benchmarks/inference/best inference provider for small tasks
small inference tasks · latency and usable yield · measured Sep 3, 2026

Best LLM inference provider for small tasks and short prompts

What the benchmark found. Baseten led end-to-end p99 latency at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The scorecard keeps latency, tokens per second, 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. Measured across 10 LLM inference providers serving GLM 5.3 Flash, 600 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. 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 end-to-end p95 and p99. For a user-visible microtask, time to first token indicates when feedback begins; for structured JSON, end-to-end latency 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 with the model identifiers below; the links open the provider documentation used to verify each adapter.

[01] measured comparison

Which LLM inference provider handles short, bounded tasks best?

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.

benchmarks/inference/best-inference-provider-for-small-tasksreviewed run
LLM inference providers compared on end-to-end latency, time to first token, tokens per second, task success and failures
Provider / APIEnd-to-end p99Δ vs fastestTTFT p95Tokens/s p95Task successΔ vs most accurateFailure rate
Basetenzai-org/GLM-5.3-Flash ↗3.23 s0 ms1.16 s61.6 tok/s95.8%-3.7 pp0.0%
Modalzai-org/GLM-5.3-Flash ↗3.28 s+50 ms1.09 s84.7 tok/s99.3%-0.2 pp0.0%
Telnyxzai-org/GLM-5.3-Flash ↗3.48 s+250 ms1.34 s67.5 tok/s99.5%0.0 pp0.0%
Novita AIzai-org/glm-5.3-flash ↗7.25 s+4020 ms3.41 s29.6 tok/s94.2%-5.3 pp4.0%
Z.AIglm-5.3-flash ↗8.59 s+5362 ms3.14 s27.8 tok/s97.5%-2.0 pp0.2%
Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗10.5 s+7259 ms5.83 s27.9 tok/s99.5%0.0 pp0.0%
Parasailzai-org/GLM-5.3-Flash ↗11.5 s+8283 ms5.54 s22.8 tok/s98.3%-1.2 pp1.3%
Together AIzai-org/GLM-5.3-Flash ↗12.4 s+9221 ms2.26 s34.0 tok/s98.5%-1.0 pp1.0%
Nebiuszai-org/GLM-5.3-Flash ↗15.0 s+11742 ms7.11 s78.8 tok/s84.8%-14.7 pp14.8%
DeepInfrazai-org/GLM-5.3-Flash ↗17.1 s+13842 ms6.41 s9.0 tok/s95.5%-4.0 pp4.0%

Provider by provider, in the order above

  • Baseten: 331 ms median, 3.23 seconds end-to-end p99, time to first token p95 1.16 seconds, 240.5 tokens per second, 95.8% task success, 0.0% failures.
  • Modal: 574 ms median, 3.28 seconds end-to-end p99, time to first token p95 1.09 seconds, 222.2 tokens per second, 99.3% task success, 0.0% failures.
  • Telnyx: 669 ms median, 3.48 seconds end-to-end p99, time to first token p95 1.34 seconds, 184.3 tokens per second, 99.5% task success, 0.0% failures.
  • Novita AI: 1.74 seconds median, 7.25 seconds end-to-end p99, time to first token p95 3.41 seconds, 60.6 tokens per second, 94.2% task success, 4.0% failures.
  • Z.AI: 1.75 seconds median, 8.59 seconds end-to-end p99, time to first token p95 3.14 seconds, 56.3 tokens per second, 97.5% task success, 0.2% failures.
  • Fireworks AI: 1.95 seconds median, 10.49 seconds end-to-end p99, time to first token p95 5.83 seconds, 58.2 tokens per second, 99.5% task success, 0.0% failures.
  • Parasail: 1.59 seconds median, 11.51 seconds end-to-end p99, time to first token p95 5.54 seconds, 52.4 tokens per second, 98.3% task success, 1.3% failures.
  • Together AI: 609 ms median, 12.45 seconds end-to-end p99, time to first token p95 2.26 seconds, 151.6 tokens per second, 98.5% task success, 1.0% failures.
  • Nebius: 1.45 seconds median, 14.97 seconds end-to-end p99, time to first token p95 7.11 seconds, 269.3 tokens per second, 84.8% task success, 14.8% failures.
  • DeepInfra: 1.83 seconds median, 17.07 seconds end-to-end p99, time to first token p95 6.41 seconds, 69.4 tokens per second, 95.5% task success, 4.0% failures.
[02] workflow fit

Which small-task patterns does the run represent?

  • 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.
[03] how it was measured

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_base tokenizer, 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 ↗.
[04] faq

Choosing inference for small tasks

Do the latency and accuracy leaders match for small inference tasks?

Baseten led end-to-end p99 latency at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The scorecard keeps latency, tokens per second, 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 end-to-end p95 and p99. For a user-visible microtask, time to first token indicates when feedback begins; for structured JSON, end-to-end latency 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.

[05] related comparisons
[06] source benchmark

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 →

Markdown version