Codestral 2508 model profile
Evidence snapshot:
Codestral 2508 is a Mistral AI model profile with a 256,000-token context window. The dated catalog snapshot records text, file modalities and interfaces for file input, prompt caching, structured output, text generation, tool calling.
Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Blog Post
Codestral 2508 is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Codestral 2508 is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
Codestral 2508 accepts text and file and returns text. The snapshot records file input, prompt caching, structured output, text generation, and tool calling as capabilities or interfaces.
The dated reference snapshot lists $0.3 input and $0.9 output per million tokens. Cached input is $0.03 per million tokens.
The 2026-09-06 snapshot includes 6 Design Arena categories. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Model research profile
- Provider or publisher
- Mistral AI
- Context window
- 256,000 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- 2025-03-31
- Reference catalog ID
- mistralai/codestral-2508
When to pick Codestral 2508
Codestral 2508 is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
Context, modalities, and identity
Codestral 2508 accepts text and file and returns text. The snapshot records file input, prompt caching, structured output, text generation, and tool calling as capabilities or interfaces.
- Provider or publisher
- Mistral AI
- Context window
- 256,000 tokens
- Maximum output
- 204,800 tokens
- Input modalities
- text and file
- Output modalities
- text
- Knowledge cutoff
- 2025-03-31
Model overview
Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Blog Post
- Reference model ID
- mistralai/codestral-2508
- Canonical version
- mistralai/codestral-2508
- Hugging Face ID
- Not separately documented
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- frequency_penalty, max_tokens, prediction, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p
- file input
- prompt caching
- structured output
- text generation
- tool calling
Dated pricing snapshot
The dated reference snapshot lists $0.3 input and $0.9 output per million tokens. Cached input is $0.03 per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.3 per 1M tokens
- Cached input
- $0.03 per 1M tokens
- Output
- $0.9 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 2 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 2
- Best p50 latency
- No recent metric
- Best p50 throughput
- No recent metric
- Availability with routing
- No recent metric
- Availability without routing
- No recent metric
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| Mistral | unknown | $0.3 / 1M | $0.9 / 1M | $0.03 / 1M | 256,000 tokens | No recent p50 | No recent p50 | 99.97% |
| Mistral | unknown | $0.33 / 1M | $0.99 / 1M | $0.033 / 1M | 256,000 tokens | No recent p50 | No recent p50 | No recent uptime |
Benchmark evidence and limitations
The 2026-09-06 snapshot includes 6 Design Arena categories. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
| Design Arena category | Elo | Win rate | Rank |
|---|---|---|---|
| codecategories | 1024 | 38.5% | #107 |
| dataviz | 1036 | 41.1% | #99 |
| gamedev | 997 | 36.3% | #110 |
| uicomponent | 1039 | 46.7% | #97 |
| website | 1026 | 37.8% | #111 |
| 3d | 1057 | 45.4% | #95 |
Popularity and market context
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
- Third-party catalog rank
- No verified rank
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use Codestral 2508?
Codestral 2508 is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
What is Codestral 2508?
Codestral 2508 is a Mistral AI model profile with a 256,000-token context window. The dated catalog snapshot records text, file modalities and interfaces for file input, prompt caching, structured output, text generation, tool calling.
What context window does Codestral 2508 have?
The dated profile lists 256,000 tokens of context and up to 204,800 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for Codestral 2508?
The 2026-09-06 snapshot includes 6 Design Arena categories. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Is Codestral 2508 available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent Codestral 2508's global market share?
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- Codestral 2508 third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)
- Mistral AI official model documentation (primary, checked 2026-09-06)
- Design Arena leaderboard and methodology (benchmark, checked 2026-09-06)