---
title: "Best LLM API for Text Classification 2026 | Openbenchmarks"
description: "Best LLM API for text classification: exact category and priority success on 200 tickets per provider across 10 providers. Independent, open source."
url: https://openbenchmarks.com/inference/best-inference-provider-for-classification
---

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text classification · ticket triage · intent routing · measured Sep 3, 2026

# Best LLM API for text classification and ticket routing

**What the benchmark found.** Fireworks AI, Parasail, Telnyx, and Together AI tied for the task-success lead at 98.5%. The leading score represents approximately 197 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Parasail 0.5%; Telnyx 0.0%; Together AI 0.0%. This result is based on 200 ticket-triage submissions per provider, not the pooled lookup and extraction rows. 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.** This view isolates the benchmark's 200 ticket-triage questions instead of reusing the pooled three-task score. Each response must return both an exact category and an exact priority, making the task a measured proxy for short text classification and intent-routing decisions.

**How to read the result.** Use task success as the first gate when the classification changes a queue, policy, or next action. The percentage includes all submitted ticket cases; failure rate then separates provider delivery failures from HTTP-successful answers whose class or priority was wrong.

**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.

* **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)
* **Telnyx:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://developers.telnyx.com/api-reference/openai-chat/create-a-chat-completion-openai-compatible)
* **Together AI:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.together.ai/docs/inference/chat/overview)
* **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)
* **DeepInfra:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions)
* **Z.AI:** `glm-5.3-flash` · [API docs ↗](https://docs.z.ai/api-reference/llm/chat-completion)
* **Novita AI:** `zai-org/glm-5.3-flash` · [API docs ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion)
* **Nebius:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion)

\[01\] measured comparison

## Which LLM API classifies tickets most accurately?

This table contains only ticket-triage aggregates. Exact successes are shown out of 200, with failure rate and end-to-end p99 retained so accuracy can be balanced against operational reliability and blocking latency.

benchmarks/inference/best-inference-provider-for-classification reviewed run 

__LLM inference providers sorted by exact task success rate__
| Provider / API | Task success | Exact successes | Failure rate | End-to-end p99 |
| --- | --- | --- | --- | --- |
| Fireworks AI [accounts/fireworks/models/glm-5p3-flash ↗](https://docs.fireworks.ai/api-reference/post-chatcompletions) | 98.5% | 197 / 200 | 0.0% | 7.70 s |
| Parasail [zai-org/GLM-5.3-Flash ↗](https://docs.parasail.io/parasail-docs/api-reference/chat-completions) | 98.5% | 197 / 200 | 0.5% | 13.1 s |
| [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) | 98.5% | 197 / 200 | 0.0% | 3.78 s |
| [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) | 98.5% | 197 / 200 | 0.0% | 6.83 s |
| [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) | 98.0% | 196 / 200 | 0.0% | 1.20 s |
| [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) | 98.0% | 196 / 200 | 0.0% | 2.76 s |
| DeepInfra [zai-org/GLM-5.3-Flash ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions) | 96.5% | 193 / 200 | 2.0% | 17.1 s |
| Z.AI [glm-5.3-flash ↗](https://docs.z.ai/api-reference/llm/chat-completion) | 96.5% | 193 / 200 | 0.5% | 5.75 s |
| Novita AI [zai-org/glm-5.3-flash ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion) | 95.0% | 190 / 200 | 3.5% | 4.11 s |
| Nebius [zai-org/GLM-5.3-Flash ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion) | 84.5% | 169 / 200 | 14.5% | 15.0 s |

### Provider by provider, in the order above

* Fireworks AI: 98.5% task success (197 of 200 exact), 0.0% failures, end-to-end p99 7.70 seconds.
* Parasail: 98.5% task success (197 of 200 exact), 0.5% failures, end-to-end p99 13.12 seconds.
* Telnyx: 98.5% task success (197 of 200 exact), 0.0% failures, end-to-end p99 3.78 seconds.
* Together AI: 98.5% task success (197 of 200 exact), 0.0% failures, end-to-end p99 6.83 seconds.
* Baseten: 98.0% task success (196 of 200 exact), 0.0% failures, end-to-end p99 1.20 seconds.
* Modal: 98.0% task success (196 of 200 exact), 0.0% failures, end-to-end p99 2.76 seconds.
* DeepInfra: 96.5% task success (193 of 200 exact), 2.0% failures, end-to-end p99 17.07 seconds.
* Z.AI: 96.5% task success (193 of 200 exact), 0.5% failures, end-to-end p99 5.75 seconds.
* Novita AI: 95.0% task success (190 of 200 exact), 3.5% failures, end-to-end p99 4.11 seconds.
* Nebius: 84.5% task success (169 of 200 exact), 14.5% failures, end-to-end p99 14.99 seconds.

\[02\] workflow fit

## Which classification workflows are closest to the measured task?

* **Support ticket triage.** Route an inbound ticket only when both its issue category and urgency level are correct.
* **Intent routing.** Map short user text to a bounded intent enum before choosing a handler, tool, or workflow branch.
* **Priority assignment.** Return a structured severity label that downstream SLA and escalation rules can consume without parsing prose.

\[03\] 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

## Classification provider questions from the ticket task

### Which provider achieved the highest exact success on the ticket-triage family?

Fireworks AI, Parasail, Telnyx, and Together AI tied for the task-success lead at 98.5%. The leading score represents approximately 197 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Parasail 0.5%; Telnyx 0.0%; Together AI 0.0%. This result is based on 200 ticket-triage submissions per provider, not the pooled lookup and extraction rows.

### What must a classification response return to count as correct?

A ticket-triage response counts as correct only when both the category and priority exactly match the reference enums. A correct category with the wrong priority receives no partial task-success credit.

### How far can this ticket benchmark generalize to a private intent-routing taxonomy?

Intent routing is represented only by the benchmark's bounded ticket-triage task. It is useful evidence for short structured classification, but it does not test a production team's private intent taxonomy, class balance, or escalation policy.

### Which LLM API is fastest for classification and ticket routing?

The end-to-end p99 column on this table is computed from the ticket-triage family alone. A synchronous classifier blocks the next branch until the complete category-and-priority object arrives, so p99 is the figure that protects a routing SLA; read it beside exact task success, because a fast wrong label does not finish the job.

\[05\] related comparisons

## Same run, other questions

* [LLM inference provider benchmark: complete scorecard and methodology →](https://openbenchmarks.com/inference)
* [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)
* [Fastest LLM inference provider for small tasks →](https://openbenchmarks.com/inference/fastest-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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