Structured JSON output AI models: parse replies without a regex
| # | AI model | Capabilities | API $/1M (in / out) | Context window | Price / mo | Output $/1M | Overview | Visit site |
|---|---|---|---|---|---|---|---|---|
| 141 | GPT-5 Image Mini OpenAI | $2.5 / $2 | 400K tokens | API only | 2 | View | ||
| 142 | GPT-5.4 Pro OpenAI | $30 / $180 | 1.05M tokens | API only | 180 | View | ||
| 143 | Mistral Large 3 Mistral AI | $0.5 / $1.5 | 256K tokens | API only | 1.5 | View | ||
| 144 | MiMo-V2.6-Pro Xiaomi | $0.435 / $0.87 | 1M tokens | API only | 0.87 | View | ||
| 145 | MiMo-V2.6-Flash Xiaomi | $0.14 / $0.28 | 1M tokens | API only | 0.28 | View | ||
| 146 | MiMo-V2.6-Pro-UltraSpeed Xiaomi | $4.35 / $8.7 | 1M tokens | API only | 8.7 | View | ||
| 147 | Fugu Max Sakana | $2 / $6 | API only | 6 | View | |||
| 148 | Solar Mini 4 Upstage | $0.1 / $0.4 | 512K tokens | API only | 0.4 | View | ||
| 149 | Perceptron Mk1.5 Perceptron | $0.15 / $1.5 | 36.9K tokens | API only | 1.5 | View | ||
| 150 | Schematron V2 Turbo Inference.net | $0.03 / $0.15 | 125K tokens | API only | 0.15 | View | ||
| 151 | Schematron V2 Small Inference.net | $0.05 / $0.23 | 125K tokens | API only | 0.23 | View |
Browse by need
Structured output means the model is constrained to return JSON matching a schema you supply, so your code can parse the response without defensive cleanup, retries or regex. In production this is the difference between a demo and a pipeline.
151 models support it, spanning $0.25 to $50 per million output tokens. The feature is close to universal among major vendors, so the choice comes down to price and context. Note that schema constraints guarantee shape, not correctness: a model can return a perfectly formed object full of nonsense.
Models with this capability
The 151 models below all support this capability. Prices are input and output per 1M tokens from the vendor official price list; for dirhams multiply by 3.6725. Tap a card to open the model page.
How to choose
- Keep schemas shallow — deeply nested structures raise failure rates on smaller models.
- Mark optional fields properly so the model can omit rather than invent.
- Validate values after parsing; shape validation is not business validation.
- Combine with reasoning when extraction requires judgement rather than pattern matching.
Common mistakes
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Frequently asked questions
Does a schema guarantee correct answers?
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Is it slower than plain text output?
Does it slow generation down?
Can I enforce enums?
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