Codestral API pricing 2508
Published on-demand rates for Mistral's mistral/codestral-2508, captured 2026-08-10 from public pricing data and committed to our archive. Context window 256k tokens; first observed in our archive 2025-12-15.
| Meter | USD per 1M tokens |
|---|---|
| Input | $0.300 |
| Output | $0.900 |
| Cached inputno published cache-read rate; the whole input bills at the standard rate | — |
Standard on-demand rates. Excludes batch endpoints and negotiated pricing; confirm on Mistral's pricing page before committing.
What it costs on a real workload
The four frozen profiles the LLMPrice indices price, per 1M tokens processed, at the rates above. Where no cache-read rate is published, the whole input bills at the standard rate.
| Profile | In / out mix | Cached share | Cost |
|---|---|---|---|
| Retrieval | 800k / 200k | 70% | $0.420 |
| Chat | 500k / 500k | 30% | $0.600 |
| Content | 200k / 800k | 20% | $0.780 |
| Agent | 900k / 100k | 85% | $0.360 |
Profile definitions are frozen and published on the methodology page. Compare every model on these profiles →
Observed rate history
No repricing observed since this model entered our archive on 2025-12-15. When a rate moves, the change appears here and on the sitewide ticker.
Priced near this one
The models closest to Codestral on a retrieval workload, cheaper and more expensive alike. We publish no quality score, so this is what they cost and nothing more.
| Model | Provider | Input | Output | vs Codestral |
|---|---|---|---|---|
| Gemini 3.1 Flash Lite | $0.250 | $1.50 | 11% less | |
| GPT 5 Mini | OpenAI | $0.250 | $2.00 | 13% more |
| Grok Code Fast | xAI | $0.200 | $1.50 | 14% less |
| GPT 5.4 Nano | OpenAI | $0.200 | $1.25 | 26% less |
Compared on the retrieval profile: 800k input, 200k output, 70% of input cached where the model offers a cache rate.
No affiliate or referral arrangements: the console link carries no parameters and nothing on this page is paid placement. Codestral is a trademark of its owner; LLMPrice.io is independent and unaffiliated.