Cohere Command R vs GPT-5 nano
GPT-5 nano is cheaper for every workload modelled here.
List prices checked
Specifications and rates
| Property | Cohere Command R | GPT-5 nano |
|---|---|---|
| Input per 1M | $0.150 | $0.050 |
| Output per 1M | $0.600 | $0.400 |
| Cached input per 1M | — | $0.0050 |
| Blended (3:1) | $0.262 | $0.138 |
| Context window | 128K | 400K |
| Max output | — | 128K |
| Inputs accepted | text | text, image |
Source: Cohere pricing (May 2026) · OpenAI pricing (May 2026). Published list prices only — committed-use discounts, regional variation, and per-request charges are not included. Not yet independently re-checked against the vendor’s page.
Monthly cost by workload
Identical token volumes through both rate cards. Output-heavy profiles are where the ranking flips.
| Workload | Cohere Command R | GPT-5 nano | Cheaper |
|---|---|---|---|
| In-app chat assistantA support or help assistant handling a few thousand conversations a month, with short prompts and short answers. | $6.00 | $3.00 | GPT-5 nanosaves $3.00/mo (50%) |
| RAG over documentsRetrieval-augmented answering where each request stuffs several retrieved passages into the prompt, so input dominates. | $54 | $21 | GPT-5 nanosaves $33/mo (61%) |
| Coding agentAn agentic loop that reads files and writes patches, generating heavy output and re-sending large context on every turn. | $120 | $60 | GPT-5 nanosaves $60/mo (50%) |
What each one is for
Cohere Command R
by Cohere
The cheap end of Cohere's range, aimed at retrieval pipelines where the model's job is to ground an answer in supplied documents rather than to reason from scratch.
GPT-5 nano
by OpenAI
The cheapest model OpenAI publishes, intended for classification, routing, and cleanup passes where the task is simple and the volume is enormous.
Common questions
- Is Cohere Command R or GPT-5 nano cheaper in 2026?
- GPT-5 nano, for every workload modelled here. On a blended 3:1 basis it costs $0.138 per million tokens against Cohere Command R's $0.262.
- Which has the larger context window?
- GPT-5 nano, at 400K tokens against 128K. A larger window only helps if you can afford to fill it — the cost of one full-context request is in the table above.
- Which is cheaper for output-heavy work like agents?
- GPT-5 nano, at $0.400 per million output tokens against $0.600. Reasoning tokens bill as output, so for agentic loops this axis usually decides the invoice regardless of what the input rates look like.
- What is not included in these figures?
- Batch-processing discounts, fine-tuning, long-context surcharges above a provider's standard tier, and any enterprise agreement. These are published pay-as-you-go list prices, checked against each provider's own pricing page on the date shown.
- Is switching from Cohere Command R to GPT-5 nano worth it?
- On price alone it is about 48% cheaper on a blended basis. Whether that survives contact with your workload depends on output quality and on retry rate — a cheaper model that needs two attempts is not cheaper. Model the specific volumes in the calculator before you migrate.