Fastest LLM API provider
What the benchmark found. Baseten was the fastest complete-response LLM API at E2E P99 of 3.23 seconds. Its TTFA P95 was 1.16 seconds, and its operational failure rate was 0.0% across 600 submissions.
What this page compares. For an application calling an LLM API, the useful finish line is often not the first streamed character but the complete JSON result that unlocks the next step. This comparison measures that API round trip across identical lookup, routing, and extraction prompts.
How to read the result. The table keeps API startup and completion in view together: TTFA P95 shows when visible output begins, while E2E P95 and P99 show when the application has the complete answer. Every row links to the API documentation and model key used in the run.
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. Baseten, Modal, Telnyx, Novita AI, Z.AI, Fireworks AI, Parasail, Together AI, 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 ↗ - Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
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 ↗
LLM APIs sorted by end-to-end P99
The table is API-focused: model key, documentation, first-answer tail, complete-answer tail, and operational failures. It does not repeat the main benchmark's full streaming matrix.
| Provider / API | E2E P95 | E2E P99 | Δ vs fastest P99 | TTFA P95 | Task success | Failure rate |
|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 2.29 s | 3.23 s | 0 ms | 1.16 s | 95.8% | 0.0% |
| Modalzai-org/GLM-5.3-Flash ↗ | 1.90 s | 3.28 s | +50 ms | 1.09 s | 99.3% | 0.0% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 2.33 s | 3.48 s | +250 ms | 1.34 s | 99.5% | 0.0% |
| Novita AIzai-org/glm-5.3-flash ↗ | 5.00 s | 7.25 s | +4020 ms | 3.41 s | 94.2% | 4.0% |
| Z.AIglm-5.3-flash ↗ | 4.83 s | 8.59 s | +5362 ms | 3.14 s | 97.5% | 0.2% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 6.72 s | 10.5 s | +7259 ms | 5.83 s | 99.5% | 0.0% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 7.74 s | 11.5 s | +8283 ms | 5.54 s | 98.3% | 1.3% |
| Together AIzai-org/GLM-5.3-Flash ↗ | 3.66 s | 12.4 s | +9221 ms | 2.26 s | 98.5% | 1.0% |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 7.45 s | 15.0 s | +11742 ms | 7.11 s | 84.8% | 14.8% |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 11.0 s | 17.1 s | +13842 ms | 6.41 s | 95.5% | 4.0% |
LLM API workflows where completion latency matters
- Tool-result generation. Choose on E2E P95 when an agent cannot make its next tool call until a schema-valid JSON result is complete.
- Live support routing. Read TTFA only if the text is shown to an operator; read E2E when category and priority feed an automated queue.
- Inline document extraction. Use E2E P99 and task success together when a user is waiting for contract fields to populate a form.
Choosing a fast LLM API — measured answers
What was the fastest LLM API for a complete structured response?
Baseten was the fastest complete-response LLM API at E2E P99 of 3.23 seconds. Its TTFA P95 was 1.16 seconds, and its operational failure rate was 0.0% across 600 submissions.
Which API model identifiers were used for the LLM latency test?
Baseten: zai-org/GLM-5.3-Flash; Modal: zai-org/GLM-5.3-Flash; Telnyx: zai-org/GLM-5.3-Flash; Novita AI: zai-org/glm-5.3-flash; Z.AI: glm-5.3-flash; Fireworks AI: accounts/fireworks/models/glm-5p3-flash; Parasail: zai-org/GLM-5.3-Flash; Together AI: zai-org/GLM-5.3-Flash; Nebius: zai-org/GLM-5.3-Flash; DeepInfra: zai-org/GLM-5.3-Flash. Each identifier links to the provider documentation used for this comparison where available.
Should an agent workflow optimize TTFA or end-to-end latency?
Optimize E2E latency when the next agent step requires the complete JSON object. Optimize TTFA when visible text is rendered immediately and the user benefits before the structured response finishes.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →