Best inference provider for classification and intent routing
What the benchmark found. Fireworks AI, Parasail, Telnyx, and Together AI tied for the task-success lead at 98.5%. The leading score represents approximately 197 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Parasail 0.5%; Telnyx 0.0%; Together AI 0.0%. This result is based on 200 ticket-triage submissions per provider, not the pooled lookup and extraction rows.
What this page compares. This view isolates the benchmark's 200 ticket-triage questions instead of reusing the pooled three-task score. Each response must return both an exact category and an exact priority, making the task a measured proxy for short structured classification and intent-routing decisions.
How to read the result. Use task success as the first gate when the classification changes a queue, policy, or next action. The percentage includes all submitted ticket cases; failure rate then separates provider delivery failures from HTTP-successful answers whose class or priority was wrong.
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.
Task-specific table. The table below uses only the published ticket-triage aggregate rather than the three-family pooled summary. Each provider therefore contributes 200 submitted requests to this view.
APIs used. The runner called each provider's OpenAI-compatible chat-completions surface. Fireworks AI, Parasail, Telnyx, Together AI, Baseten, Modal, DeepInfra, Z.AI, 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 ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗ - Z.AI:
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 ↗
Providers sorted by ticket-triage task success
This table contains only ticket-triage aggregates. Exact successes are shown out of 200, with failure rate and E2E P99 retained so accuracy can be balanced against operational reliability and blocking latency.
| Provider / API | Task success | Exact successes | Failure rate | E2E P99 |
|---|---|---|---|---|
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 98.5% | 197 / 200 | 0.0% | 7.70 s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 98.5% | 197 / 200 | 0.5% | 13.1 s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 98.5% | 197 / 200 | 0.0% | 3.78 s |
| Together AIzai-org/GLM-5.3-Flash ↗ | 98.5% | 197 / 200 | 0.0% | 6.83 s |
| Basetenzai-org/GLM-5.3-Flash ↗ | 98.0% | 196 / 200 | 0.0% | 1.20 s |
| Modalzai-org/GLM-5.3-Flash ↗ | 98.0% | 196 / 200 | 0.0% | 2.76 s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 96.5% | 193 / 200 | 2.0% | 17.1 s |
| Z.AIglm-5.3-flash ↗ | 96.5% | 193 / 200 | 0.5% | 5.75 s |
| Novita AIzai-org/glm-5.3-flash ↗ | 95.0% | 190 / 200 | 3.5% | 4.11 s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 84.5% | 169 / 200 | 14.5% | 15.0 s |
Classification workflows closest to the measured task
- Support ticket triage. Route an inbound ticket only when both its issue category and urgency level are correct.
- Intent routing. Map short user text to a bounded intent enum before choosing a handler, tool, or workflow branch.
- Priority assignment. Return a structured severity label that downstream SLA and escalation rules can consume without parsing prose.
Classification provider questions from the ticket task
Which provider achieved the highest exact success on the ticket-triage family?
Fireworks AI, Parasail, Telnyx, and Together AI tied for the task-success lead at 98.5%. The leading score represents approximately 197 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Parasail 0.5%; Telnyx 0.0%; Together AI 0.0%. This result is based on 200 ticket-triage submissions per provider, not the pooled lookup and extraction rows.
What must a classification response return to count as correct?
A ticket-triage response counts as correct only when both the category and priority exactly match the reference enums. A correct category with the wrong priority receives no partial task-success credit.
How far can this ticket benchmark generalize to a private intent-routing taxonomy?
Intent routing is represented only by the benchmark's bounded ticket-triage task. It is useful evidence for short structured classification, but it does not test a production team's private intent taxonomy, class balance, or escalation policy.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →