benchmarks/inference/fireworks ai alternatives
fireworks ai alternatives · measured LLM inference APIs · measured Sep 3, 2026

Fireworks AI alternatives, benchmarked

What the benchmark found. Baseten was the lowest-p99 measured alternative at 3.23 seconds end-to-end, compared with 10.49 seconds for Fireworks AI. Baseten's task success rate was 95.8%. 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. 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 end-to-end p99 latency, task success, and slow-tail tokens per second. 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 with the model identifiers below; the links open the provider documentation used to verify each adapter.

[01] measured comparison

Which measured Fireworks AI alternatives are faster or more accurate?

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.

benchmarks/inference/fireworks-ai-alternativesreviewed run
Measured alternatives with deltas from Fireworks AI
Alternative / APIEnd-to-end p99Δ p99 vs Fireworks AITask successΔ successTokens/s p95Δ tokens/s p95
Basetenzai-org/GLM-5.3-Flash ↗3.23 s-7259 ms95.8%-3.7 pp61.6 tok/s+33.8 tok/s
Modalzai-org/GLM-5.3-Flash ↗3.28 s-7209 ms99.3%-0.2 pp84.7 tok/s+56.9 tok/s
Telnyxzai-org/GLM-5.3-Flash ↗3.48 s-7009 ms99.5%0.0 pp67.5 tok/s+39.6 tok/s
Novita AIzai-org/glm-5.3-flash ↗7.25 s-3239 ms94.2%-5.3 pp29.6 tok/s+1.7 tok/s
Z.AIglm-5.3-flash ↗8.59 s-1896 ms97.5%-2.0 pp27.8 tok/s-0.0 tok/s
Parasailzai-org/GLM-5.3-Flash ↗11.5 s+1025 ms98.3%-1.2 pp22.8 tok/s-5.0 tok/s
Together AIzai-org/GLM-5.3-Flash ↗12.4 s+1962 ms98.5%-1.0 pp34.0 tok/s+6.2 tok/s
Nebiuszai-org/GLM-5.3-Flash ↗15.0 s+4483 ms84.8%-14.7 pp78.8 tok/s+50.9 tok/s
DeepInfrazai-org/GLM-5.3-Flash ↗17.1 s+6583 ms95.5%-4.0 pp9.0 tok/s-18.9 tok/s

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.
  • Modal: 574 ms median, 3.28 seconds end-to-end p99, time to first token p95 1.09 seconds, 222.2 tokens per second, 99.3% task success, 0.0% failures.
  • Telnyx: 669 ms median, 3.48 seconds end-to-end p99, time to first token p95 1.34 seconds, 184.3 tokens per second, 99.5% task success, 0.0% failures.
  • Novita AI: 1.74 seconds median, 7.25 seconds end-to-end p99, time to first token p95 3.41 seconds, 60.6 tokens per second, 94.2% task success, 4.0% failures.
  • Z.AI: 1.75 seconds median, 8.59 seconds end-to-end p99, time to first token p95 3.14 seconds, 56.3 tokens per second, 97.5% task success, 0.2% 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.
  • Parasail: 1.59 seconds median, 11.51 seconds end-to-end p99, time to first token p95 5.54 seconds, 52.4 tokens per second, 98.3% task success, 1.3% failures.
  • Together AI: 609 ms median, 12.45 seconds end-to-end p99, time to first token p95 2.26 seconds, 151.6 tokens per second, 98.5% task success, 1.0% failures.
  • Nebius: 1.45 seconds median, 14.97 seconds end-to-end p99, time to first token p95 7.11 seconds, 269.3 tokens per second, 84.8% task success, 14.8% failures.
  • DeepInfra: 1.83 seconds median, 17.07 seconds end-to-end p99, time to first token p95 6.41 seconds, 69.4 tokens per second, 95.5% task success, 4.0% failures.
[02] workflow fit

What should I test before replacing Fireworks AI?

  • Low-latency structured generation. Replay your shortest blocking JSON tasks and compare end-to-end p95 and 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 time to first token for the visible start and tokens per second p95 for slow-tail continuation rather than choosing from median throughput alone.
[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 ↗.
[04] faq

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 end-to-end 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 end-to-end 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.

[05] related comparisons
[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 →

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