---
title: "Together AI Alternatives 2026: APIs Compared | Openbenchmarks"
description: "Together AI alternatives for LLM inference: 9 providers on the same workload as deltas from Together AI on p99 latency, accuracy and tokens/s. Open source."
url: https://openbenchmarks.com/inference/together-ai-alternatives
---

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together ai alternatives · measured LLM inference APIs · measured Sep 3, 2026

# 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 ↗](https://docs.baseten.co/reference/inference-api/chat-completions)
* **Modal:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://modal.com/docs/guide/endpoints#calling-your-endpoint)
* **Telnyx:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://developers.telnyx.com/api-reference/openai-chat/create-a-chat-completion-openai-compatible)
* **Novita AI:** `zai-org/glm-5.3-flash` · [API docs ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion)
* **Z.AI:** `glm-5.3-flash` · [API docs ↗](https://docs.z.ai/api-reference/llm/chat-completion)
* **Fireworks AI:** `accounts/fireworks/models/glm-5p3-flash` · [API docs ↗](https://docs.fireworks.ai/api-reference/post-chatcompletions)
* **Parasail:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.parasail.io/parasail-docs/api-reference/chat-completions)
* **Together AI:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.together.ai/docs/inference/chat/overview)
* **Nebius:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion)
* **DeepInfra:** `zai-org/GLM-5.3-Flash` · [API docs ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions)

\[01\] measured comparison

## 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.

benchmarks/inference/together-ai-alternatives reviewed run 

__Measured alternatives with deltas from Together AI__
| Alternative / API | End-to-end p99 | Δ p99 vs Together AI | Task success | Δ success | Tokens/s p95 | Δ tokens/s p95 |
| --- | --- | --- | --- | --- | --- | --- |
| [Baseten](https://www.baseten.co "Open Baseten in a new tab") [zai-org/GLM-5.3-Flash ↗](https://docs.baseten.co/reference/inference-api/chat-completions) | 3.23 s | \-9221 ms | 95.8% | \-2.7 pp | 61.6 tok/s | +27.6 tok/s |
| [Modal](https://modal.com/ "Open Modal in a new tab") [zai-org/GLM-5.3-Flash ↗](https://modal.com/docs/guide/endpoints#calling-your-endpoint) | 3.28 s | \-9171 ms | 99.3% | +0.8 pp | 84.7 tok/s | +50.7 tok/s |
| [Telnyx](https://telnyx.com "Open Telnyx in a new tab") [zai-org/GLM-5.3-Flash ↗](https://developers.telnyx.com/api-reference/openai-chat/create-a-chat-completion-openai-compatible) | 3.48 s | \-8971 ms | 99.5% | +1.0 pp | 67.5 tok/s | +33.5 tok/s |
| Novita AI [zai-org/glm-5.3-flash ↗](https://novita.ai/docs/api-reference/model-apis-llm-create-chat-completion) | 7.25 s | \-5201 ms | 94.2% | \-4.3 pp | 29.6 tok/s | \-4.4 tok/s |
| Z.AI [glm-5.3-flash ↗](https://docs.z.ai/api-reference/llm/chat-completion) | 8.59 s | \-3858 ms | 97.5% | \-1.0 pp | 27.8 tok/s | \-6.2 tok/s |
| Fireworks AI [accounts/fireworks/models/glm-5p3-flash ↗](https://docs.fireworks.ai/api-reference/post-chatcompletions) | 10.5 s | \-1962 ms | 99.5% | +1.0 pp | 27.9 tok/s | \-6.2 tok/s |
| Parasail [zai-org/GLM-5.3-Flash ↗](https://docs.parasail.io/parasail-docs/api-reference/chat-completions) | 11.5 s | \-937 ms | 98.3% | \-0.2 pp | 22.8 tok/s | \-11.2 tok/s |
| Nebius [zai-org/GLM-5.3-Flash ↗](https://docs.tokenfactory.nebius.com/api-reference/inference/create-chat-completion) | 15.0 s | +2521 ms | 84.8% | \-13.7 pp | 78.8 tok/s | +44.8 tok/s |
| DeepInfra [zai-org/GLM-5.3-Flash ↗](https://docs.deepinfra.com/api-reference/chat-completions/openai-chat-completions) | 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.

\[02\] workflow fit

## 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.

\[03\] 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 ↗](https://github.com/openbenchmarks-labs/inference).

\[04\] faq

## 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.

\[05\] related comparisons

## Same run, other questions

* [LLM inference provider benchmark: complete scorecard and methodology →](https://openbenchmarks.com/inference)
* [Baseten vs Fireworks AI →](https://openbenchmarks.com/inference/baseten-vs-fireworks-ai)
* [Baseten vs Together AI →](https://openbenchmarks.com/inference/baseten-vs-together-ai)
* [Fireworks AI vs Together AI →](https://openbenchmarks.com/inference/fireworks-ai-vs-together-ai)
* [DeepInfra vs Together AI →](https://openbenchmarks.com/inference/deepinfra-vs-together-ai)
* [Baseten alternatives, benchmarked →](https://openbenchmarks.com/inference/baseten-alternatives)
* [Fireworks AI alternatives, benchmarked →](https://openbenchmarks.com/inference/fireworks-ai-alternatives)

\[06\] 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 →](https://openbenchmarks.com/inference) 

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