Most accurate inference provider for GLM 5.3 Flash
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%. The score reflects the complete hosted GLM response path, while holding prompts and requested model configuration constant.
What this page compares. The requested model is fixed, but the usable result can still differ across provider paths. This page measures which GLM 5.3 Flash endpoint returned the highest share of exact, schema-valid answers across lookup, classification, and extraction.
How to read the result. Task success uses all 600 calls as the denominator. Wrong answers, malformed JSON, missing fields, timeouts, and HTTP or transport failures all reduce usable yield; failure rate separately identifies only operational delivery failures.
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 ↗
GLM 5.3 Flash providers sorted by task success
Exact-success counts make the accuracy percentage auditable. E2E P99 is shown as a secondary decision metric so equally accurate GLM endpoints can still be distinguished by user wait.
| 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 |
GLM tasks where exact output should lead
- Typed extraction. Require the complete contract object to match before accepting a provider for automated downstream writes.
- Operational classification. Use exact category and priority success when the GLM answer directly routes a ticket or workflow.
- Grounded lookup. Use task success for short factual answers drawn from supplied notes, then latency as the tie-breaker.
GLM 5.3 Flash accuracy questions
Which GLM 5.3 Flash provider achieved the highest exact task success?
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%. The score reflects the complete hosted GLM response path, while holding prompts and requested model configuration constant.
What must a GLM contract extraction return to count as correct?
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 is GLM 5.3 Flash accuracy not identical across hosting providers?
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 →