Most accurate LLM API provider
What the benchmark found. Fireworks AI and Telnyx tied for the usable-answer lead: approximately 597 exact successes from 600 submissions. Operational failure counts were Fireworks AI 0; Telnyx 0.
What this page compares. This is an API-yield view of accuracy: out of every request submitted, how many produced the exact structured result the application asked for? It keeps wrong or malformed 2xx responses separate from HTTP, timeout, and transport failures.
How to read the result. The table converts task success into exact successful requests out of 600 and shows the remaining operational failures separately. That makes it possible to distinguish a model-formatting miss from an API call that never delivered an answer.
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 ↗
LLM APIs sorted by successful structured results
Successes, operational failures, and other unsuccessful results reconcile to the submitted workload. E2E P95 is included only as a secondary decision metric.
| Provider / API | Task success | Exact successes | Operational failures | Other unsuccessful | Failure rate | E2E P95 |
|---|---|---|---|---|---|---|
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 99.5% | 597 / 600 | 0 | 3 | 0.0% | 6.72 s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 99.5% | 597 / 600 | 0 | 3 | 0.0% | 2.33 s |
| Modalzai-org/GLM-5.3-Flash ↗ | 99.3% | 596 / 600 | 0 | 4 | 0.0% | 1.90 s |
| Together AIzai-org/GLM-5.3-Flash ↗ | 98.5% | 591 / 600 | 6 | 3 | 1.0% | 3.66 s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 98.3% | 590 / 600 | 8 | 2 | 1.3% | 7.74 s |
| Z.AIglm-5.3-flash ↗ | 97.5% | 585 / 600 | 1 | 14 | 0.2% | 4.83 s |
| Basetenzai-org/GLM-5.3-Flash ↗ | 95.8% | 575 / 600 | 0 | 25 | 0.0% | 2.29 s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 95.5% | 573 / 600 | 24 | 3 | 4.0% | 11.0 s |
| Novita AIzai-org/glm-5.3-flash ↗ | 94.2% | 565 / 600 | 24 | 11 | 4.0% | 5.00 s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 84.8% | 509 / 600 | 89 | 2 | 14.8% | 7.45 s |
Where usable-answer yield matters 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 E2E P95 to size how quickly cases arrive for operators.
LLM API accuracy — operational questions
Which LLM API returned the largest number of usable answers out of 600?
Fireworks AI and Telnyx tied for the usable-answer lead: approximately 597 exact successes from 600 submissions. Operational failure counts were Fireworks AI 0; Telnyx 0.
Are malformed HTTP 200 responses counted as API failures or accuracy misses?
A malformed or schema-invalid HTTP 200 is an accuracy miss, not an operational API failure. Failure rate is reserved for timeouts, HTTP 429, other 4xx, 5xx, and transport errors.
Why does this LLM API page report counts as well as percentages?
Counts expose the denominator directly. A 99.5% task-success rate means 597 exact successes from 600 submissions; percentages alone can hide how many cases require repair or human review.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →