Structured JSON output AI models: parse replies without a regex

Structured JSON output
Services found: 151
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
141

GPT-5 Image Mini

OpenAI
VisionImage generationStructured JSON output
$2.5 / $2400K tokensAPI only2View
142

GPT-5.4 Pro

OpenAI
Text & chatVisionReasoning
$30 / $1801.05M tokensAPI only180View
143

Mistral Large 3

Mistral AI
Text & chatVisionReasoning
$0.5 / $1.5256K tokensAPI only1.5View
144

MiMo-V2.6-Pro

Xiaomi
Text & chatVisionAudio
$0.435 / $0.871M tokensAPI only0.87View
145

MiMo-V2.6-Flash

Xiaomi
Text & chatVisionAudio
$0.14 / $0.281M tokensAPI only0.28View
146

MiMo-V2.6-Pro-UltraSpeed

Xiaomi
Text & chatVisionAudio
$4.35 / $8.71M tokensAPI only8.7View
147

Fugu Max

Sakana
Text & chatReasoningAdjustable thinking
$2 / $6API only6View
148

Solar Mini 4

Upstage
Text & chatReasoningAdjustable thinking
$0.1 / $0.4512K tokensAPI only0.4View
149

Perceptron Mk1.5

Perceptron
Text & chatVisionAudio
$0.15 / $1.536.9K tokensAPI only1.5View
150

Schematron V2 Turbo

Inference.net
Text & chatStructured JSON outputPrompt caching
$0.03 / $0.15125K tokensAPI only0.15View
151

Schematron V2 Small

Inference.net
Text & chatStructured JSON outputPrompt caching
$0.05 / $0.23125K tokensAPI only0.23View

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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.

Shape, not truthA schema guarantees parseable output; it says nothing about whether the values are right.
Fewer retriesConstrained decoding removes the cleanup layer most teams write by hand.
Near universalEvery major vendor implements it, so price and context decide rather than availability.

Models with this capability

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

Enormous schemasFifty fields in one call is slower and less accurate than two focused calls.
No fallback pathEven constrained models fail occasionally. Handle it rather than assume success.

Who this suits

Data pipelinesTurning documents and messages into rows without a parsing layer.
BackendsAny place a model output feeds directly into typed code.
AnalyticsConsistent categorisation across large volumes of free text.

Frequently asked questions

Does a schema guarantee correct answers?

No — only correct shape. A model can return a perfectly valid object full of wrong values.

Which vendors support it?

Effectively all the major ones: OpenAI, Anthropic, Google, Mistral, Alibaba, DeepSeek, Z.ai, Moonshot and MiniMax.

Is it slower than plain text output?

Marginally, and the trade is almost always worth it: no retries, no regex, no defensive parsing.

Does it slow generation down?

Marginally. The saving in retries and cleanup outweighs it in nearly every case.

Can I enforce enums?

Yes — constraining a field to a fixed set is the most reliable way to keep classification stable.

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