Pixtral Large API Pricing

Mistral has deprecated this model. The price is kept here for reference; check the provider's documentation before building on it.

Mistral · released Nov 1, 2024 · 128K context · API model ID pixtral-large-latest

Pixtral Large by Mistral API pricing: $2.00 per 1M input tokens and $6.00 per 1M output tokens (provider list price via models.dev, Oct 10, 2026).

Prices

Pixtral Large prices, USD per 1M tokens
PriceUSD per 1M tokens
Input$2.00
Output$6.00
Cache readNot published
Cache writeNot published

Mistral's list price for its own API, as compiled by models.dev and checked Oct 10, 2026, 09:00 UTC. Prices on cloud platforms and resellers can differ. Provider documentation

Estimated monthly cost

$1,500

per month

per day
$50.00
per request
$0.005

Cost = input tokens × input price + output tokens × output price, 30 days a month. Cached input uses the cache read price. Cache write fees are not included.

Compare this workload across models

Monthly cost by workload

WorkloadPer month
Chatbot1,000 in / 500 out tokens, 10,000 requests a day$1,500/mo
RAG8,000 in / 500 out tokens, 5,000 requests a day$2,850/mo
Coding agent50,000 in / 2,000 out tokens, 1,000 requests a day, 80% cached input$3,360/mo

Context and capabilities

Context
128,000 tokens
Max output
128,000 tokens
Input
text, image
Output
text
Knowledge cutoff
November 2024
Open weights
Yes
Tool calling
Yes
Reasoning
No
Structured outputs
No

Capabilities and limits as listed by models.dev.

Similarly priced models

Price history

No price changes since we started tracking on Oct 8, 2026.

Questions

How much does Pixtral Large cost per month?
For a chatbot handling 10,000 requests a day with 1,000 input and 500 output tokens each, Pixtral Large costs about $1,500 per month at Mistral's list prices ($2.00 per 1M input tokens, $6.00 per 1M output tokens).
Does Pixtral Large support prompt caching?
No cache read price is published for Pixtral Large.
How long is Pixtral Large's context window?
Pixtral Large accepts up to 128,000 tokens (128K) of context and returns up to 128,000 tokens per response.