codestral-2
vertex_ai-mistral_models · chat model
codestral-2 is listed here as a chat model from vertex_ai-mistral_models. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Quick read
Best for
Use this page when you need a fast view of cost, context size, and supported features before testing the model in your own workload.
Things to verify
Always check the provider page for discounts, cache pricing, region rules, and any model limits that may not appear in public metadata.
Pricing
| Item | Price |
|---|---|
| Input | $0.3000 / 1M tokens |
| Output | $0.9000 / 1M tokens |
| Embedding | $0.3000 / 1M tokens |
Token limits
Capabilities
| Capability | Supported |
|---|---|
| Vision | — |
| Function calling | ✅ |
| Parallel function calling | — |
| Tool choice | ✅ |
| Prompt caching | — |
| Reasoning | — |
| Response schema | — |
| System messages | — |
| Audio input | — |
| Audio output | — |
| Web search | — |
| PDF input | — |
| Video input | — |
Benchmarks
Most benchmark rows are attached to the base model family rather than this provider route. Open benchmark explorer
| Benchmark | Score | Metric | Scope | Checked | Source |
|---|---|---|---|---|---|
| Codestral 25.01 overview table | 81.1% | HumanEval | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 78.2% | MBPP | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 51.3% | CruxEval | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 31.5% | LiveCodeBench | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 34.0% | RepoBench | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 63.5% | Spider | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 50.5% | CanItEdit | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 65.6% | HumanEval (average) | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Codestral 25.01 overview table | 82.1% | HumanEvalFIM (average) | Base model: Codestral (Codestral-2405) | 2026-05-31 | Link |
| Aider Polyglot | 11.1% | percent correct | Base model: Codestral (Codestral 25.01) | 2026-05-31 | Link |
Sources
| Source links | |
| Pricing data | LiteLLM model cost map |
| Synced at | 2026-05-28 |
Similar models
This list is ranked by overall similarity. Use filters to emphasize the lens that matters most for the replacement you are making.
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| codestral-2 vertex_ai-mistral_models | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| codestral-2@001 vertex_ai-mistral_models | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Same provider Overall 60% |
| codestral-2 vertex_ai-mistral_models | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Same provider Overall 60% |
| codestral-2@001 vertex_ai-mistral_models | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Same provider Overall 60% |
| DeepSeek-V3-0324 deepinfra | In $0.2500 / 1M tokens Out $0.8800 / 1M tokens | text
Output: text | Function callingTool choice | 163.8K | Partial I/O overlap Overall 55% |
| grok-3-mini vercel_ai_gateway | In $0.3000 / 1M tokens Out $0.5000 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Partial I/O overlap Overall 55% |
| DeepSeek-V3.1-Terminus deepinfra | In $0.2700 / 1M tokens Out $1.0000 / 1M tokens | text
Output: text | Function callingTool choice | 163.8K | Partial I/O overlap Overall 55% |
| codestral-2501 vertex_ai-mistral_models | In $0.2000 / 1M tokens Out $0.6000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Same provider Overall 53% |
| codestral@2405 vertex_ai-mistral_models | In $0.2000 / 1M tokens Out $0.6000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Same provider Overall 53% |
| codestral@latest vertex_ai-mistral_models | In $0.2000 / 1M tokens Out $0.6000 / 1M tokens |
Output: unknown | Function callingTool choice | 128.0K | Same provider Overall 53% |
| codestral vercel_ai_gateway | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens | text
Output: text | Function callingTool choice | 4.0K | Partial I/O overlap Overall 45% |
| codestral-2508 Mistral | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens | text
Output: text | Function callingTool choice | 256.0K | Partial I/O overlap Overall 44% |
| Llama-3.3-70B-Instruct azure_ai | In $0.7100 / 1M tokens Out $0.7100 / 1M tokens | text
Output: text | Function callingTool choice | 2.0K | Partial I/O overlap Overall 38% |
| gpt-3.5-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 37% |
| gpt-35-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 37% |
| glm-4.6v novita | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens | imagetext
Output: text | Function callingTool choice | 32.8K | Partial I/O overlap Overall 31% |
| gpt-35-turbo-16k-0613 Azure | In $3.0000 / 1M tokens Out $4.0000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 29% |
| gpt-4 Azure | In $30.0000 / 1M tokens Out $60.0000 / 1M tokens | imagetext
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 26% |
| gpt-4-0613 Azure | In $30.0000 / 1M tokens Out $60.0000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 26% |
No models match this filter.