benchmarks/inference/baseten alternatives
baseten alternatives · measured LLM inference platforms · measured Sep 3, 2026

Baseten alternatives, benchmarked

What the benchmark found. Modal was the lowest-p99 measured alternative at 3.28 seconds end-to-end, compared with 3.23 seconds for Baseten. Modal's task success rate was 99.3%. 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. Baseten 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 Baseten 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 Baseten alternatives are faster or more accurate?

The Baseten 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/baseten-alternativesreviewed run
Measured alternatives with deltas from Baseten
Alternative / APIEnd-to-end p99Δ p99 vs BasetenTask successΔ successTokens/s p95Δ tokens/s p95
Modalzai-org/GLM-5.3-Flash ↗3.28 s+50 ms99.3%+3.5 pp84.7 tok/s+23.1 tok/s
Telnyxzai-org/GLM-5.3-Flash ↗3.48 s+250 ms99.5%+3.7 pp67.5 tok/s+5.9 tok/s
Novita AIzai-org/glm-5.3-flash ↗7.25 s+4020 ms94.2%-1.7 pp29.6 tok/s-32.1 tok/s
Z.AIglm-5.3-flash ↗8.59 s+5362 ms97.5%+1.7 pp27.8 tok/s-33.8 tok/s
Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗10.5 s+7259 ms99.5%+3.7 pp27.9 tok/s-33.8 tok/s
Parasailzai-org/GLM-5.3-Flash ↗11.5 s+8283 ms98.3%+2.5 pp22.8 tok/s-38.8 tok/s
Together AIzai-org/GLM-5.3-Flash ↗12.4 s+9221 ms98.5%+2.7 pp34.0 tok/s-27.6 tok/s
Nebiuszai-org/GLM-5.3-Flash ↗15.0 s+11742 ms84.8%-11.0 pp78.8 tok/s+17.2 tok/s
DeepInfrazai-org/GLM-5.3-Flash ↗17.1 s+13842 ms95.5%-0.3 pp9.0 tok/s-52.7 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 Baseten?

  • Real-time structured routing. Replay your shortest blocking JSON tasks and compare end-to-end p95 and p99 with Baseten; 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

Baseten alternatives: questions unique to this comparison

Which measured alternatives beat Baseten on end-to-end p99?

No measured alternative had a lower end-to-end p99 than Baseten's 3.23 seconds in this run.

How does Baseten's task success compare with lower-latency options?

No measured alternative improved both end-to-end p99 and task success relative to Baseten on this workload.

Can a Baseten-compatible chat-completions client use this ranking directly?

The measured alternatives expose OpenAI-compatible chat-completions surfaces, but authentication, base URLs, and model identifiers differ. Treat the ranking as a shortlist, then validate your Baseten client against the linked API documentation before migrating.

[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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