Fastest GLM 5.3 Flash API provider
What the benchmark found. Baseten recorded the lowest end-to-end p99 latency at 3.23 seconds on this run, with a median of 331 ms, time to first token p95 of 1.16 seconds, 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. 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. 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 end-to-end p99 when slow-edge complete-response latency is the requirement. Read end-to-end 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 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 GLM 5.3 Flash endpoint has the lowest end-to-end 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.
| Provider | End-to-end p50 | End-to-end p95 | End-to-end p99 | Δ vs fastest p99 | Task success | Failure rate |
|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 331 ms | 2.29 s | 3.23 s | 0 ms | 95.8% | 0.0% |
| Modalzai-org/GLM-5.3-Flash ↗ | 574 ms | 1.90 s | 3.28 s | +50 ms | 99.3% | 0.0% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 669 ms | 2.33 s | 3.48 s | +250 ms | 99.5% | 0.0% |
| Novita AIzai-org/glm-5.3-flash ↗ | 1.74 s | 5.00 s | 7.25 s | +4020 ms | 94.2% | 4.0% |
| Z.AIglm-5.3-flash ↗ | 1.75 s | 4.83 s | 8.59 s | +5362 ms | 97.5% | 0.2% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 1.95 s | 6.72 s | 10.5 s | +7259 ms | 99.5% | 0.0% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 1.59 s | 7.74 s | 11.5 s | +8283 ms | 98.3% | 1.3% |
| Together AIzai-org/GLM-5.3-Flash ↗ | 609 ms | 3.66 s | 12.4 s | +9221 ms | 98.5% | 1.0% |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 1.45 s | 7.45 s | 15.0 s | +11742 ms | 84.8% | 14.8% |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 1.83 s | 11.0 s | 17.1 s | +13842 ms | 95.5% | 4.0% |
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.
Where does GLM endpoint latency affect the product?
- Inline extraction. Use end-to-end 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 time to first token when users see partial content; complete-response latency still controls when downstream code can proceed.
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 ↗.
GLM 5.3 Flash latency questions
Which provider had the fastest GLM 5.3 Flash end-to-end p99?
Baseten recorded the lowest end-to-end p99 latency at 3.23 seconds on this run, with a median of 331 ms, time to first token p95 of 1.16 seconds, 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 median end-to-end latency at 331 ms, while Baseten had the lowest end-to-end 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 end-to-end latency value. They stay in the submitted-request denominator for failure rate and task success, which must be read beside the latency columns.
Same run, other questions
- LLM inference provider benchmark: complete scorecard and methodology →
- GLM 5.3 Flash API providers compared: latency, tokens per second and accuracy →
- Most accurate GLM 5.3 Flash provider →
- Best LLM inference provider: speed, accuracy and failures compared →
- Fireworks AI vs Together AI →
- Fastest LLM inference provider: time to first token, tokens per second and end-to-end latency →
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 →