Best inference provider for small tasks
What the benchmark found. Baseten led E2E P99 at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. The result applies to short, deliberately easy requests and does not rank providers for long-form or high-concurrency inference.
What this page compares. Small tasks are short, bounded inference calls where application overhead and provider queuing can matter more than long-output throughput. This benchmark uses meeting-note lookup, ticket classification, and contract-field extraction with a 256-token output ceiling.
How to read the result. Choose by task success first when the answer triggers automation, then compare E2E P95 and P99. For a user-visible microtask, TTFA indicates when feedback begins; for structured JSON, E2E indicates when the result is usable.
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 ↗
Providers compared for short, bounded inference tasks
The table combines the most decision-relevant metrics and adds deltas from the fastest and most accurate providers. It does not claim the ordering applies to long-form generation, long context, or high-concurrency serving.
| 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 ↗ | 3.23 s | 0 ms | 1.16 s | 61.6 tok/s | 95.8% | -3.7 pp | 0.0% |
| Modalzai-org/GLM-5.3-Flash ↗ | 3.28 s | +50 ms | 1.09 s | 84.7 tok/s | 99.3% | -0.2 pp | 0.0% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 3.48 s | +250 ms | 1.34 s | 67.5 tok/s | 99.5% | 0.0 pp | 0.0% |
| Novita AIzai-org/glm-5.3-flash ↗ | 7.25 s | +4020 ms | 3.41 s | 29.6 tok/s | 94.2% | -5.3 pp | 4.0% |
| Z.AIglm-5.3-flash ↗ | 8.59 s | +5362 ms | 3.14 s | 27.8 tok/s | 97.5% | -2.0 pp | 0.2% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 10.5 s | +7259 ms | 5.83 s | 27.9 tok/s | 99.5% | 0.0 pp | 0.0% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 11.5 s | +8283 ms | 5.54 s | 22.8 tok/s | 98.3% | -1.2 pp | 1.3% |
| Together AIzai-org/GLM-5.3-Flash ↗ | 12.4 s | +9221 ms | 2.26 s | 34.0 tok/s | 98.5% | -1.0 pp | 1.0% |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 15.0 s | +11742 ms | 7.11 s | 78.8 tok/s | 84.8% | -14.7 pp | 14.8% |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 17.1 s | +13842 ms | 6.41 s | 9.0 tok/s | 95.5% | -4.0 pp | 4.0% |
Small-task patterns represented in the run
- Lookup. Retrieve one exact fact from a short meeting note and return only the required answer.
- Classification. Assign a support category and priority that downstream routing code can consume.
- Extraction. Return a complete typed contract object under a strict schema and short output limit.
Choosing inference for small tasks
Do the latency and accuracy leaders match for small inference tasks?
Baseten led E2E P99 at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. The result applies to short, deliberately easy requests and does not rank providers for long-form or high-concurrency inference.
Which metrics matter most for a short structured inference call?
Choose by task success first when the answer triggers automation, then compare E2E P95 and P99. For a user-visible microtask, TTFA indicates when feedback begins; for structured JSON, E2E indicates when the result is usable.
What workload boundaries apply to this small-task provider table?
The table combines the most decision-relevant metrics and adds deltas from the fastest and most accurate providers. It does not claim the ordering applies to long-form generation, long context, or high-concurrency serving.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →