GPT-5 vs Mistral Large
The cheaper model depends on how much output you generate: GPT-5 wins 1 of 3 workloads, Mistral Large takes the rest.
List prices checked
Specifications and rates
| Property | GPT-5 | Mistral Large |
|---|---|---|
| Input per 1M | $1.25 | $2.00 |
| Output per 1M | $10.00 | $6.00 |
| Cached input per 1M | $0.125 | — |
| Blended (3:1) | $3.44 | $3.00 |
| Context window | 400K | 131K |
| Max output | 128K | — |
| Inputs accepted | text, image | text |
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 | Mistral Large | Cheaper |
|---|---|---|---|
| In-app chat assistantA support or help assistant handling a few thousand conversations a month, with short prompts and short answers. | $75 | $70 | Mistral Largesaves $5.00/mo (7%) |
| RAG over documentsRetrieval-augmented answering where each request stuffs several retrieved passages into the prompt, so input dominates. | $525 | $690 | GPT-5saves $165/mo (24%) |
| Coding agentAn agentic loop that reads files and writes patches, generating heavy output and re-sending large context on every turn. | $1,500 | $1,400 | Mistral Largesaves $100/mo (7%) |
What each one is for
GPT-5
by OpenAI
OpenAI's flagship, priced aggressively below the other frontier models on input and with a 90% cached-input discount that makes long system prompts nearly free to reuse.
Mistral Large
by Mistral AI
The French lab's flagship, priced below every US frontier model and hosted in the EU, which is often the deciding factor rather than the benchmark scores.
Common questions
- Is GPT-5 or Mistral Large cheaper in 2026?
- It depends on the shape of the workload. GPT-5 is cheaper for 1 of the 3 profiles below and Mistral Large for the rest — the split follows how much output each workload generates.
- Which has the larger context window?
- GPT-5, 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 Large, at $6.00 per million output tokens against $10.00. 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 GPT-5 to Mistral Large worth it?
- On price alone it is about 13% 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.