Together AI alternatives, benchmarked
What the benchmark found. Baseten was the lowest-p99 measured alternative at 3.23 seconds end-to-end, compared with 12.45 seconds for Together 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. Together 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 Together 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.
- 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 ↗
Which measured Together AI alternatives are faster or more accurate?
The Together 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 | End-to-end p99 | Δ p99 vs Together AI | Task success | Δ success | Tokens/s p95 | Δ tokens/s p95 |
|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 3.23 s | -9221 ms | 95.8% | -2.7 pp | 61.6 tok/s | +27.6 tok/s |
| Modalzai-org/GLM-5.3-Flash ↗ | 3.28 s | -9171 ms | 99.3% | +0.8 pp | 84.7 tok/s | +50.7 tok/s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 3.48 s | -8971 ms | 99.5% | +1.0 pp | 67.5 tok/s | +33.5 tok/s |
| Novita AIzai-org/glm-5.3-flash ↗ | 7.25 s | -5201 ms | 94.2% | -4.3 pp | 29.6 tok/s | -4.4 tok/s |
| Z.AIglm-5.3-flash ↗ | 8.59 s | -3858 ms | 97.5% | -1.0 pp | 27.8 tok/s | -6.2 tok/s |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 10.5 s | -1962 ms | 99.5% | +1.0 pp | 27.9 tok/s | -6.2 tok/s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 11.5 s | -937 ms | 98.3% | -0.2 pp | 22.8 tok/s | -11.2 tok/s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 15.0 s | +2521 ms | 84.8% | -13.7 pp | 78.8 tok/s | +44.8 tok/s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 17.1 s | +4621 ms | 95.5% | -3.0 pp | 9.0 tok/s | -25.1 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.
What should I test before replacing Together AI?
- OpenAI-compatible application traffic. Replay your shortest blocking JSON tasks and compare end-to-end p95 and p99 with Together 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.
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 ↗.
Together AI alternatives: questions unique to this comparison
Which Together AI alternatives reduced complete-response tail latency?
Baseten, Modal, Telnyx, Novita AI, Z.AI, Fireworks AI, and Parasail have a lower end-to-end p99 than Together AI's 12.45 seconds in this run.
What quality trade-off appears when moving away from Together AI?
Modal, Telnyx, and Fireworks AI improved both end-to-end p99 and task success relative to Together AI on this workload.
Which Together AI model key was exercised in the benchmark?
Together AI was called with model key zai-org/GLM-5.3-Flash, streaming enabled, low reasoning, deterministic sampling, a 256-token ceiling, and one request in flight.
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 →