---
title: "Baseten vs Fireworks AI 2026: Latency and Accuracy | Openbenchmarks"
description: "Baseten vs Fireworks AI for LLM inference: end-to-end p99, time to first token, tokens per second, task success and failures, same 600 requests. Open source."
url: https://openbenchmarks.com/inference/baseten-vs-fireworks-ai
---

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LLM inference API · measured head-to-head · measured Sep 3, 2026

# Baseten vs Fireworks AI

**What the benchmark found.** Baseten had the lower end-to-end p99 latency at 3.23 seconds. Fireworks AI had the higher task success rate at 99.5%. Both figures come from 600 submitted requests per provider. 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.** Two serverless inference platforms compared on the same model, questions, and single-user schedule. The table reports the winner and signed gap on each axis instead of asking readers to compare two distant leaderboard rows.

**How to read the result.** Both providers received identical selected items in the same temporal rounds. Each axis below is computed independently; the faster complete response is not automatically the more accurate or reliable API.

**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)
* **Fireworks AI:** `accounts/fireworks/models/glm-5p3-flash` · [API docs ↗](https://docs.fireworks.ai/api-reference/post-chatcompletions)

\[01\] measured comparison

## Baseten vs Fireworks AI: which is faster, and which is more accurate?

Lower wins for latency and failure rate; higher wins for tokens per second and task success. The gap is shown in the native unit of each metric.

benchmarks/inference/baseten-vs-fireworks-ai reviewed run 

__Baseten and Fireworks AI measured head to head__
| Metric | Baseten | Fireworks AI | Measured leader | Absolute gap |
| --- | --- | --- | --- | --- |
| End-to-end p99 | 3.23 s | 10.5 s | Baseten | 7259 ms |
| End-to-end p95 | 2.29 s | 6.72 s | Baseten | 4431 ms |
| Time to first token p95 | 1.16 s | 5.83 s | Baseten | 4665 ms |
| Tokens per second p50 | 240.5 tok/s | 58.2 tok/s | Baseten | 182.2 tok/s |
| Tokens per second p95 | 61.6 tok/s | 27.9 tok/s | Baseten | 33.8 tok/s |
| Task success | 95.8% | 99.5% | Fireworks AI | 3.7 pp |
| Failure rate | 0.0% | 0.0% | Tie | 0.0 pp |

### 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.
* 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.

\[02\] workflow fit

## Where should I compare Baseten with Fireworks AI?

* **Blocking JSON microservices.** Start with task success, then compare end-to-end p95 and p99 because downstream code waits for the complete structured result.
* **Contract extraction endpoints.** Compare time to first token p95 for response start and tokens per second p95 for the slow throughput tail.
* **Interactive support copilots.** Use end-to-end p99 with failure rate when this model call sits directly on a user-visible critical path.

\[03\] 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

## Baseten vs Fireworks AI: direct questions

### Baseten vs Fireworks AI: which finished p99 responses faster?

Baseten had the lower end-to-end p99 latency at 3.23 seconds. Fireworks AI had the higher task success rate at 99.5%. Both figures come from 600 submitted requests per provider.

### Which platform won more usable tasks, Baseten or Fireworks AI?

Baseten task success was 95.8% versus 99.5% for Fireworks AI. Operational failure rates were 0.0% and 0.0% respectively.

### How do the Baseten and Fireworks model keys differ in this test?

Baseten used zai-org/GLM-5.3-Flash; Fireworks AI used accounts/fireworks/models/glm-5p3-flash. Both received the logical low-reasoning setting, streaming JSON-object output, temperature 0, and the same 600 questions.

\[05\] related comparisons

## Same run, other questions

* [LLM inference provider benchmark: complete scorecard and methodology →](https://openbenchmarks.com/inference)
* [Baseten vs Together AI →](https://openbenchmarks.com/inference/baseten-vs-together-ai)
* [Fireworks AI vs Together AI →](https://openbenchmarks.com/inference/fireworks-ai-vs-together-ai)
* [DeepInfra vs Together AI →](https://openbenchmarks.com/inference/deepinfra-vs-together-ai)
* [Baseten alternatives, benchmarked →](https://openbenchmarks.com/inference/baseten-alternatives)
* [Together AI alternatives, benchmarked →](https://openbenchmarks.com/inference/together-ai-alternatives)
* [Fireworks AI alternatives, benchmarked →](https://openbenchmarks.com/inference/fireworks-ai-alternatives)

\[06\] 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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