Most accurate inference provider
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%.
What this page compares. Holding the model name constant does not guarantee identical usable output. Providers can differ in malformed responses, truncation, transport reliability, and whether the returned object exactly matches the requested task schema. This page sorts the run by task success rate.
How to read the result. Task success uses all 600 submitted requests per provider as its denominator. A response counts only when it is schema-valid and exactly correct under the task-family rubric; operational failures, malformed JSON, incomplete objects, and wrong answers all reduce the score.
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 task success
Higher is better. Correct-request counts make the percentage concrete, while failure rate and E2E P99 separate answer quality from service reliability and speed.
| 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 |
Workflows where exact task success should lead
- Contract-field writeback. Require task success first because a fast incomplete object can write incorrect dates, renewal terms, or notice periods into another system.
- Automated ticket triage. Use exact category-and-priority success when the answer routes work without a human reviewing each result.
- Meeting-note lookup. Use task success when the answer is consumed as a fact; latency becomes the tie-breaker among providers that clear the required quality bar.
Inference accuracy — benchmark-specific questions
Which provider completed the most exact-correct microtasks?
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%.
What makes a contract extraction count as accurate here?
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.
How can two providers serving GLM 5.3 Flash get different success rates?
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 →