PRICE~moonshotai/kimi-latest cached_input_per_mtok decreased from 0.80 to 0.291mPRICE~moonshotai/kimi-latest output_per_mtok decreased from 13.00 to 11.361mPRICE~moonshotai/kimi-latest input_per_mtok decreased from 0.99 to 0.751mPRICE~deepseek/deepseek-pro-latest cached_input_per_mtok increased from 0.0042 to 0.101mPRICE~deepseek/deepseek-pro-latest output_per_mtok increased from 0.40 to 4.201mPRICE~deepseek/deepseek-pro-latest input_per_mtok decreased from 0.13 to 0.131mPRICE~moonshotai/kimi-latest cached_input_per_mtok increased from 0.29 to 0.8032mPRICE~moonshotai/kimi-latest output_per_mtok increased from 11.36 to 13.0032mPRICE~moonshotai/kimi-latest input_per_mtok increased from 0.75 to 0.9932mPERFORMANCEGLM-5.2 → Scaleway: TTFT ↑ 20%1hPERFORMANCEDeepSeek V4 Flash → Scaleway: TTFT ↑ 35%1hPERFORMANCEMistral Medium 3.5 → Scaleway: TTFT ↓ 21%1hPERFORMANCEDeepSeek V4 Flash → Scaleway: throughput ↓ 20%1hPERFORMANCEGPT-OSS 120B → Scaleway: throughput ↑ 31%1hPERFORMANCEGPT-OSS 120B → Scaleway: TTFT ↓ 24%1hPERFORMANCEGPT-OSS 120B → Together AI: throughput ↑ 20%1hPERFORMANCEKimi K3 via OpenRouter: TTFT ↓ 49%1hPERFORMANCEGLM-5.2 via OpenRouter: reliability recovered 96.8% → 100.0%1hPERFORMANCEKimi K3 via Cortecs: throughput ↑ 184%1hPERFORMANCEKimi K3 via Cortecs: TTFT ↓ 42%1hPERFORMANCEGLM-5.2 via OpenRouter: throughput ↓ 49%1hPERFORMANCEGLM-5.2 via OpenRouter: TTFT ↑ 121%1hPERFORMANCEMiniMax M3 via OpenRouter: reliability recovered 96.8% → 100.0%1hPERFORMANCEDeepSeek V4 Pro via Cortecs: throughput ↑ 27%1hPERFORMANCEGPT-OSS 120B via OpenRouter: throughput ↓ 22%1hPERFORMANCEGLM-4.7 via OpenRouter: TTFT ↓ 78%1hPERFORMANCEGPT-OSS 20B → Groq: TTFT ↑ 24%1hPERFORMANCEKimi K3 → Together AI: TTFT ↓ 34%1hPERFORMANCEDeepSeek V4 Flash via OpenRouter: throughput ↓ 17%1hPERFORMANCELlama 3.3 70B via Cortecs: TTFT ↓ 51%1hPERFORMANCEQwen3 235B via OpenRouter: throughput ↑ 37%1hPERFORMANCEQwen3 235B via OpenRouter: TTFT ↓ 29%1hPERFORMANCEDeepSeek V4 Flash → Together AI: throughput ↑ 19%1hPERFORMANCEGLM-5.3 via OpenRouter: throughput ↓ 17%1hPERFORMANCEGPT-OSS 20B via Cortecs: throughput ↑ 28%1hPERFORMANCELlama 3.3 70B → Together AI: TTFT ↓ 33%1hPRICE~moonshotai/kimi-latest cached_input_per_mtok decreased from 0.80 to 0.291hPRICE~moonshotai/kimi-latest output_per_mtok decreased from 13.00 to 11.361hPRICE~moonshotai/kimi-latest input_per_mtok decreased from 0.99 to 0.751hPRICE~deepseek/deepseek-v4-flash-latest cached_input_per_mtok decreased from 0.0077 to 0.00111h
ENGINEERING STUDY

Structured Output: Trust, but Validate

Schema-conformance rates across measured providers, one mechanical validator, no partial credit.

Key findings
  • Measured structured-output validity spans 91.7%–100.0% across providers.
  • Cerebras leads; Mistral trails — a pipeline parsing its output re-requests or crashes on the difference.

Conformance, measured

Each structured-output case demands schema-valid JSON and validates the result mechanically — no partial credit. The native-schema column records each provider's declared enforcement mode; whether it separates the table is for the measured column to say. Anthropic runs this suite prompt-only by recorded request quirk.

ProviderValidityNative schema mode
Cerebras100.0%no
Groq100.0%yes
Scaleway100.0%yes
OpenAI100.0%yes
Cortecs98.3%yes
Together96.8%yes
OpenRouter96.4%yes
Mistral91.7%yes
Structured-output validity by measured provider · ● MEASURED
What this means for engineers
  • Treat schema validity as an SLO with a measured baseline per provider — and always validate downstream regardless.
Dataset

Figures recompute from the live rolling window on every visit; the snapshot and versions above are the provenance of the regime that produced them. View the current benchmark →

Cite this researchInferenceBench (2026). Structured Output: Trust, but Validate. InferenceBench Research.
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Structured Output: Trust, but Validate · InferenceBench