benchmarks/inference/fastest inference provider for glm 5 3 flash
GLM 5.3 Flash latency · P99 comparison

Fastest inference provider for GLM 5.3 Flash

What the benchmark found. Baseten recorded the lowest E2E P99 at 3.23 seconds on this run, with E2E P50 of 331 ms, task success of 95.8%, and failure rate of 0.0%. This is a provider-layer latency result for one fixed GLM 5.3 Flash configuration.

What this page compares. This view isolates the delivery question for GLM 5.3 Flash: which provider completes the same short structured workload with the lowest client-observed tail latency? It does not substitute accelerator specifications or vendor-reported throughput for measured requests.

How to read the result. Use E2E P99 when slow-edge complete-response latency is the requirement. Read E2E P50 for a typical response and failure rate for requests that never produced a valid completion timestamp; the benchmark does not designate one universal headline metric.

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. Baseten, Modal, Telnyx, Novita AI, Z.AI, Fireworks AI, Parasail, Together AI, Nebius, and DeepInfra used the model identifiers below; the links open the provider documentation used to verify each adapter.

GLM 5.3 Flash endpoints sorted by E2E P99

The delta column shows each endpoint's additional P99 wait versus the fastest measured GLM provider. Accuracy and operational failures remain beside latency to prevent incomplete service from looking artificially fast.

benchmarks/inference/fastest-inference-provider-for-glm-5-3-flashreviewed run
Inference providers sorted by E2E P99 latency
Provider E2E P50E2E P95E2E P99Δ vs fastest P99Task successFailure rate
Basetenzai-org/GLM-5.3-Flash331 ms2.29 s3.23 s0 ms95.8%0.0%
Modalzai-org/GLM-5.3-Flash574 ms1.90 s3.28 s+50 ms99.3%0.0%
Telnyxzai-org/GLM-5.3-Flash669 ms2.33 s3.48 s+250 ms99.5%0.0%
Novita AIzai-org/glm-5.3-flash1.74 s5.00 s7.25 s+4020 ms94.2%4.0%
Z.AIglm-5.3-flash1.75 s4.83 s8.59 s+5362 ms97.5%0.2%
Fireworks AIaccounts/fireworks/models/glm-5p3-flash1.95 s6.72 s10.5 s+7259 ms99.5%0.0%
Parasailzai-org/GLM-5.3-Flash1.59 s7.74 s11.5 s+8283 ms98.3%1.3%
Together AIzai-org/GLM-5.3-Flash609 ms3.66 s12.4 s+9221 ms98.5%1.0%
Nebiuszai-org/GLM-5.3-Flash1.45 s7.45 s15.0 s+11742 ms84.8%14.8%
DeepInfrazai-org/GLM-5.3-Flash1.83 s11.0 s17.1 s+13842 ms95.5%4.0%

Where GLM endpoint latency affects the product

  • Inline extraction. Use E2E P99 when the interface cannot populate a structured record until the complete GLM response arrives.
  • Classification before routing. Use P95 for ordinary SLA planning and P99 for the rare waits that hold the next system action.
  • Visible streamed output. Add TTFA when users see partial content; complete-response latency still controls when downstream code can proceed.

GLM 5.3 Flash latency questions

Which provider had the fastest GLM 5.3 Flash E2E P99?

Baseten recorded the lowest E2E P99 at 3.23 seconds on this run, with E2E P50 of 331 ms, task success of 95.8%, and failure rate of 0.0%. This is a provider-layer latency result for one fixed GLM 5.3 Flash configuration.

Did the same GLM host lead typical and tail completion latency?

Baseten had the lowest E2E P50 at 331 ms, while Baseten had the lowest E2E P99 at 3.23 seconds. The same provider led both points of the distribution.

How are unsuccessful GLM 5.3 Flash requests handled in latency results?

Timeouts, HTTP errors, and transport failures have no completed visible answer and therefore no honest E2E latency value. They stay in the submitted-request denominator for failure rate and task success, which must be read beside the latency columns.

Read the complete inference benchmark

Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →