 88
  5. humanity’s last exam4.2%#83 of 89
  6. terminal-bench v2.112.0%#52 of 57
  1. GPQA68.0%
  2. MMMU-Pro55.7%
  3. LiveCodeBench46.5%
  4. AIME 202538.0%
  5. SciCode36.6%
  6. IFBench36.2%
  7. AA-LCR36.0%
  8. Tau224.6%
  9. Terminal-Bench Hard15.9%
  10. Terminal-Bench v2.112.0%
  11. Tau Banking5.8%
  12. Humanity’s Last Exam4.2%
  13. AutomationBench Partial1.5%
  14. GDP.pdf All-pass1.2%
  15. CritPt0.0%
  16. Terminal-Bench v4.00.0%
  • Intelligence Index9.71
  • Omniscience Index-39.57
  • GDPval Elo588.39
  • coding index (API)20.1
  • agentic index (API)2.4
  • intelligence index (API)9.7

view source ↗all source data and breakdowns (json) ↗

each configuration is listed separately; these evaluations describe the tested model configuration, and performance and costs can differ on impossibl. unmeasured scores are omitted and api values keep their source units. last checked 2026-09-11.

compare Mistral Large 3 with other models

frontier labs and open weights, best-ranked firstusd / 1m tok · in / out

impossibl models --all →

frequently asked questions

What is Mistral Large 3?

Mistral Large 3 is Mistral's open-weight, general-purpose multimodal model, released under Apache 2.0 for chat, document question-answering, and agentic workloads. It uses a granular mixture-of-experts design and supports function calling, structured outputs, built-in agent tools, and batch processing.

How much does Mistral Large 3 cost?

Mistral Large 3 costs $0.50 per million input tokens and $1.50 per million output tokens through the Impossibl API. All rates are in USD.

view the pricing table ↑

What is the context length of Mistral Large 3?

Mistral Large 3 has a 128,000-token context window through the Impossibl API.

What inputs and outputs does Mistral Large 3 support?

Mistral Large 3 accepts text and image as input. Mistral Large 3 returns text.

How do I use Mistral Large 3 through an API?

Mistral Large 3 is available through the Impossibl API. Use mistral/mistral-large-3 as the model identifier with /v1/chat/completions. The quickstart explains how to create an API key and send your first request.

follow the api quickstart →

When was Mistral Large 3 released?

Mistral released Mistral Large 3 on December 2, 2025.

this page describes Mistral Large 3 as listed by the impossibl public api. prices and availability come from that catalog, refreshed here every five minutes. this is an impossibl offering, not a comparison of independent hosting-provider quotes. model descriptions and release dates, where available, use the creator sources linked under the answers above.

view the source catalog ↗model as markdown ↗report a correction →