Most accurate LLM inference provider for AI agents

Which LLM inference provider returns the most exact results for AI agents?

Higher task success is better. Exact-success counts show the expected usable yield from 600 calls, with end-to-end p99 retained as a secondary tie-breaker for agent paths that also have a latency budget.

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.

Which agent decisions depend on exactness?

• Autonomous writeback. Prefer exact task success when an agent writes extracted fields into a CRM, contract system, or ticket queue without review.
• Tool argument generation. A schema-valid but wrong enum or date can trigger the wrong action, so field-level plausibility is not enough.
• Fact lookup before action. Use the meeting-note task as a proxy for retrieving a short fact that controls a later agent decision.

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 ↗.

Accurate inference for AI agents

Same run, other questions

• LLM inference provider benchmark: complete scorecard and methodology →
• Best LLM inference provider for AI agents: tool calling latency and accuracy →
• Best LLM inference provider for tool-calling agents →
• Fastest LLM inference provider for AI agents →
• Best LLM inference API for chatbots and copilots: time to first token and tokens per second →
• Best LLM inference provider for chat applications →
• Best LLM inference provider: speed, accuracy and failures compared →

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 →