Use case
Best model for structured output
Long-read guideChoose a model that reliably returns valid JSON, schema-constrained responses, and predictable formats for agent tooling and workflow automation.
Best model for structured output
Choose a model that reliably returns valid JSON, schema-constrained responses, and predictable formats for agent tooling and workflow automation.
Why this guide works
- Prioritize schema adherence over raw intelligence
- Test with your actual JSON schemas and edge cases
- Look for native structured output support, not just prompting
Shortlist
These models excel at returning structured, parseable output for production tooling.
OpenAI
GPT-5.4
OpenAI
OpenAI's GPT-5.4, the most capable and efficient frontier model for professional work. First general-purpose model with native computer-use capabilities. Combines industry-leading coding from GPT-5.3-Codex with improved agentic workflows.
- Context
- 1,000,000
- Input
- $0.005/1K tok
- Output
- $0.02/1K tok
- Action
- Compare-ready
Anthropic
Claude Sonnet 4.6
Claude 4.6
Anthropic's current Sonnet tier for fast frontier reasoning, coding, and long-context agent work.
- Context
- 1,000,000
- Input
- $0.003/1K tok
- Output
- $0.02/1K tok
- Action
- Compare-ready
Google DeepMind
Gemini 3.1 Pro
Gemini 3.1
Google's Gemini 3.1 Pro, designed for complex tasks where simple answers aren't enough. Released Feb 2026 with enhanced reasoning and multimodal capabilities.
- Context
- 1,048,576
- Input
- $0.0013/1K tok
- Output
- $0.01/1K tok
- Action
- Compare-ready
DeepSeek
DeepSeek-V3
DeepSeek
DeepSeek's MoE flagship models (671B total, 37B activated params) with 128K context, strong on math, code, and reasoning benchmarks.
- Context
- 128,000
- Input
- N/A
- Output
- N/A
- Action
- Compare-ready
Decision table
Match the model to your structured output requirements and schema complexity.
| Need | Why it fits | Model |
|---|---|---|
| Complex nested schemas | Best when your schemas are deeply nested with conditional fields and validation rules. | GPT-5.4OpenAI |
| Strict schema enforcement | Best when you need guaranteed valid output without fallback parsing or repair loops. | Claude Sonnet 4.6Anthropic |
| High-volume structured extraction | Best when you need fast, reliable structured output at scale with multimodal input. | Gemini 3.1 ProGoogle DeepMind |
| Open-weight structured output | Best when you need self-hosted structured output generation without API dependency. | DeepSeek-V3DeepSeek |
Evaluation framework
Structured output quality depends on schema adherence, not just general intelligence.
Test with your actual schemas
Use your production JSON schemas with real data, not toy examples. Test edge cases and optional fields.
Measure parse rate
Track what percentage of outputs parse successfully without repair. Aim for 99%+ for production.
Check native support
Models with native structured output (response_format: json_schema) are more reliable than prompted approaches.
Test with adversarial inputs
Verify the model handles ambiguous, missing, or conflicting information without producing invalid output.
Common scenarios
Structured output needs vary by application architecture.
API integration
Use a model with native JSON mode that guarantees valid output for direct API consumption.
Data extraction pipeline
Use a model that handles complex nested schemas reliably for automated data extraction at scale.
Agent tool calling
Use a model with strong function calling that returns properly formatted tool invocations.
Methodology
This guide prioritizes schema adherence and parse reliability over general capability.
We test with real production schemas, not synthetic examples.
We measure parse rates and error recovery quality.
We evaluate native structured output support vs. prompt-based approaches.
Next step
Choose the structured output model for your pipeline
Compare models on schema adherence, parse reliability, and integration fit.