Best inference provider for customer support automation
What the benchmark found. Baseten led E2E P99 at 1.20 seconds; Telnyx, Together AI, Fireworks AI, and Parasail led task success at 98.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. The comparison uses only the ticket-triage family and does not test response helpfulness, multi-turn resolution, or human CSAT.
What this page compares. Customer support automation needs a usable routing decision, not just fluent text. This view uses the 200 ticket-triage requests per provider to keep exact category-and-priority success, complete-response latency, visible response start, generation speed, and delivery failures together.
How to read the result. Set the task-success and failure-rate requirements first if the result routes customers automatically. Use E2E P95/P99 for blocking automation; use TTFA only when the system shows partial text to an operator or customer before the structured decision is complete.
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. Baseten, Modal, Telnyx, Novita AI, Z.AI, Together AI, Fireworks AI, Parasail, Nebius, and DeepInfra used the model identifiers below; the links open the provider documentation used to verify each adapter.
- Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Novita AI:
zai-org/glm-5.3-flash· API docs ↗ - Z.AI:
glm-5.3-flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Nebius:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗
Support automation providers across accuracy and wait
The table derives latency and accuracy deltas from ticket-triage aggregates only. It does not include lookup or contract items, and it does not collapse quality and speed into a synthetic score.
| Provider / API | E2E P99 | Δ vs fastest | TTFA P95 | Generation P95 | Task success | Δ vs most accurate | Failure rate |
|---|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 1.20 s | 0 ms | 538 ms | 49.1 tok/s | 98.0% | -0.5 pp | 0.0% |
| Modalzai-org/GLM-5.3-Flash ↗ | 2.76 s | +1555 ms | 1.06 s | 99.4 tok/s | 98.0% | -0.5 pp | 0.0% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 3.78 s | +2582 ms | 2.21 s | 93.2 tok/s | 98.5% | 0.0 pp | 0.0% |
| Novita AIzai-org/glm-5.3-flash ↗ | 4.11 s | +2905 ms | 2.64 s | 25.9 tok/s | 95.0% | -3.5 pp | 3.5% |
| Z.AIglm-5.3-flash ↗ | 5.75 s | +4551 ms | 2.65 s | 21.5 tok/s | 96.5% | -2.0 pp | 0.5% |
| Together AIzai-org/GLM-5.3-Flash ↗ | 6.83 s | +5629 ms | 1.31 s | 42.1 tok/s | 98.5% | 0.0 pp | 0.0% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 7.70 s | +6498 ms | 5.62 s | 29.6 tok/s | 98.5% | 0.0 pp | 0.0% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 13.1 s | +11924 ms | 5.67 s | 22.1 tok/s | 98.5% | 0.0 pp | 0.5% |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 15.0 s | +13789 ms | 6.10 s | 122.4 tok/s | 84.5% | -14.0 pp | 14.5% |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 17.1 s | +15870 ms | 6.23 s | 7.7 tok/s | 96.5% | -2.0 pp | 2.0% |
Support operations approximated by ticket triage
- Queue assignment. Map the issue to a support category before selecting a specialist queue or automated playbook.
- Urgency detection. Assign priority alongside intent so SLA timers and escalations can start without reading generated prose.
- Agent-assist preprocessing. Populate structured ticket metadata before a human agent opens the case, balancing yield with completion latency.
Customer support inference — operational choices
Do the ticket-accuracy and ticket-latency leaders match?
Baseten led E2E P99 at 1.20 seconds; Telnyx, Together AI, Fireworks AI, and Parasail led task success at 98.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. The comparison uses only the ticket-triage family and does not test response helpfulness, multi-turn resolution, or human CSAT.
Which metrics should an automated support router gate on?
Gate automated routing on exact ticket task success and operational failure rate, then compare E2E P95/P99 among providers that clear those thresholds. TTFA matters only when partial text is actually shown before routing completes.
What customer-support work is outside this ticket-triage benchmark?
The task measures category and priority assignment for one-turn tickets. It does not score drafted reply helpfulness, policy adherence, knowledge retrieval, multi-turn resolution, handoff quality, or customer satisfaction.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →