Mistral: Pixtral Large
Pixtral Large is a language model from Mistral released on Nov 1, 2024. It reads text and images and writes text, with a 128K token context window and up to 128K tokens per reply.
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It costs $2 per million input tokens and $6 per million output tokens.
It supports tool calling, and its weights are open, so you can host it yourself.
- Modalities
- In / out price
- $2 / $6 per 1M
- Context
- 128K
- Max output
- 128K
- Released
- Nov 1, 2024
- Knowledge cutoff
- Nov 2024
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
Pixtral Large pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $2 / 1M tokens | |
|---|---|---|
| Output | $6 / 1M tokens |
What Pixtral Large costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call Pixtral Large through an OpenAI compatible API.
- Tools
tools - Temperature
temperature
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Pixtral Large questions
How much does the Pixtral Large API cost?
$2 per million input tokens and $6 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of Pixtral Large?
128,000 tokens of input, and it can write up to 128,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does Pixtral Large support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is Pixtral Large open source?
Its weights are published, so you can run it on your own hardware or a cloud of your choice. Read the license before commercial use.
When was Pixtral Large released?
Nov 1, 2024, with training data up to Nov 2024.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on Pixtral Large?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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