of GLM 5.3's structured outputs fail on Decart (fp4).
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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| # | Model | Broken structured outputs | Rank | Best for this model | Structured outputs a day |
|---|---|---|---|---|---|
| 1 | GLM 5.2fp4/fast | 1.07% | 6 of 22 | Venice (fp8) 0.23% | 7.3k |
| 2 | DeepSeek V4.1 Flash 1017fp4 | 1.18% | 16 of 33 | Reka AI 0.40% | 149k |
| 3 | DeepSeek V4.1 Flashfp4 | 2.53% | 21 of 28 | DeepInfra (fp8/flex) 0.35% | 325k |
| 4 | Kimi K3mxfp4 | 3.43% | 18 of 20 | Parasail (fp4) 0.20% | 3.9k |
| 5 | GLM 5.3fp4 | 3.47% | 15 of 28 | Together 0.25% | 26k |
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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.
on GLM 5.2, its best model.
models, where the settings tell OpenRouter to skip Decart.
dotjson plugs into your inference stack, so every model you serve gets perfect structured outputs from day 0.
Fix Decart's structured outputs↵