benchmarks/inference/best llm api for json mode
JSON mode · exact structured objects · measured Sep 3, 2026

Best LLM API for JSON mode output: exact objects, by provider

What the benchmark found. Fireworks AI, Modal, and Telnyx tied for the task-success lead at 100.0%. The leading score represents approximately 200 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Modal 0.0%; Telnyx 0.0%. The table is based on 200 ten-field contract extractions per provider and requires an exact typed object. Measured across 10 LLM inference providers serving GLM 5.3 Flash, 200 requests each, Sep 3, 2026.

Providers measured. Baseten, DeepInfra, Fireworks AI, Modal, Nebius, Novita AI, Parasail, Telnyx, Together AI, and Z.AI, each serving GLM 5.3 Flash behind an OpenAI-compatible chat-completions endpoint.

What this page compares. Every provider accepted the same JSON-object response mode. This page measures what came back: whether the 200 contract-extraction answers per provider were complete, correctly typed JSON objects, not whether the endpoint advertises JSON mode.

How to read the result. Lead with exact task success when the object feeds another system. A partial object earns no credit. Failure rate separates delivery problems from formatting misses; end-to-end p99 compares the wait among providers that clear the quality bar.

Workload and configuration. Every provider received the same 600 deliberately easy structured tasks: 200 meeting-note lookups, 200 ticket-triage classifications, and 200 contract-term extractions. GLM 5.3 Flash ran with streaming enabled, reasoning low, temperature 0, top-p 1, a 256-token output ceiling, one attempt, a 20-second timeout, and per-provider concurrency one.

Task-specific table. The table below uses only the published contract-terms-extraction aggregate rather than the three-family pooled summary. Each provider therefore contributes 200 submitted requests to this view.

APIs used. The runner called each provider's OpenAI-compatible chat-completions surface with the model identifiers below; the links open the provider documentation used to verify each adapter.

[01] measured comparison

Which LLM API returns exact JSON objects most reliably?

Rows use only contract-term extraction aggregates: exact successes out of 200 complete ten-field schemas, with failure rate and end-to-end p99.

benchmarks/inference/best-llm-api-for-json-modereviewed run
LLM inference providers sorted by exact task success rate
Provider / APITask successExact successesFailure rateEnd-to-end p99
Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗100.0%200 / 2000.0%11.9 s
Modalzai-org/GLM-5.3-Flash ↗100.0%200 / 2000.0%3.37 s
Telnyxzai-org/GLM-5.3-Flash ↗100.0%200 / 2000.0%3.48 s
Together AIzai-org/GLM-5.3-Flash ↗98.5%197 / 2001.5%6.13 s
Parasailzai-org/GLM-5.3-Flash ↗97.5%195 / 2002.5%12.2 s
Z.AIglm-5.3-flash ↗96.0%192 / 2000.0%8.74 s
Novita AIzai-org/glm-5.3-flash ↗93.0%186 / 2003.0%8.99 s
DeepInfrazai-org/GLM-5.3-Flash ↗92.0%184 / 2008.0%19.8 s
Basetenzai-org/GLM-5.3-Flash ↗90.0%180 / 2000.0%4.55 s
Nebiuszai-org/GLM-5.3-Flash ↗85.5%171 / 20014.5%13.8 s

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.
[02] workflow fit

Where does JSON mode reliability decide the provider?

  • Document to record. Extracted fields written to a database or CRM must be complete and typed; exact-object success is the usable yield.
  • Tool arguments. Deterministic code accepts or rejects the object; a wrong enum or date is a wrong action.
  • Validation cost. Every rejected object creates a repair or retry path; fewer rejections is cheaper than a faster call.
[03] how it was measured

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 ↗.
[04] faq

JSON mode questions

Which LLM API returned the most exact JSON objects?

Fireworks AI, Modal, and Telnyx tied for the task-success lead at 100.0%. The leading score represents approximately 200 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Modal 0.0%; Telnyx 0.0%. The table is based on 200 ten-field contract extractions per provider and requires an exact typed object.

Does a partially correct JSON object receive credit?

Contract extraction counts only when every required field is present and typed correctly; strings and enums compare exactly, dates use ISO YYYY-MM-DD, and the full object must match. Correct-field diagnostics do not create partial task-success credit.

Why do JSON mode results differ when every provider serves the same model?

Even with the same requested model, delivery layers can differ in truncation, malformed output, model routing, stream completion, and operational reliability. Task success measures the usable result produced by the complete provider path, not model intelligence in isolation.

[05] related comparisons
[06] source benchmark

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 →