---
title: "Best LLM Inference Provider for Tool Calling 2026 | Openbenchmarks"
description: "Best LLM inference provider for tool-calling agents: end-to-end p99 latency and exact structured results across 10 providers serving GLM 5.3 Flash."
url: https://openbenchmarks.com/inference/best-inference-provider-for-tool-calling
---

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tool calling · latency and exact arguments · measured Sep 3, 2026

# Best LLM inference provider for tool-calling agents

**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. For tool-calling agents the complete structured result is the finish line; time to first token matters only when text is shown to a person first. 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.** A tool-calling agent cannot proceed until the model returns a complete, schema-valid object it can pass to the next tool. This page applies the full scorecard to that step: complete-answer latency and exact task success, with failures kept separate.

**How to read the result.** Set a task-success floor first, because a plausible but wrong argument triggers the wrong action. Then compare end-to-end p95 and p99 among the providers that clear it; sequential tool calls compound tail latency.

**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 is best for tool-calling agents on latency and exact results?

The full table is retained: a tool-calling step can be blocked by complete-result latency, wrong structured output or an API failure, and no synthetic score hides those.

benchmarks/inference/best-inference-provider-for-tool-calling reviewed run 

__GLM 5.3 Flash inference providers listed alphabetically with balanced pooled latency metrics__
| Provider | End-to-end latency | Time to first output token | Time to first visible answer token | Token generation speed | Task success | Failure rate | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| P50 | P95 | P99 | P50 | P95 | P99 | P50 | P95 | P99 | P50 | P95 | P99 | | | |
| [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 | 228 ms | 499 ms | 946 ms | 287 ms | 1.16 s | 2.65 s | 240.5 tok/s | 61.6 tok/s | 22.6 tok/s | **95.8%** | 0.0% no operational failures |
| 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 | 808 ms | 3.78 s | 9.08 s | 1.15 s | 6.41 s | 13.2 s | 69.4 tok/s | 9.0 tok/s | 5.0 tok/s | **95.5%** | 4.0% 429 0.3% · transport 3.7% |
| 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 | 586 ms | 5.67 s | 8.74 s | 1.06 s | 5.83 s | 8.74 s | 58.2 tok/s | 27.9 tok/s | 14.9 tok/s | **99.5%** | 0.0% no operational failures |
| [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 | 376 ms | 812 ms | 2.72 s | 470 ms | 1.09 s | 2.79 s | 222.2 tok/s | 84.7 tok/s | 34.9 tok/s | **99.3%** | 0.0% no operational failures |
| 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 | 925 ms | 7.11 s | 14.6 s | 1.14 s | 7.11 s | 14.6 s | 269.3 tok/s | 78.8 tok/s | 39.9 tok/s | **84.8%** | 14.8% timeout 2.8% · other 4xx 11.5% · transport 0.5% |
| 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 | 1.13 s | 1.79 s | 4.28 s | 1.40 s | 3.41 s | 5.58 s | 60.6 tok/s | 29.6 tok/s | 11.6 tok/s | **94.2%** | 4.0% timeout 0.2% · 429 3.8% |
| 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 | 856 ms | 4.96 s | 10.9 s | 1.11 s | 5.54 s | 10.9 s | 52.4 tok/s | 22.8 tok/s | 15.6 tok/s | **98.3%** | 1.3% timeout 0.8% · 429 0.2% · transport 0.3% |
| [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 | 497 ms | 802 ms | 2.90 s | 574 ms | 1.34 s | 3.00 s | 184.3 tok/s | 67.5 tok/s | 47.3 tok/s | **99.5%** | 0.0% no operational failures |
| [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 | 377 ms | 2.23 s | 12.2 s | 393 ms | 2.26 s | 12.2 s | 151.6 tok/s | 34.0 tok/s | 21.7 tok/s | **98.5%** | 1.0% timeout 0.3% · 429 0.5% · 5xx 0.2% |
| Z.AI [glm-5.3-flash ↗](https://docs.z.ai/api-reference/llm/chat-completion) | 1.75 s | 4.83 s | 8.59 s | 1.05 s | 1.89 s | 4.76 s | 1.39 s | 3.14 s | 5.00 s | 56.3 tok/s | 27.8 tok/s | 11.5 tok/s | **97.5%** | 0.2% timeout 0.2% |

Rows are listed alphabetically. Generation-speed P95 and P99 are coverage-based slow-tail floors: 95% and 99% of measured responses respectively generated at least that fast.

### 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 tool-calling patterns does this benchmark represent?

* **Tool selection.** Ticket triage approximates choosing a category and priority before dispatching the next action.
* **Typed tool arguments.** Contract extraction approximates producing a typed object that deterministic code must accept without repair.
* **Fact before action.** Meeting-note lookup approximates retrieving one grounded fact that controls the next call.

\[03\] 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

## Tool-calling inference: questions this page answers

### Which inference provider is fastest for tool-calling agents at p99?

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. For tool-calling agents the complete structured result is the finish line; time to first token matters only when text is shown to a person first.

### Which provider returns the most exact tool arguments?

Set a task-success floor first, because a plausible but wrong argument triggers the wrong action. Then compare end-to-end p95 and p99 among the providers that clear it; sequential tool calls compound tail latency.

### Should a tool-calling agent optimize time to first token or end-to-end latency?

The full table is retained: a tool-calling step can be blocked by complete-result latency, wrong structured output or an API failure, and no synthetic score hides those.

\[05\] related comparisons

## Same run, other questions

* [LLM inference provider benchmark: complete scorecard and methodology →](https://openbenchmarks.com/inference)
* [Best LLM inference provider for AI agents: tool calling latency and accuracy →](https://openbenchmarks.com/inference/best-inference-provider-for-my-ai-agent)
* [Fastest LLM inference provider for AI agents →](https://openbenchmarks.com/inference/fastest-inference-provider-for-my-ai-agent)
* [Most accurate LLM inference provider for AI agents →](https://openbenchmarks.com/inference/most-accurate-inference-provider-for-my-ai-agent)
* [Best LLM inference API for chatbots and copilots: time to first token and tokens per second →](https://openbenchmarks.com/inference/best-inference-provider-for-chatbots-and-copilots)
* [Best LLM inference provider for chat applications →](https://openbenchmarks.com/inference/best-inference-provider-for-chat-applications)
* [Best LLM inference provider: speed, accuracy and failures compared →](https://openbenchmarks.com/inference/best-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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