Most reliable LLM API for structured output and JSON schema extraction

Which LLM API returns exact, schema-valid structured output most reliably?

The rows use only contract-term extraction aggregates. Exact successes are out of 200 complete schemas; end-to-end tail latency and operational failures remain visible without copying the pooled leaderboard.

Provider by provider, in the order above

• Fireworks AI: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 11.91 seconds.
• Modal: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 3.37 seconds.
• Telnyx: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 3.48 seconds.
• Together AI: 98.5% task success (197 of 200 exact), 1.5% failures, end-to-end p99 6.13 seconds.
• Parasail: 97.5% task success (195 of 200 exact), 2.5% failures, end-to-end p99 12.18 seconds.
• Z.AI: 96.0% task success (192 of 200 exact), 0.0% failures, end-to-end p99 8.74 seconds.
• Novita AI: 93.0% task success (186 of 200 exact), 3.0% failures, end-to-end p99 8.99 seconds.
• DeepInfra: 92.0% task success (184 of 200 exact), 8.0% failures, end-to-end p99 19.84 seconds.
• Baseten: 90.0% task success (180 of 200 exact), 0.0% failures, end-to-end p99 4.55 seconds.
• Nebius: 85.5% task success (171 of 200 exact), 14.5% failures, end-to-end p99 13.80 seconds.

Which structured-output workflows does the schema represent?

• Contract metadata extraction. Extract effective dates, renewal terms, payment terms, notice periods, and termination rights into typed fields.
• JSON tool arguments. Require every key and type before passing model output to deterministic application code.
• Document-to-record automation. Treat exact-object success as the usable yield when a response populates a database or review form.

How is LLM inference provider latency and accuracy measured here?

• Same model. Every endpoint serves GLM 5.3 Flash; the provider layer is the only variable.
• Same request. One-turn, non-tool chat completion in JSON-object mode, streaming on, temperature 0, top-p 1, reasoning low, 256-token ceiling, 20-second timeout, concurrency one.
• Client clock, pinned tokenizer. Time to first token, end-to-end latency and tokens per second come from the client; visible answer tokens are counted with a pinned tiktoken o200k_base tokenizer, never provider-reported usage.
• Exact task success. An answer counts only when it is schema-valid and exactly correct under the task rubric. Timeouts, HTTP errors and malformed responses stay in the denominator.
• Serverless endpoints only. Dedicated capacity is outside the comparison. Runner and per-request records: openbenchmarks-labs/inference ↗.

Structured output and JSON schema questions

Same run, other questions

• LLM inference provider benchmark: complete scorecard and methodology →
• Same model, different accuracy: LLM inference provider endpoint accuracy →
• Most reliable LLM API provider: failure rate, errors, timeouts and usable answers →
• Best LLM inference provider for RAG answer extraction →
• Best LLM inference provider: speed, accuracy and failures compared →
• Fireworks AI vs Together AI →
• Fastest LLM inference provider: time to first token, tokens per second and end-to-end latency →

Read the complete LLM inference provider benchmark

Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the LLM Inference Provider Benchmark →