GPT-5 nano vs Mistral Small
GPT-5 nano is cheaper for every workload modelled here.
List prices checked
Specifications and rates
| Property | GPT-5 nano | Mistral Small |
|---|---|---|
| Input per 1M | $0.050 | $0.100 |
| Output per 1M | $0.400 | $0.300 |
| Cached input per 1M | $0.0050 | — |
| Blended (3:1) | $0.138 | $0.150 |
| Context window | 400K | 131K |
| Max output | 128K | — |
| Inputs accepted | text, image | text, image |
Source: OpenAI pricing (May 2026) · Mistral AI 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 | GPT-5 nano | Mistral Small | Cheaper |
|---|---|---|---|
| In-app chat assistantA support or help assistant handling a few thousand conversations a month, with short prompts and short answers. | $3.00 | $3.50 | GPT-5 nanosaves $0.50/mo (14%) |
| RAG over documentsRetrieval-augmented answering where each request stuffs several retrieved passages into the prompt, so input dominates. | $21 | $35 | GPT-5 nanosaves $14/mo (39%) |
| Coding agentAn agentic loop that reads files and writes patches, generating heavy output and re-sending large context on every turn. | $60 | $70 | GPT-5 nanosaves $10/mo (14%) |
What each one is for
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.
Mistral Small
by Mistral AI
An open-weight model available both as a hosted API and as a download, which makes it the cheap tier you can also run yourself if the economics change.
Common questions
- Is GPT-5 nano or Mistral Small 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 Mistral Small's $0.150.
- Which has the larger context window?
- GPT-5 nano, at 400K tokens against 131K. 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?
- Mistral Small, at $0.300 per million output tokens against $0.400. 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 Mistral Small to GPT-5 nano worth it?
- On price alone it is about 8% 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.