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 ↗ - Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗
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.
| 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.
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.
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_basetokenizer, 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 ↗.
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.
Same run, other questions
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 →