Fireworks AI vs Together AI
What the benchmark found. Fireworks AI had the lower E2E P99 at 10.49 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. Fireworks AI and Together AI expose different model identifiers but were held to one logical inference contract. This direct comparison shows where each led and the size of the measured gap.
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. Fireworks AI and Together AI used 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 ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗
Fireworks AI and Together 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 | Fireworks AI | Together AI | Measured leader | Absolute gap |
|---|---|---|---|---|
| E2E P99 | 10.5 s | 12.4 s | Fireworks AI | 1962 ms |
| E2E P95 | 6.72 s | 3.66 s | Together AI | 3053 ms |
| TTFA P95 | 5.83 s | 2.26 s | Together AI | 3565 ms |
| Generation P50 | 58.2 tok/s | 151.6 tok/s | Together AI | 93.4 tok/s |
| Generation P95 | 27.9 tok/s | 34.0 tok/s | Together AI | 6.2 tok/s |
| Task success | 99.5% | 98.5% | Fireworks AI | 1.0 pp |
| Failure rate | 0.0% | 1.0% | Fireworks AI | 1.0 pp |
Where to compare Fireworks AI with Together AI
- Schema-constrained API responses. Start with task success, then compare E2E P95/P99 because downstream code waits for the complete structured result.
- Streamed copilot output. Compare TTFA P95 for response start and generation P95 for the slow throughput tail.
- Latency-sensitive extraction. Use E2E P99 with failure rate when this model call sits directly on a user-visible critical path.
Fireworks AI vs Together AI — direct questions
Fireworks AI vs Together AI: who led on E2E P99?
Fireworks AI had the lower E2E P99 at 10.49 seconds. Fireworks AI had the higher task success rate at 99.5%. Both figures come from 600 submitted requests per provider.
Which API had the stronger slow-tail generation speed?
Together AI had the stronger generation P95 at 34.0 tokens/second. P95 is the throughput floor reached by at least 95% of measured responses.
Were Fireworks and Together evaluated with identical reasoning settings?
Yes. Both providers received the same logical low-reasoning setting, plus identical streaming, sampling, output-limit, task, and concurrency settings. Their provider-specific model identifiers are linked in the hero.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →