---
title: "Fastest LLM Inference Provider for Small Tasks 2026 | Openbenchmarks"
description: "Fastest LLM inference provider for small tasks: end-to-end p99 on 256-token microtasks across 10 providers serving GLM 5.3 Flash. Open source."
url: https://openbenchmarks.com/inference/fastest-inference-provider-for-small-tasks
---

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small-task inference latency · measured p99 · measured Sep 3, 2026

# Fastest LLM inference provider for small tasks

**What the benchmark found.** Baseten recorded the lowest end-to-end p99 latency at 3.23 seconds on this run, with a median of 331 ms, time to first token p95 of 1.16 seconds, task success of 95.8%, and failure rate of 0.0%. The ranking covers concurrency-one microtasks with a maximum 256-token completion, not batch throughput. 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.** This page ranks providers on the complete client-observed wait for short lookup, classification, and extraction requests. The output is bounded to 256 tokens, so the result targets microtasks rather than essays, code generation, or long-context synthesis.

**How to read the result.** Use end-to-end p99 when the small task blocks another application step. Compare p50 to understand the typical wait, then inspect task success and failure rate so a provider is not rewarded for responses that did not complete correctly.

**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.

* **Baseten:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.baseten.co/reference/inference-api/chat-completions)
* **Modal:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://modal.com/docs/guide/endpoints#calling-your-endpoint)
* **Telnyx:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://developers.telnyx.com/api-reference/openai-chat/create-a-chat-completion-openai-compatible)
* **Novita AI:** `zai-org/glm-5.3-flash` · [API docs ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion)
* **Z.AI:** `glm-5.3-flash` · [API docs ↗](https://docs.z.ai/api-reference/llm/chat-completion)
* **Fireworks AI:** `accounts/fireworks/models/glm-5p3-flash` · [API docs ↗](https://docs.fireworks.ai/api-reference/post-chatcompletions)
* **Parasail:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.parasail.io/parasail-docs/api-reference/chat-completions)
* **Together AI:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.together.ai/docs/inference/chat/overview)
* **Nebius:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion)
* **DeepInfra:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions)

\[01\] measured comparison

## Which LLM inference provider returns small tasks fastest at p99?

Lower is better. Delta versus the fastest p99 result makes the tail-latency gap explicit, while task success and failures retain the denominator context for all 600 submitted requests.

benchmarks/inference/fastest-inference-provider-for-small-tasks reviewed run 

__LLM inference providers sorted by end-to-end p99 latency__
| Provider | End-to-end p50 | End-to-end p95 | End-to-end p99 | Δ vs fastest p99 | Task success | Failure rate |
| --- | --- | --- | --- | --- | --- | --- |
| [Baseten](https://www.baseten.co "Open Baseten in a new tab") [zai-org/GLM-5.3-Flash ↗](https://docs.baseten.co/reference/inference-api/chat-completions) | 331 ms | 2.29 s | 3.23 s | 0 ms | 95.8% | 0.0% |
| [Modal](https://modal.com/ "Open Modal in a new tab") [zai-org/GLM-5.3-Flash ↗](https://modal.com/docs/guide/endpoints#calling-your-endpoint) | 574 ms | 1.90 s | 3.28 s | +50 ms | 99.3% | 0.0% |
| [Telnyx](https://telnyx.com "Open Telnyx in a new tab") [zai-org/GLM-5.3-Flash ↗](https://developers.telnyx.com/api-reference/openai-chat/create-a-chat-completion-openai-compatible) | 669 ms | 2.33 s | 3.48 s | +250 ms | 99.5% | 0.0% |
| Novita AI [zai-org/glm-5.3-flash ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion) | 1.74 s | 5.00 s | 7.25 s | +4020 ms | 94.2% | 4.0% |
| Z.AI [glm-5.3-flash ↗](https://docs.z.ai/api-reference/llm/chat-completion) | 1.75 s | 4.83 s | 8.59 s | +5362 ms | 97.5% | 0.2% |
| Fireworks AI [accounts/fireworks/models/glm-5p3-flash ↗](https://docs.fireworks.ai/api-reference/post-chatcompletions) | 1.95 s | 6.72 s | 10.5 s | +7259 ms | 99.5% | 0.0% |
| Parasail [zai-org/GLM-5.3-Flash ↗](https://docs.parasail.io/parasail-docs/api-reference/chat-completions) | 1.59 s | 7.74 s | 11.5 s | +8283 ms | 98.3% | 1.3% |
| [Together AI](https://www.together.ai "Open Together AI in a new tab") [zai-org/GLM-5.3-Flash ↗](https://docs.together.ai/docs/inference/chat/overview) | 609 ms | 3.66 s | 12.4 s | +9221 ms | 98.5% | 1.0% |
| Nebius [zai-org/GLM-5.3-Flash ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion) | 1.45 s | 7.45 s | 15.0 s | +11742 ms | 84.8% | 14.8% |
| DeepInfra [zai-org/GLM-5.3-Flash ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions) | 1.83 s | 11.0 s | 17.1 s | +13842 ms | 95.5% | 4.0% |

### 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 short workflows expose tail latency?

* **Form autofill.** A user waits for a small extracted object before reviewing or submitting a record.
* **Request routing.** A classifier must finish before the application selects a queue, policy, or next tool.
* **Inline factual lookup.** A compact answer is rendered inside an existing workflow rather than as a long generated response.

\[03\] 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 ↗](https://github.com/openbenchmarks-labs/inference).

\[04\] faq

## Fast inference for small tasks

### Which provider returned small structured tasks fastest at end-to-end p99?

Baseten recorded the lowest end-to-end p99 latency at 3.23 seconds on this run, with a median of 331 ms, time to first token p95 of 1.16 seconds, task success of 95.8%, and failure rate of 0.0%. The ranking covers concurrency-one microtasks with a maximum 256-token completion, not batch throughput.

### Can the fastest typical microtask provider lose at the slow tail?

Baseten had the lowest median end-to-end latency at 331 ms, while Baseten had the lowest end-to-end p99 at 3.23 seconds. The same provider led both points of the distribution.

### Why are timed-out small-task requests reported outside latency percentiles?

Timeouts, HTTP errors, and transport failures have no completed visible answer and therefore no honest end-to-end latency value. They stay in the submitted-request denominator for failure rate and task success, which must be read beside the latency columns.

\[05\] related comparisons

## Same run, other questions

* [LLM inference provider benchmark: complete scorecard and methodology →](https://openbenchmarks.com/inference)
* [Best LLM API for text classification and ticket routing →](https://openbenchmarks.com/inference/best-inference-provider-for-classification)
* [Best LLM API for intent routing and ticket triage →](https://openbenchmarks.com/inference/best-inference-provider-for-intent-routing)
* [Fastest LLM API for classification and ticket routing →](https://openbenchmarks.com/inference/fastest-inference-provider-for-classification)
* [Best LLM API for customer support automation →](https://openbenchmarks.com/inference/best-inference-provider-for-customer-support-automation)
* [Best LLM inference provider for small tasks and short prompts →](https://openbenchmarks.com/inference/best-inference-provider-for-small-tasks)
* [Best LLM inference provider for enterprise AI programs →](https://openbenchmarks.com/inference/best-inference-provider-for-ai-transformation)

\[06\] 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 →](https://openbenchmarks.com/inference) 

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