Best LLM for JSON / Structured Output

Ranked on JSON-mode reliability, schema-adherence, and price. Failures here tax the rest of your pipeline.

Updated April 2026. Top 3 this month: GPT-5, Gemini 2 Pro, Claude Opus 4.7.

How we rank

Structured outputs — JSON, XML, YAML — look simple and are not. Models that are strong at prose can still fail to emit valid JSON under pressure. We weight JSON-mode support and schema adherence, then price; for agentic pipelines JSON reliability is often a bigger efficiency lever than raw reasoning.

Pillars and weights: JSON mode (50%) · schema adherence (30%) · price (20%). Our full methodology is published on the methodology page.

Top ranked models

RankModelProviderInput $/1MOutput $/1MContext
1GPT-5OpenAI$1.25$10.00200,000
2Gemini 2 ProGoogle$3.50$10.502,000,000
3Claude Opus 4.7Anthropic$5.00$25.00200,000
4GPT-5 nanoOpenAI$0.05$0.40400,000
5GPT-4.1 nanoOpenAI$0.10$0.401,000,000
6GPT-4o miniOpenAI$0.15$0.60128,000
7GPT-4.1 miniOpenAI$0.40$1.601,000,000
8o4-miniOpenAI$0.40$1.60200,000
9GPT-3.5 TurboOpenAI$0.50$1.5016,385
10GPT-5 miniOpenAI$0.25$2.00400,000

Tips for json / structured output

  • Always send a schema. Most modern models support a constrained output mode.
  • Validate server-side. Never trust the model to handle `null` vs. `undefined` correctly.
  • If you see repeated schema violations, switch to function-calling rather than free-form JSON.

Frequently asked questions

Which LLM produces the most reliable JSON?

As of April 2026, our weighted top 3 are GPT-5, Gemini 2 Pro, Claude Opus 4.7.

JSON mode vs function calling?

Function calling is stricter and preferred for agent tools. JSON mode is fine for single-shot extraction.

Should I include a schema in the prompt?

Yes — even if your provider supports constrained decoding, an in-prompt schema reduces post-generation errors.

Related tasks

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Data refreshed daily via our snapshot cron. See our public JSON API for programmatic access.