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
121

GLM 5 Turbo

Z.ai
Text & chatReasoningTool calling
$1.2 / $4202.8K tokensAPI only4View
122

Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image)

Google
VisionAdjustable thinkingImage generation
$0.25 / $1.565.5K tokensAPI only1.5View
123

Kimi K2 Thinking

Moonshot AI
Text & chatReasoningTool calling
$0.6 / $2.5262.1K tokensAPI only2.5View
124

GPT-5.2-Codex

OpenAI
Text & chatVisionCoding
$1.75 / $14400K tokensAPI only14View
125

Qwen3.5 Plus 2026-02-15

Qwen
Text & chatVisionVideo
$0.26 / $1.561M tokensAPI only1.56View
126

Qwen3.5 Plus 2026-04-20

Qwen
Text & chatVisionVideo
$0.3 / $1.81M tokensAPI only1.8View
127

Qwen3 Coder Plus

Qwen
Text & chatCodingReasoning
$0.65 / $3.251M tokensAPI only3.25View
128

GPT-5.1-Codex-Mini

OpenAI
Text & chatVisionCoding
$0.25 / $2400K tokensAPI only2View
129

GPT-5.4 Image 2

OpenAI
VisionImage generationStructured JSON output
$8 / $15272K tokensAPI only15View
130

Nano Banana (Gemini 2.5 Flash Image)

Google
VisionImage generationWeb search grounding
$0.3 / $2.532.8K tokensAPI only2.5View
131

GLM 4.6V

Z.ai
Text & chatVisionVideo
$0.3 / $0.9131.1K tokensAPI only0.9View
132

GPT-5.1-Codex

OpenAI
Text & chatVisionCoding
$1.25 / $10400K tokensAPI only10View
133

MiniMax M2.1

MiniMax
Text & chatReasoningTool calling
$0.3 / $1.2204.8K tokensAPI only1.2View
134

Nano Banana Pro (Gemini 3 Pro Image)

Google
VisionAdjustable thinkingImage generation
$2 / $12131.1K tokensAPI only12View
135

MiniMax M2-her

MiniMax
Text & chatReasoningTool calling
$0.3 / $1.265.5K tokensAPI only1.2View
136

MiniMax M2

MiniMax
Text & chatReasoningTool calling
$0.255 / $1.02204.8K tokensAPI only1.02View
137

GPT-5.5 Pro

OpenAI
Text & chatVisionReasoning
$30 / $1801.05M tokensAPI only180View
138

GPT-5.1-Codex-Max

OpenAI
Text & chatVisionCoding
$1.25 / $10400K tokensAPI only10View
139

Qwen3 Max Thinking

Qwen
Text & chatReasoningAdjustable thinking
$0.78 / $3.9262.1K tokensAPI only3.9View
140

GPT Audio Mini

OpenAI
Text & chatAudioStructured JSON output
$0.6 / $2.4128K tokensAPI only2.4View

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.

Frequently searched

Ratings 0

Avg. score 0

Rate this page

Help us improve