GLM-5.3-Flash Benchmarks

Developers choosing an inference provider care about how fast the model keeps generating once it starts, and whether the request still completes when the prompt gets large. Telnyx Inference is built around that production path: OpenAI-compatible access to models like GLM-5.3-Flash on globally deployed GPUs, without asking developers to manage serving infrastructure.

To test how that looks in practice, we benchmarked GLM-5.3-Flash across Telnyx, Baseten, Together AI, and Fireworks using the same harness and prompt profiles we have been using for recent provider comparisons. The practical question is what happens when prompt size and output size change: where does each provider stay responsive, where does generation speed hold up, and where do requests fail.

For the Telnyx run, benchmark requests were initiated from a Telnyx stateful actor. The same actor also issued the competitor API calls, which keeps the orchestration path consistent. Telnyx used zai-org/GLM-5.3-Flash. Baseten and Together AI used the same model ID. Fireworks used accounts/fireworks/models/glm-5p3-flash.

What stood out when we benchmarked GLM-5.3-Flash

Telnyx had the strongest generation-speed result in this run: 196.4 output tokens/sec p50, ahead of Baseten at 190.1 tok/s, Fireworks at 127.9 tok/s, and Together AI at 102.4 tok/s.

Many real inference workloads are not one-sentence responses. Once the model starts generating, throughput controls how quickly a longer answer, summary, extraction, or agent response finishes.

Reliable completion across the full benchmark

Latency numbers are only useful when the request succeeds. Telnyx completed all 60 benchmark runs with zero failed rows. Together AI and Fireworks also completed all 60 rows. Baseten completed 44 rows and returned 16 HTTP 429 rate-limit errors, all on the 100k-input profiles.

That does not mean Baseten is slow. It means the large-context portion of this run was incomplete for Baseten, so its latency results should be read with that caveat.

Benchmarked from a stateful actor

The Telnyx benchmark traffic ran from a stateful actor, not an external client. The actor issued all competitor API calls from the same runtime, which keeps the orchestration path consistent and removes client-side variability from the comparison.

Stateful actors run on Telnyx edge compute, colocated with the inference infrastructure. That means the benchmark requests to Telnyx Inference did not cross the public internet. Requests to Baseten, Together AI, and Fireworks did.

The developer takeaway from GLM-5.3-Flash benchmarking

The strong signal from this run is generation speed plus completion reliability. Telnyx delivered the highest p50 throughput at 196.4 tok/s and completed every benchmark row without failure.

For agentic and workflow-heavy apps, what matters is whether the model keeps generating quickly and whether the request completes when the prompt gets large. This run suggests both hold on Telnyx Inference.

To build against these models directly, visit Telnyx Inference and start with the OpenAI-compatible API path. See inference pricing for per-token costs.

Methodology

• Run date: September 2, 2026
• Model scope: GLM-5.3-Flash
• Providers: Telnyx, Baseten, Together AI, Fireworks
• Prompt profiles: 1k / ~100, 10k / ~100, 100k / ~100, 1k / ~1k, 10k / ~1k, 100k / ~1k
• Repeats: 10 per provider/profile
• Total attempted rows: 240
• Successful rows used in percentile calculations: 224
• Failed rows excluded from latency and throughput percentiles: 16
• Timing method: streamed responses with E2E latency, TTFT, and effective output tokens/sec captured by the benchmark harness
• Telnyx execution path: benchmark traffic initiated from a Telnyx stateful actor using the regional us-east-2 invoke URL

Caveats

Baseten returned 16 HTTP 429 rate-limit failures: 6 on 100k / ~100 and 10 on 100k / ~1k. Those rows were excluded from percentile calculations.

Telnyx throughput was calculated from non-stream token-usage probes for 59 of 60 successful Telnyx rows because streamed usage was not available for those rows. E2E and TTFT measurements were not affected by that token-usage probe.

The actor invoke region is known, but the final GPU-serving region for each provider was not independently logged. These results should be treated as a point-in-time benchmark, not an SLA.

References

• Telnyx GLM-5.3-Flash release note: https://telnyx.com/release-notes/glm-5-3-flash-inference
• Telnyx stateful actors docs: https://developers.telnyx.com/docs/edge-compute/stateful-actors
• Baseten GLM-5.3-Flash library page: https://www.baseten.co/library/glm-53-flash/
• Together AI GLM-5.3-Flash page: https://www.together.ai/models/glm-5-3-flash
• Fireworks GLM-5.3-Flash page: https://fireworks.ai/models/fireworks/glm-5p3-flash