BenchLeader
Mistral AIAuto-detected

Codestral 2508

Codestral 2508 is a Mistral AI proprietary model. It is not yet ranked: 1 independent result so far, and the index needs at least five across two categories. At $0.450 per million tokens blended it is cheaper than most ranked models. Output speed of 37 tokens per second puts it in the slowest quarter, with a first token in 0.3 s.

Blended price
$0.450/M
$0.300 in · $0.900 out
Output speed
37 tok/s
OpenRouter traffic, 7-day median; not yet measured by Artificial Analysis
First answer
0.29 s
Context
128k
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Reasoning32

Versions

Mistral AI has shipped 2 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
Codestral 2405
Codestral 2508this page

Benchmark results

One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

BenchmarkdefaultSource
Kagi LLM Benchmark32.5%#122Kagi LLM Benchmark

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Mistral37 tok/s0.29 s$0.300$0.900256k

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.00048.4 s
Summarise a 30-page report12,000 / 600$0.004116.5 s
Code edit6,000 / 1,500$0.003240.8 s
Agentic coding session60,000 / 4,000$0.0221.8 min
Structured extraction2,000 / 200$0.00085.7 s

See also

Data as of 10 Sept 2026. Compare with another model.