GPT-5 nano vs Mistral Small 3.2
Pricing verdict: GPT-5 nano vs Mistral Small 3.2: GPT-5 nano is cheaper for input-heavy usage ($0.05/M vs $0.10/M input tokens), while GPT-5 nano is better for long-context tasks (128,000 tokens).
Direct answer: choose GPT-5 nano for lower token spend and choose GPT-5 nano when your workload needs longer context.
Compare API pricing, input and output token costs, context windows, and monthly estimates on one page so you can pick the right model fast.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
GPT-5 nano
Mistral Small 3.2
Cost Differences
Both models cost the same at the default workload.
Quick Recommendation
Direct API pricing is a tie at the default workload: both land around $0.75/month. Choose the model that better fits your workflow.
Feature Comparison
| Feature | GPT-5 nano | Mistral Small 3.2 |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Input Price | $0.05/1M tokens | $0.10/1M tokens |
| Output Price | $0.40/1M tokens | $0.30/1M tokens |
| Context Window | 128,000 tokens | 128,000 tokens |
| Max Output | 8,192 tokens | 8,192 tokens |
| Category | efficient | efficient |
| Capabilities | text | textcode |
| Release Date | 8/7/2025 | 12/2/2025 |
GPT-5 nano vs Mistral Small 3.2: Which Should You Choose?
Choosing between GPT-5 nano and Mistral Small 3.2 depends on your priorities: cost efficiency, context length, or raw capability. GPT-5 nano is the more affordable option at $0.05/1M input tokens — 50% cheaper than Mistral Small 3.2.
These models come from different providers — OpenAI and Mistral AI — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with OpenAI, switching to Mistral AIinvolves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
Both models are in the efficient category, making this a direct head-to-head comparison. At scale — say 10,000 requests per day — direct API pricing stays tied, so the real decision is context, latency, and provider fit.
Output costs matter too. GPT-5 nano charges $0.40/1M output tokens vs $0.30 for Mistral Small 3.2. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Mistral Small 3.2 has the edge here at $0.30/1M output tokens.
Best Use Cases
Choose GPT-5 nano when:
- • Budget is a primary concern
- • You're already using OpenAI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Choose Mistral Small 3.2 when:
- • You need more capabilities (code)
- • You're already using Mistral AI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Pros and Caveats at a Glance
GPT-5 nano
- • Input pricing: $0.05/M tokens
- • Output pricing: $0.40/M tokens
- • Context window: 128,000 tokens
- • Max output: 8,192 tokens
Watch out for
- • Higher output cost than Mistral Small 3.2
Mistral Small 3.2
- • Input pricing: $0.10/M tokens
- • Output pricing: $0.30/M tokens
- • Context window: 128,000 tokens
- • Max output: 8,192 tokens
Watch out for
- • Higher input cost than GPT-5 nano
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
GPT-5 nano (OpenAI)
Mistral Small 3.2 (Mistral AI)
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Which is cheaper, GPT-5 nano or Mistral Small 3.2?▼
What is the context window difference between GPT-5 nano and Mistral Small 3.2?▼
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Which model has better overall pricing for heavy usage?▼
Where can I compare OpenAI and Mistral AI API pricing beyond this model matchup?▼
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