Ministral 3 14B vs GPT-5 nano
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Ministral 3 14B | GPT-5 nano |
|---|---|---|
| Provider | Mistral | OpenAI |
| Input Price (1M) | $0.20 | $0.05 |
| Output Price (1M) | $0.20 | $0.40 |
| Context Window | 262,000 | 400,000 |
Verdict
Ministral 3 14B costs $0.20 per 1M input tokens and $0.20 per 1M output tokens. GPT-5 nano costs $0.05 per 1M input tokens and $0.40 per 1M output tokens. GPT-5 nano is 75% cheaper on input tokens than Ministral 3 14B. For output tokens, Ministral 3 14B is the more affordable option at $0.20/1M vs $0.40.
On context window, GPT-5 nano supports 400,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Ministral 3 14B
- ✓ Your workload is output-heavy — Ministral 3 14B generates text cheaper
- ✓ You are already integrated with Mistral
When to choose GPT-5 nano
- ✓ You need the lowest input token cost ($ 0.05/1M)
- ✓ You need a larger context window (400,000 tokens)
- ✓ You are already integrated with OpenAI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Ministral 3 14B cheaper than GPT-5 nano? ▼
GPT-5 nano is cheaper on input tokens at $0.05/1M vs $0.20/1M for Ministral 3 14B — a 75% saving.
What is the context window of Ministral 3 14B vs GPT-5 nano? ▼
Ministral 3 14B has a 262,000-token context window. GPT-5 nano has a 400,000-token context window. GPT-5 nano supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Ministral 3 14B or GPT-5 nano? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GPT-5 nano is the cheaper option. For maximum context length, GPT-5 nano supports 400,000 tokens. Use the comparison table above to find the right fit for your workload.