Most accurate inference provider for my AI agent
What the benchmark found. Fireworks AI and Telnyx tied for the task-success lead at 99.5%. The leading score represents approximately 597 exact successful results from 600 requests; operational failure rates were Fireworks AI 0.0%; Telnyx 0.0%. For autonomous agents, task success measures whether a submitted call produced an answer safe to pass to the next step without repair.
What this page compares. An agent needs more than an HTTP-successful response: the returned fact, classification, or tool argument must be exact and schema-valid. This page orders providers by successful results across all 600 submissions.
How to read the result. Use task success as the primary gate when the agent acts without human review. It includes incorrect and malformed results as well as operational failures, then keeps failure rate separate so prompt-quality misses and delivery failures can be diagnosed independently.
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.
APIs used. The runner called each provider's OpenAI-compatible chat-completions surface. Fireworks AI, Telnyx, Modal, Together AI, Parasail, Z.AI, Baseten, DeepInfra, Novita AI, and Nebius used the model identifiers below; the links open the provider documentation used to verify each adapter.
- Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Z.AI:
glm-5.3-flash· API docs ↗ - Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗ - Novita AI:
zai-org/glm-5.3-flash· API docs ↗ - Nebius:
zai-org/GLM-5.3-Flash· API docs ↗
Inference providers sorted by exact AI-agent task success
Higher task success is better. Exact-success counts show the expected usable yield from 600 calls, with E2E P99 retained as a secondary tie-breaker for agent paths that also have a latency budget.
| Provider / API | Task success | Exact successes | Failure rate | E2E P99 |
|---|---|---|---|---|
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 99.5% | 597 / 600 | 0.0% | 10.5 s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 99.5% | 597 / 600 | 0.0% | 3.48 s |
| Modalzai-org/GLM-5.3-Flash ↗ | 99.3% | 596 / 600 | 0.0% | 3.28 s |
| Together AIzai-org/GLM-5.3-Flash ↗ | 98.5% | 591 / 600 | 1.0% | 12.4 s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 98.3% | 590 / 600 | 1.3% | 11.5 s |
| Z.AIglm-5.3-flash ↗ | 97.5% | 585 / 600 | 0.2% | 8.59 s |
| Basetenzai-org/GLM-5.3-Flash ↗ | 95.8% | 575 / 600 | 0.0% | 3.23 s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 95.5% | 573 / 600 | 4.0% | 17.1 s |
| Novita AIzai-org/glm-5.3-flash ↗ | 94.2% | 565 / 600 | 4.0% | 7.25 s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 84.8% | 509 / 600 | 14.8% | 15.0 s |
Agent decisions where exactness matters
- 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.
Accurate inference for AI agents
Which provider produced the most exact usable outputs for an AI agent?
Fireworks AI and Telnyx tied for the task-success lead at 99.5%. The leading score represents approximately 597 exact successful results from 600 requests; operational failure rates were Fireworks AI 0.0%; Telnyx 0.0%. For autonomous agents, task success measures whether a submitted call produced an answer safe to pass to the next step without repair.
How strict is the structured-output accuracy test for agent tool arguments?
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 can AI-agent success vary across providers serving 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.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →