Best LLMs for JSON & Structured Output
When model output feeds a parser instead of a person, conformance is the requirement: a response that is 99 percent valid JSON is 100 percent a failure. This page lists only models whose structured-output support is affirmatively verified, then ranks them by general quality evidence, because within the conformant set you still want the model that gets the content right.
Through Inferbase, every served model here accepts the standard response_format parameter, including json_object mode and json_schema passthrough, on one OpenAI-compatible endpoint.
The top pick
The strongest measured fit for this task right now, taken from the live ranking below.
- Intelligence Index
- 57.6
- Context
- 1M
The ranking
Ordered from live catalog data; the note beneath the table states exactly how. Missing values are shown as missing, never estimated.
| # | Model | Quality index | Intelligence Index | Context |
|---|---|---|---|---|
| 1 | anthropic | 100 | 57.6 | 1M |
| 2 | anthropic | 99.5 | 56.0 | 1M |
| 3 | anthropic | 99 | 53.4 | 1M |
| 4 | google | 98.1 | 52.6 | 1M |
| 5 | anthropic | 97.1 | 50.8 | 1M |
| 6 | anthropic | 96.7 | 49.6 | 1M |
| 7 | openai | 95.7 | 47.0 | 1.1M |
| 8 | xai | 95.2 | 46.4 | 500K |
| 9 | qwen | 94.3 | 45.4 | 1M |
| 10 | zai | 93.8 | 44.8 | 1M |
This list includes only models whose support for this capability is affirmatively verified, from first-party API capability flags or documented evidence; models whose support is unknown are excluded rather than assumed. Within that set, ordering follows the general quality index, a 0-100 percentile among the 183 qualifying models with measured benchmark evidence.
marks models served through the Inferbase API. Missing values are shown as No data rather than estimated.
Also worth evaluating
The next ranks after the top ten, for teams that want a wider shortlist.
Or stop choosing manually
The honest answer changes as benchmarks and prices move. Routing makes the choice per request instead of once.
Send model="auto" to one OpenAI-compatible endpoint and each request is served by the best fit from the same quality and price data behind this ranking, with the decision disclosed per request.
Every routed request records the task it was classified as, the models that qualified, the one that won and why, so a pick made here can be checked against what the router chose in production. A model pinned by name is served as named, with no routing decision.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferbase.ai/v1",
api_key="YOUR_INFERBASE_KEY",
)
# Let routing pick the best model per request,
# or pin any model id from GET /models.
response = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "..."}],
)Frequently asked questions
How this ranking works and how to act on it.
An affirmative capability record from a first-party source that the model supports constrained JSON output, such as a serving API’s json_object or json_schema modes. Models where support is unknown are excluded rather than assumed.
json_object mode guarantees syntactically valid JSON but not your schema. json_schema enforcement constrains the output shape itself. For extraction pipelines, schema enforcement removes a whole class of validation retries; Inferbase passes json_schema through to models that support it.
Constrained decoding can slightly reduce fluency on open-ended tasks, but for the extraction and classification workloads where structured output matters, the effect is negligible compared to the reliability gained. Quality differences between models dwarf the constraint effect.
Usually the cheapest verified model whose quality index clears your accuracy bar, which this table lets you find directly. Extraction rarely needs frontier reasoning; it needs conformance and consistency at volume.
Send the standard OpenAI response_format parameter on the chat completions endpoint, with a pinned model or a custom pool. The request shape is identical to using a provider directly, so existing SDK code ports with a base URL change.
Structured output is a model returning data in a shape your code can parse rather than prose. JSON mode guarantees the response is valid JSON; schema enforcement goes further and constrains the response to a JSON schema you supply, so fields, types and enums are honoured. The first prevents parse errors; the second prevents shape errors.
The open-weight entries here: each is listed because its structured-output support is affirmatively verified. Whether a given provider honours a full schema or only JSON mode depends on the serving stack, so the model page shows the capability’s source, and the Try now button opens the playground on a served model where you can test your schema.
Validate it anyway. Send the schema with the request where the model supports it, then parse and validate the response against the same schema in your code, and retry with the validation error in the prompt on failure. Structured-output support removes most malformed responses; it does not remove the need for a parser that refuses the rest.
Related rankings
The same evidence-first method, applied to other tasks.
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