Fireworks AI alternatives, benchmarked
What the benchmark found. Baseten was the lowest-P99 measured alternative at 3.23 seconds, compared with 10.49 seconds for Fireworks AI. Baseten's task success rate was 95.8%.
What this page compares. Fireworks AI is compared with every other provider that served the same GLM 5.3 Flash microtask workload. This is not a feature-directory list: each alternative received the same 600 questions under the same streaming, reasoning, sampling, timeout, and concurrency contract.
How to read the result. The table expresses every alternative as a delta from Fireworks AI on E2E P99, task success, and slow-tail token generation speed. Positive and negative deltas retain their natural direction, so each column states whether lower or higher is better.
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, Modal, Telnyx, Novita AI, Z.AI, Fireworks AI, Parasail, Together AI, Nebius, and DeepInfra 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 ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Novita AI:
zai-org/glm-5.3-flash· API docs ↗ - Z.AI:
glm-5.3-flash· API docs ↗ - Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - Nebius:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗
How measured Fireworks AI alternatives differ from Fireworks AI
The Fireworks AI row is the baseline and is not repeated in the alternatives table. Compare the deltas before treating a faster provider as a substitute: an alternative can gain latency while losing successful-result yield.
| Alternative / API | E2E P99 | Δ P99 vs Fireworks AI | Task success | Δ success | Generation P95 | Δ P95 |
|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 3.23 s | -7259 ms | 95.8% | -3.7 pp | 61.6 tok/s | +33.8 tok/s |
| Modalzai-org/GLM-5.3-Flash ↗ | 3.28 s | -7209 ms | 99.3% | -0.2 pp | 84.7 tok/s | +56.9 tok/s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 3.48 s | -7009 ms | 99.5% | 0.0 pp | 67.5 tok/s | +39.6 tok/s |
| Novita AIzai-org/glm-5.3-flash ↗ | 7.25 s | -3239 ms | 94.2% | -5.3 pp | 29.6 tok/s | +1.7 tok/s |
| Z.AIglm-5.3-flash ↗ | 8.59 s | -1896 ms | 97.5% | -2.0 pp | 27.8 tok/s | -0.0 tok/s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 11.5 s | +1025 ms | 98.3% | -1.2 pp | 22.8 tok/s | -5.0 tok/s |
| Together AIzai-org/GLM-5.3-Flash ↗ | 12.4 s | +1962 ms | 98.5% | -1.0 pp | 34.0 tok/s | +6.2 tok/s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 15.0 s | +4483 ms | 84.8% | -14.7 pp | 78.8 tok/s | +50.9 tok/s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 17.1 s | +6583 ms | 95.5% | -4.0 pp | 9.0 tok/s | -18.9 tok/s |
Workflows to test before replacing Fireworks AI
- Low-latency structured generation. Replay your shortest blocking JSON tasks and compare E2E P95/P99 with Fireworks AI; this benchmark's ticket-triage family is the closest measured proxy.
- Contract and document extraction. Check exact task success before migrating. The benchmark requires complete typed contract objects, so it exposes substitutions that are fast but less dependable.
- Streamed assistant responses. Compare TTFA for the visible start and generation P95 for slow-tail continuation rather than choosing from median throughput alone.
Fireworks AI alternatives — questions unique to this comparison
Which Fireworks AI alternatives delivered a faster P99 response?
Baseten, Modal, Telnyx, Novita AI, and Z.AI have a lower E2E P99 than Fireworks AI's 10.49 seconds in this run.
Did any Fireworks alternative improve both accuracy and latency?
No measured alternative improved both E2E P99 and task success relative to Fireworks AI on this workload.
How was the Fireworks GLM endpoint configured for this comparison?
Fireworks AI was called with model key accounts/fireworks/models/glm-5p3-flash, streaming enabled, low reasoning, deterministic sampling, a 256-token ceiling, and one request in flight.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →