Best LLMs for JSON & Structured Output in July 2026
Models with verified structured-output support, ranked by measured quality, for pipelines where a malformed response is a production incident.
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
- 59.9
- Context
- 1M
- $ in / out per 1M
- $10 / $50
The ranking
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 154 qualifying models with measured benchmark evidence.
| # | Model | Quality index | Intelligence Index | Context | $ in / out per 1M |
|---|---|---|---|---|---|
| 1 | anthropic | 100 | 59.9 | 1M | $10 / $50 |
| 2 | openai | 99.4 | 58.9 | 1.1M | $5 / $30 |
| 3 | anthropic | 98.8 | 55.7 | 1M | $5 / $25 |
| 4 | openai | 98.2 | 55.0 | 1.1M | $2.5 / $15 |
| 5 | openai | 97.6 | 54.8 | 1.1M | $5 / $30 |
| 6 | anthropic | 97.1 | 53.5 | 1M | $5 / $25 |
| 7 | anthropic | 96.5 | 53.4 | 1M | $2 / $10 |
| 8 | openai | 95.9 | 51.4 | 1.1M | $2.5 / $15 |
| 9 | openai | 95.3 | 51.2 | 1.1M | $1 / $6 |
| 10 | zai | 94.7 | 51.1 | 1.0M | $0.93 / $3 |
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
This page exists because model choice is a per-task decision, and the honest answer changes as benchmarks and prices move. Routing makes that decision per request instead.
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.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferbase.ai/api/v1/inference",
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.
Related rankings
The same evidence-first method, applied to other tasks.
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