Best LLM API for customer support automation
What the benchmark found. Baseten led end-to-end p99 latency at 1.20 seconds; Telnyx, Together AI, Fireworks AI, and Parasail led task success at 98.5%. The scorecard keeps latency, tokens per second, 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. Measured across 10 LLM inference providers serving GLM 5.3 Flash, 200 requests each, Sep 3, 2026.
Providers measured. Baseten, DeepInfra, Fireworks AI, Modal, Nebius, Novita AI, Parasail, Telnyx, Together AI, and Z.AI, each serving GLM 5.3 Flash behind an OpenAI-compatible chat-completions endpoint.
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, tokens per second, 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 end-to-end p95 and p99 for blocking automation; use time to first token 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 with 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 ↗
Which LLM API routes support tickets accurately and fast?
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 | End-to-end p99 | Δ vs fastest | TTFT p95 | Tokens/s 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% |
Provider by provider, in the order above
- Baseten: 274 ms median, 1.20 seconds end-to-end p99, time to first token p95 538 ms, 313.0 tokens per second, 98.0% task success, 0.0% failures.
- Modal: 493 ms median, 2.76 seconds end-to-end p99, time to first token p95 1.06 seconds, 270.3 tokens per second, 98.0% task success, 0.0% failures.
- Telnyx: 645 ms median, 3.78 seconds end-to-end p99, time to first token p95 2.21 seconds, 211.9 tokens per second, 98.5% task success, 0.0% failures.
- Novita AI: 1.63 seconds median, 4.11 seconds end-to-end p99, time to first token p95 2.64 seconds, 65.4 tokens per second, 95.0% task success, 3.5% failures.
- Z.AI: 1.62 seconds median, 5.75 seconds end-to-end p99, time to first token p95 2.65 seconds, 57.2 tokens per second, 96.5% task success, 0.5% failures.
- Together AI: 417 ms median, 6.83 seconds end-to-end p99, time to first token p95 1.31 seconds, 178.6 tokens per second, 98.5% task success, 0.0% failures.
- Fireworks AI: 1.07 seconds median, 7.70 seconds end-to-end p99, time to first token p95 5.62 seconds, 63.3 tokens per second, 98.5% task success, 0.0% failures.
- Parasail: 1.48 seconds median, 13.12 seconds end-to-end p99, time to first token p95 5.67 seconds, 48.8 tokens per second, 98.5% task success, 0.5% failures.
- Nebius: 1.02 seconds median, 14.99 seconds end-to-end p99, time to first token p95 6.10 seconds, 323.3 tokens per second, 84.5% task success, 14.5% failures.
- DeepInfra: 1.23 seconds median, 17.07 seconds end-to-end p99, time to first token p95 6.23 seconds, 89.5 tokens per second, 96.5% task success, 2.0% failures.
Which support operations does ticket triage approximate?
- 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.
How is LLM inference provider latency and accuracy measured here?
- Same model. Every endpoint serves GLM 5.3 Flash; the provider layer is the only variable.
- Same request. One-turn, non-tool chat completion in JSON-object mode, streaming on, temperature 0, top-p 1, reasoning low, 256-token ceiling, 20-second timeout, concurrency one.
- Client clock, pinned tokenizer. Time to first token, end-to-end latency and tokens per second come from the client; visible answer tokens are counted with a pinned
tiktoken o200k_basetokenizer, never provider-reported usage. - Exact task success. An answer counts only when it is schema-valid and exactly correct under the task rubric. Timeouts, HTTP errors and malformed responses stay in the denominator.
- Serverless endpoints only. Dedicated capacity is outside the comparison. Runner and per-request records: openbenchmarks-labs/inference ↗.
Customer support inference: operational choices
Do the ticket-accuracy and ticket-latency leaders match?
Baseten led end-to-end p99 latency at 1.20 seconds; Telnyx, Together AI, Fireworks AI, and Parasail led task success at 98.5%. The scorecard keeps latency, tokens per second, 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 end-to-end p95 and p99 among providers that clear those thresholds. Time to first token 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.
Same run, other questions
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Read the complete LLM inference provider benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the LLM Inference Provider Benchmark →