Baseten vs Fireworks AI
What the benchmark found. Baseten had the lower E2E P99 at 3.23 seconds. Fireworks AI had the higher task success rate at 99.5%. Both figures come from 600 submitted requests per provider.
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. Baseten and Fireworks AI used the model identifiers below; the links open the provider documentation used to verify each adapter.
- Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗
Baseten and Fireworks AI, axis by axis
Lower wins for latency and failure rate; higher wins for token generation speed and task success. The gap is shown in the native unit of each metric.
| Metric | Baseten | Fireworks AI | Measured leader | Absolute gap |
|---|---|---|---|---|
| E2E P99 | 3.23 s | 10.5 s | Baseten | 7259 ms |
| E2E P95 | 2.29 s | 6.72 s | Baseten | 4431 ms |
| TTFA P95 | 1.16 s | 5.83 s | Baseten | 4665 ms |
| Generation P50 | 240.5 tok/s | 58.2 tok/s | Baseten | 182.2 tok/s |
| Generation 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 |
Where to compare Baseten with Fireworks AI
- Blocking JSON microservices. Start with task success, then compare E2E P95/P99 because downstream code waits for the complete structured result.
- Contract extraction endpoints. Compare TTFA P95 for response start and generation P95 for the slow throughput tail.
- Interactive support copilots. Use E2E P99 with failure rate when this model call sits directly on a user-visible critical path.
Baseten vs Fireworks AI — direct questions
Baseten vs Fireworks AI: which finished P99 responses faster?
Baseten had the lower E2E P99 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.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →