Mistral: Mistral Large 4 API Pricing
Mistral: Mistral Large 4 costs $1.36 per 1M input tokens and $4.18 per 1M output tokens (cache reads: $0.140). Context window: 1.0M. Prices verified 2026-10-21 · source
Alternatives to consider
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Rebuilt daily from our snapshot archive — the chart appears once a model has 5+ snapshots of history.
From our own daily snapshot archive — independent of provider announcements.
Intelligence measures reasoning/knowledge/coding benchmarks. A high score with a low blended price = value pick; check the value chart to see where this model lands.
Token economics
| Token type | Price | vs base input |
|---|---|---|
| Standard input | $1.36 | — |
| Cache read | $0.140 | −90% |
| Output | $4.18 | — |
Prompt caching stores repeated context server-side: reads cost far less, but writes carry a one-time premium.
Higher effort = deeper thinking = more reasoning tokens billed as output.
What would Mistral: Mistral Large 4 cost you per month?
| Workload | Requests/mo | Avg tokens (in/out) | Est. monthly cost |
|---|---|---|---|
| Light chatbot | 10,000 | 800 / 400 | $27.60 |
| Coding agent | 5,000 | 8,000 / 2,000 | $96.20 |
| RAG pipeline | 30,000 | 4,000 / 500 | $225.90 |
| Doc summarizer | 2,000 | 20,000 / 1,000 | $62.76 |
Estimates use list prices without prompt caching. Caching can cut input cost by up to 90% on repeated context.
About Mistral: Mistral Large 4
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...
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Frequently asked questions
How much does Mistral: Mistral Large 4 cost?
Mistral: Mistral Large 4 costs $1.36 per 1M input tokens and $4.18 per 1M output tokens; cached input tokens cost $0.140 per 1M. Verified 2026-10-21 — provider source.
What is the context window?
1.0M tokens, with up to 262K output tokens per response. That's roughly 1311 pages of text in a single request.
Does it support prompt caching?
Yes — cached input is $0.140/1M, about 90% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.
What's a cheaper alternative?
ScaleDown: Summarize saves about 99% on a typical workload with comparable quality.
Official resources
- Mistral: Mistral Large 4 on OpenRouter — live endpoint, pricing source of record.
- Artificial Analysis leaderboard → — independent intelligence and performance rankings.
- Mistral models → — first-party model specifications and limits.
- Shred your current bill — paste a CSV and see if switching saves money.