能力 · 2026-05-12
支持结构化输出的 AI 模型
对比支持 JSON mode / 结构化输出的 AI 模型 —— 数据抽取、分类与结构化摘要等管道更稳。
这是什么？
- 结构化输出（也称 JSON mode 或 response_format=json_schema）将模型约束为你提供的 schema 所匹配的 JSON 文档。
- 不同于提示里写「请用 JSON 回复」，结构化输出在解码阶段强制约束 —— 模型无法输出非法 JSON。
为什么重要
- 可避免 JSON 解析错误和「好的，这是 JSON：…」这类越狱前缀。
- 对任何把 LLM 输出接到类型化系统的流程都至关重要：抽取、分类、结构化摘要等。
206 个模型支持此能力
显示前 60 / 共 206 项。 用 完整目录 进一步筛选。
Frequently asked questions
How many AI models support 结构化输出?
206 canonical models in our database currently support 结构化输出. The list is regenerated on every data refresh, so it always reflects the latest model releases from models.dev.
What is the cheapest model with 结构化输出?
Voxtral Small 24B 2507 from Mistral is currently the lowest-priced option, at $0.002 per 1M input tokens and $0.002 per 1M output tokens. The full table above is sorted price-ascending.
Which model with 结构化输出 has the largest context window?
GPT-5.4 (OpenAI) leads on context at 1.05M tokens. This may matter if you also need long-document understanding alongside 结构化输出.
Which models are available on the most providers?
Production-readiness usually correlates with how many independent providers host the same weights. The top three by provider count are: Kimi K2.5 (45), GPT OSS 120B (33), GLM-5.1 (33).
How is 结构化输出 different from a regular LLM?
Structured output (a.k.a. JSON mode / response_format=json_schema) constrains the model at decode time so it cannot emit invalid JSON. This is stricter than just prompting 'reply in JSON' and removes a whole class of parsing errors.
How often is this list updated?
Daily. Our data pipeline pulls models.dev once a day, regenerates the canonical model list, and rebuilds these pages so newly released models appear within 24 hours.
Explore more
Top models with this capability
- Voxtral Small 24B 2507$0.00 in / $0.00 out
- dots.ocr$0.01 in / $0.01 out
- Hermes 4 14B$0.01 in / $0.05 out
- Sao10k L3 8B Lunaris $0.05 in / $0.05 out
- Gemma 3 12B$0.03 in / $0.10 out
Other capabilities
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Pricing comparisons
最近更新：
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.