of gpt-oss-20b's structured outputs fail on AkashML (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 | Qwen3.6 35B A3Bfp8 | 0.18% | 2 of 8 | Parasail (fp8) 0.09% | 476k |
| 2 | Gemma 4 26B A4Bfp4 | 0.30% | 2 of 11 | Prime Intellect (bf16) 0.23% | 473k |
| 3 | Llama 3.3 70B Instructfp8 | 0.67% | 3 of 5 | SambaNova Turbo 0.16% | 185k |
| 4 | Qwen3.8 27Bfp8 | 0.82% | 3 of 16 | NEAR AI (fp8) 0.25% | 72k |
| 5 | gpt-oss-120bbf16 | 0.90% | 2 of 18 | MARA 0.76% | 293k |
| 6 | GLM 5.3fp8 | 1.87% | 10 of 28 | Together 0.25% | 5.5k |
| 7 | Kimi K3fp4 | 1.98% | 10 of 20 | Parasail (fp4) 0.20% | 1.1k |
| 8 | gpt-oss-20bfp4 | 5.24% | 4 of 8 | CoreWeave (fp4) 1.17% | 76k |
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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 Qwen3.6 35B A3B, its best model.
models, where the settings tell OpenRouter to skip AkashML.
dotjson plugs into your inference stack, so every model you serve gets perfect structured outputs from day 0.
Fix AkashML's structured outputs↵