---
title: "Most Accurate GLM 5.3 Flash Provider 2026 | Openbenchmarks"
description: "Most accurate GLM 5.3 Flash provider: exact task success out of 600 requests across 10 hosts of the same model. Independent, open source."
url: https://openbenchmarks.com/inference/most-accurate-inference-provider-for-glm-5-3-flash
---

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GLM 5.3 Flash accuracy · exact task success · measured Sep 3, 2026

# Most accurate GLM 5.3 Flash provider

**What the benchmark found.** Fireworks AI and Telnyx tied for the task-success lead at 99.5%. The leading score represents approximately 597 exact successful results from 600 requests; operational failure rates were Fireworks AI 0.0%; Telnyx 0.0%. The score reflects the complete hosted GLM response path, while holding prompts and requested model configuration constant. 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.** The requested model is fixed, but the usable result can still differ across provider paths. This page measures which GLM 5.3 Flash endpoint returned the highest share of exact, schema-valid answers across lookup, classification, and extraction.

**How to read the result.** Task success uses all 600 calls as the denominator. Wrong answers, malformed JSON, missing fields, timeouts, and HTTP or transport failures all reduce usable yield; failure rate separately identifies only operational delivery failures.

**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.

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

Exact-success counts make the accuracy percentage auditable. End-to-end p99 is shown as a secondary decision metric so equally accurate GLM endpoints can still be distinguished by user wait.

benchmarks/inference/most-accurate-inference-provider-for-glm-5-3-flash 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) | 99.5% | 597 / 600 | 0.0% | 10.5 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) | 99.5% | 597 / 600 | 0.0% | 3.48 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) | 99.3% | 596 / 600 | 0.0% | 3.28 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% | 591 / 600 | 1.0% | 12.4 s |
| Parasail [zai-org/GLM-5.3-Flash ↗](https://docs.parasail.io/parasail-docs/api-reference/chat-completions) | 98.3% | 590 / 600 | 1.3% | 11.5 s |
| Z.AI [glm-5.3-flash ↗](https://docs.z.ai/api-reference/llm/chat-completion) | 97.5% | 585 / 600 | 0.2% | 8.59 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) | 95.8% | 575 / 600 | 0.0% | 3.23 s |
| DeepInfra [zai-org/GLM-5.3-Flash ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions) | 95.5% | 573 / 600 | 4.0% | 17.1 s |
| Novita AI [zai-org/glm-5.3-flash ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion) | 94.2% | 565 / 600 | 4.0% | 7.25 s |
| Nebius [zai-org/GLM-5.3-Flash ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion) | 84.8% | 509 / 600 | 14.8% | 15.0 s |

### Provider by provider, in the order above

* Fireworks AI: 99.5% task success (597 of 600 exact), 0.0% failures, end-to-end p99 10.49 seconds.
* Telnyx: 99.5% task success (597 of 600 exact), 0.0% failures, end-to-end p99 3.48 seconds.
* Modal: 99.3% task success (596 of 600 exact), 0.0% failures, end-to-end p99 3.28 seconds.
* Together AI: 98.5% task success (591 of 600 exact), 1.0% failures, end-to-end p99 12.45 seconds.
* Parasail: 98.3% task success (590 of 600 exact), 1.3% failures, end-to-end p99 11.51 seconds.
* Z.AI: 97.5% task success (585 of 600 exact), 0.2% failures, end-to-end p99 8.59 seconds.
* Baseten: 95.8% task success (575 of 600 exact), 0.0% failures, end-to-end p99 3.23 seconds.
* DeepInfra: 95.5% task success (573 of 600 exact), 4.0% failures, end-to-end p99 17.07 seconds.
* Novita AI: 94.2% task success (565 of 600 exact), 4.0% failures, end-to-end p99 7.25 seconds.
* Nebius: 84.8% task success (509 of 600 exact), 14.8% failures, end-to-end p99 14.97 seconds.

\[02\] workflow fit

## Which GLM tasks should exact output lead?

* **Typed extraction.** Require the complete contract object to match before accepting a provider for automated downstream writes.
* **Operational classification.** Use exact category and priority success when the GLM answer directly routes a ticket or workflow.
* **Grounded lookup.** Use task success for short factual answers drawn from supplied notes, then latency as the tie-breaker.

\[03\] 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

## GLM 5.3 Flash accuracy questions

### Which GLM 5.3 Flash provider achieved the highest exact task success?

Fireworks AI and Telnyx tied for the task-success lead at 99.5%. The leading score represents approximately 597 exact successful results from 600 requests; operational failure rates were Fireworks AI 0.0%; Telnyx 0.0%. The score reflects the complete hosted GLM response path, while holding prompts and requested model configuration constant.

### What must a GLM contract extraction return to count as correct?

Contract extraction counts only when every required field is present and typed correctly; strings and enums compare exactly, dates use ISO YYYY-MM-DD, and the full object must match. Correct-field diagnostics do not create partial task-success credit.

### Why is GLM 5.3 Flash accuracy not identical across hosting providers?

Even with the same requested model, delivery layers can differ in truncation, malformed output, model routing, stream completion, and operational reliability. Task success measures the usable result produced by the complete provider path, not model intelligence in isolation.

\[05\] related comparisons

## Same run, other questions

* [LLM inference provider benchmark: complete scorecard and methodology →](https://openbenchmarks.com/inference)
* [GLM 5.3 Flash API providers compared: latency, tokens per second and accuracy →](https://openbenchmarks.com/inference/best-inference-provider-for-glm-5-3-flash)
* [Fastest GLM 5.3 Flash API provider →](https://openbenchmarks.com/inference/fastest-inference-provider-for-glm-5-3-flash)
* [Best LLM inference provider: speed, accuracy and failures compared →](https://openbenchmarks.com/inference/best-inference-provider)
* [Fireworks AI vs Together AI →](https://openbenchmarks.com/inference/fireworks-ai-vs-together-ai)
* [Fastest LLM inference provider: time to first token, tokens per second and end-to-end latency →](https://openbenchmarks.com/inference/fastest-inference-provider)

\[06\] 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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