Most reliable LLM API provider: failure rate, errors, timeouts and usable answers

Which LLM API provider failed least and returned the most usable answers?

Successes, operational failures, and other unsuccessful results reconcile to the submitted workload. Failure classes are broken out so a 429-heavy endpoint and a timeout-heavy endpoint are not confused. End-to-end p95 is a secondary decision metric.

Provider by provider, in the order above

• Fireworks AI: 99.5% task success (597 of 600 exact), 0.0% failures, end-to-end p99 10.49 seconds.
• Telnyx: 99.5% task success (597 of 600 exact), 0.0% failures, end-to-end p99 3.48 seconds.
• Modal: 99.3% task success (596 of 600 exact), 0.0% failures, end-to-end p99 3.28 seconds.
• Together AI: 98.5% task success (591 of 600 exact), 1.0% failures, end-to-end p99 12.45 seconds.
• Parasail: 98.3% task success (590 of 600 exact), 1.3% failures, end-to-end p99 11.51 seconds.
• Z.AI: 97.5% task success (585 of 600 exact), 0.2% failures, end-to-end p99 8.59 seconds.
• Baseten: 95.8% task success (575 of 600 exact), 0.0% failures, end-to-end p99 3.23 seconds.
• DeepInfra: 95.5% task success (573 of 600 exact), 4.0% failures, end-to-end p99 17.07 seconds.
• Novita AI: 94.2% task success (565 of 600 exact), 4.0% failures, end-to-end p99 7.25 seconds.
• Nebius: 84.8% task success (509 of 600 exact), 14.8% failures, end-to-end p99 14.97 seconds.

Where does LLM API reliability matter more than raw speed?

• Background enrichment with validation. Maximize exact successes per submission before optimizing latency; every rejected object creates a repair or retry path in your application.
• Deterministic API pipelines. Separate HTTP-successful schema misses from transport failures so you know whether to change prompting or provider operations.
• Human-review queues. Use successful-result yield to estimate review load, then use end-to-end p95 to size how quickly cases arrive for operators.

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 ↗.

LLM API reliability: operational 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 for structured output and JSON schema extraction →
• 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 →