command-a-03-2025 is a advanced-tier language model from Cohere with 111B parameters. command-a-03-2025 is a proprietary model available exclusively through Cohere's API. Command is Cohere's enterprise-focused model family, with strong retrieval-augmented generation (RAG) performance and native tool use — widely used in enterprise search and document Q&A applications.
command-a-03-2025 is priced at $2.5/1M input tokens and $10/1M output tokens. Output tokens are 4× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, command-a-03-2025 costs approximately $7.50 per 1,000 API calls. Cohere does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $2.5/1M input rate.
Its 256K-token context window is in the top tier for production models, enabling full-document analysis, long multi-turn conversations, and large codebase understanding without chunking. Median latency (P50 time-to-first-token) is approximately 600ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 110 tokens/second — adequate for streaming at comfortable reading speed. Maximum output length is 8K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.
command-a-03-2025 is best suited to chat, coding, reasoning. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how command-a-03-2025 compares to similar models across these dimensions.
command-a-03-2025 sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. Unlike open-weights alternatives, command-a-03-2025 is only available via Cohere's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing command-a-03-2025 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (72/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.
Composite quality score based on reasoning, coding, and instruction-following benchmarks.
Input price vs 184 advanced models tracked. Avg: $1.38/1M.
| Model ID | command-a-03-2025 |
| Provider | Cohere |
| Model Family | Command A |
| Tier | Advanced |
| Context Window | 256,000 tokens (256K) |
| Max Output Tokens | 8,192 tokens |
| Input Price | $2.5 / 1M tokens |
| Output Price | $10 / 1M tokens |
| Cached Input Price | Not available |
| Throughput | 110 tokens/sec |
| Latency (P50) | 600ms |
| Intelligence Score | 72/100 |
| LiveBench Score | — |
| Cost per Task (LiveBench) | — |
| Data Confidence | — |
| HLE Score | — |
| GPQA Diamond | — |
| SWE-bench Score | — |
| OSWorld Score | — |
| BrowseComp Score | — |
| Terminal-Bench Score | — |
| AutoBench Score | — |
| Parameters | 111B |
| Vision Support | No |
| Function Calling | Yes |
| Streaming | Yes |
| Reasoning Model | No |
| Open Weights | No |
| Data Source | Static (verified) |
| Last Updated | 2026-07-01 |
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Self-hosting command-a-03-2025? These GPUs are well-matched for this model's compute and memory requirements.