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
101

Grok 4.20

xAI
Text & chatVisionReasoning
$1.25 / $2.52M tokensAPI only2.5View
102

Qwen3 VL 32B Instruct

Qwen
Text & chatVisionReasoning
$0.104 / $0.416131.1K tokensAPI only0.416View
103

MiniMax M2.5

MiniMax
Text & chatReasoningTool calling
$0.27 / $1.08204.8K tokensAPI only1.08View
104

Qwen3 VL 235B A22B Instruct

Qwen
Text & chatVisionReasoning
$0.21 / $1.9262.1K tokensAPI only1.9View
105

GPT-5.6 Terra Pro

OpenAI
Text & chatVisionReasoning
$2 / $121.05M tokensAPI only12View
106

GPT-5.1

OpenAI
Text & chatVisionReasoning
$1.25 / $10400K tokensAPI only10View
107

Qwen3.6 Plus

Qwen
Text & chatVisionVideo
$0.325 / $1.951M tokensAPI only1.95View
108

Claude Opus 4.5

Anthropic
Text & chatVisionReasoning
$5 / $25200K tokensAPI only25View
109

GLM 4.7 Flash

Z.ai
Text & chatVisionStructured JSON output
$0.0605 / $0.4202.8K tokensAPI only0.4View
110

Qwen3 VL 8B Instruct

Qwen
Text & chatVisionReasoning
$0.117 / $0.455262.1K tokensAPI only0.455View
111

GLM 4.6

Z.ai
Text & chatStructured JSON outputPrompt caching
$0.43 / $1.75204.8K tokensAPI only1.75View
112

Qwen3.6 Flash

Qwen
Text & chatVisionVideo
$0.1875 / $1.1251M tokensAPI only1.125View
113

Qwen3.5-27B

Qwen
Text & chatVisionVideo
$0.195 / $1.56262.1K tokensAPI only1.56View
114

Qwen3 Next 80B A3B Instruct

Qwen
Text & chatReasoningAdjustable thinking
$0.09 / $1.1262.1K tokensAPI only1.1View
115

Qwen3 VL 30B A3B Instruct

Qwen
Text & chatVisionReasoning
$0.15 / $0.6262.1K tokensAPI only0.6View
116

gpt-oss-safeguard-20b

OpenAI
Text & chatStructured JSON outputCode execution
$0.075 / $0.3131.1K tokensAPI only0.3View
117

GLM 5V Turbo

Z.ai
Text & chatVisionVideo
$1.2 / $4202.8K tokensAPI only4View
118

Nano Banana 2 (Gemini 3.1 Flash Image)

Google
VisionAdjustable thinkingImage generation
$0.5 / $3131.1K tokensAPI only3View
119

Qwen3 Max

Qwen
Text & chatReasoningAdjustable thinking
$0.78 / $3.9262.1K tokensAPI only3.9View
120

Qwen3 Coder Flash

Qwen
Text & chatVisionCoding
$0.195 / $0.9751M tokensAPI only0.975View

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.

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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