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
81

Qwen3 VL Flash

Alibaba
Text & chatVisionReasoning
$0.05 / $0.4256K tokensAPI only0.4View
82

GLM 5.2

Z.ai
Text & chatReasoningTool calling
$0.966 / $3.0361.05M tokensAPI only3.036View
83

DeepSeek V3.2

DeepSeek
Text & chatStructured JSON outputPrompt caching
$0.269 / $0.4163.8K tokensAPI only0.4View
84

GPT-5.6 Luna Pro

OpenAI
Text & chatVisionReasoning
$0.2 / $1.21.05M tokensAPI only1.2View
85

Gemma 4 26B A4B

Google
Text & chatVisionVideo
$0.07 / $0.34262.1K tokensAPI only0.34View
86

Qwen3.7 Flash

Qwen
Text & chatVisionVideo
$0.03 / $0.131M tokensAPI only0.13View
87

Qwen3.8 Flash

Qwen
Text & chatVisionVideo
$0.15 / $0.471M tokensAPI only0.47View
88

Kimi K2.6

Moonshot AI
Text & chatVisionReasoning
$0.95 / $4262.1K tokensAPI only4View
89

Claude Opus 4.6

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
90

GPT-5.6 Sol Pro

OpenAI
Text & chatVisionReasoning
$2 / $101.05M tokensAPI only10View
91

GPT-6 Astra Pro

OpenAI
Text & chatVisionReasoning
$10 / $501.05M tokensAPI only50View
92

Claude Opus 4.7

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
93

Qwen3.5-Flash

Qwen
Text & chatVisionVideo
$0.065 / $0.261M tokensAPI only0.26View
94

Kimi K2.5

Moonshot AI
Text & chatVisionReasoning
$0.45 / $2.25262.1K tokensAPI only2.25View
95

Qwen3.6 35B A3B

Qwen
Text & chatVisionVideo
$0.1 / $0.9262.1K tokensAPI only0.9View
96

GLM 4.7

Z.ai
Text & chatStructured JSON outputPrompt caching
$0.4 / $1.75204.8K tokensAPI only1.75View
97

GPT-5.2

OpenAI
Text & chatVisionReasoning
$1.75 / $14400K tokensAPI only14View
98

Qwen3.5-35B-A3B

Qwen
Text & chatVisionVideo
$0.3125 / $1.25262.1K tokensAPI only1.25View
99

GLM 5

Z.ai
Text & chatReasoningTool calling
$0.6 / $1.92204.8K tokensAPI only1.92View
100

Qwen3.5-9B

Qwen
Text & chatVisionVideo
$0.1 / $0.15262.1K tokensAPI only0.15View

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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