of DeepSeek V4.1 Flash 1017's structured outputs fail on Phala.
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.
| # | Model | Broken structured outputs | Rank | Best for this model | Structured outputs a day |
|---|---|---|---|---|---|
| 1 | Qwen3.5 397B A17B | 0.83% | 2 of 7 | AtlasCloud (fp8) 0.65% | 5.3k |
| 2 | Qwen3.8 27B | 0.86% | 4 of 16 | NEAR AI (fp8) 0.25% | 26k |
| 3 | GLM 5.3 Flashnvfp4 | 1.38% | 18 of 29 | Parasail (fp4) 0.28% | 31k |
| 4 | DeepSeek V3.2 | 1.39% | 3 of 5 | DeepInfra (fp4) 0.67% | 6.3k |
| 5 | gpt-oss-120b | 2.47% | 14 of 18 | MARA 0.76% | 8.7k |
| 6 | Qwen3.6 35B A3B | 2.90% | 7 of 8 | Parasail (fp8) 0.09% | 13k |
| 7 | Kimi K3 | 3.14% | 16 of 20 | Parasail (fp4) 0.20% | 5.5k |
| 8 | DeepSeek V4 Flash 0731 | 6.14% | 17 of 18 | AtlasCloud (fp4) 0.16% | 16k |
| 9 | DeepSeek V4.1 Flash 1017 | 13.29% | 32 of 33 | Reka AI 0.40% | 7.2k |
And which provider to use.
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.
of DeepSeek V4.1 Flash 1017's structured outputs fail on Phala.
on Qwen3.5 397B A17B, its best model.
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↵