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Updated October 31, 2026 · How we measure

Structured-output index

The same model breaks your pipeline's structured outputs 15× more often on some inference providers than on others. Find the best one for your pipelines.

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9 models · 119k structured outputs a day
#ModelBroken
structured outputs
RankBest for
this model
Structured outputs
a day
1Qwen3.5 397B A17B0.83%2 of 7AtlasCloud (fp8) 0.65%5.3k
2Qwen3.8 27B0.86%4 of 16NEAR AI (fp8) 0.25%26k
3GLM 5.3 Flashnvfp41.38%18 of 29Parasail (fp4) 0.28%31k
4DeepSeek V3.21.39%3 of 5DeepInfra (fp4) 0.67%6.3k
5gpt-oss-120b2.47%14 of 18MARA 0.76%8.7k
6Qwen3.6 35B A3B2.90%7 of 8Parasail (fp8) 0.09%13k
7Kimi K33.14%16 of 20Parasail (fp4) 0.20%5.5k
8DeepSeek V4 Flash 07316.14%17 of 18AtlasCloud (fp4) 0.16%16k
9DeepSeek V4.1 Flash 101713.29%32 of 33Reka AI 0.40%7.2k
New models

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And which provider to use.

For inference providers

Structured outputs don't have to break

The same provider can handle structured outputs well on one model and struggle on the next. Implementations diverge on strings, optionals, composition and conditionals (the April announcement): the schema one provider enforces, another ignores.

Worst model13.29%

of DeepSeek V4.1 Flash 1017's structured outputs fail on Phala.

Best model0.83%

on Qwen3.5 397B A17B, its best model.

Ignored on3 of 9

models, where the settings tell OpenRouter to skip Phala.

dotjson plugs into your inference stack, so every model you serve gets perfect structured outputs from day 0.

Fix Phala's structured outputs↵