Best inference provider for GLM 5.3 Flash
What the benchmark found. Baseten led E2E P99 at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. All compared endpoints were configured for GLM 5.3 Flash with streaming enabled, reasoning low, and concurrency one.
What this page compares. Every row delivers the same requested GLM 5.3 Flash model under one logical request contract, allowing the provider layer to be compared on latency, streaming behavior, exact task success, and operational reliability.
How to read the result. There is no honest single best provider across unlike units. Start with the requirement that matters for your GLM workload, reject rows that miss your task-success or failure threshold, and compare E2E P99 among the providers left.
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.
- 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 ↗
GLM 5.3 Flash providers across the production decision metrics
This focused scorecard adds deltas from both the latency leader and accuracy leader. It includes response start, slow-tail generation, and failure rate without reproducing every percentile from the main benchmark.
| Provider / API | E2E P99 | Δ vs fastest | TTFA P95 | Generation P95 | Task success | Δ vs most accurate | Failure rate |
|---|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 3.23 s | 0 ms | 1.16 s | 61.6 tok/s | 95.8% | -3.7 pp | 0.0% |
| Modalzai-org/GLM-5.3-Flash ↗ | 3.28 s | +50 ms | 1.09 s | 84.7 tok/s | 99.3% | -0.2 pp | 0.0% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 3.48 s | +250 ms | 1.34 s | 67.5 tok/s | 99.5% | 0.0 pp | 0.0% |
| Novita AIzai-org/glm-5.3-flash ↗ | 7.25 s | +4020 ms | 3.41 s | 29.6 tok/s | 94.2% | -5.3 pp | 4.0% |
| Z.AIglm-5.3-flash ↗ | 8.59 s | +5362 ms | 3.14 s | 27.8 tok/s | 97.5% | -2.0 pp | 0.2% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 10.5 s | +7259 ms | 5.83 s | 27.9 tok/s | 99.5% | 0.0 pp | 0.0% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 11.5 s | +8283 ms | 5.54 s | 22.8 tok/s | 98.3% | -1.2 pp | 1.3% |
| Together AIzai-org/GLM-5.3-Flash ↗ | 12.4 s | +9221 ms | 2.26 s | 34.0 tok/s | 98.5% | -1.0 pp | 1.0% |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 15.0 s | +11742 ms | 7.11 s | 78.8 tok/s | 84.8% | -14.7 pp | 14.8% |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 17.1 s | +13842 ms | 6.41 s | 9.0 tok/s | 95.5% | -4.0 pp | 4.0% |
GLM 5.3 Flash workloads this comparison can inform
- Short structured generation. Use task success and E2E P99 for small JSON responses that block application code.
- Streamed user interfaces. Use TTFA P95 for the visible start and generation P95 for the slow throughput floor.
- Provider redundancy. Compare model identifiers, failure rate, and output behavior before treating two GLM endpoints as interchangeable failover targets.
Selecting a GLM 5.3 Flash provider
Does one provider lead every GLM 5.3 Flash benchmark metric?
Baseten led E2E P99 at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. All compared endpoints were configured for GLM 5.3 Flash with streaming enabled, reasoning low, and concurrency one.
How should I choose a GLM 5.3 Flash host from this scorecard?
There is no honest single best provider across unlike units. Start with the requirement that matters for your GLM workload, reject rows that miss your task-success or failure threshold, and compare E2E P99 among the providers left.
What does the focused GLM provider table add to the main benchmark?
This focused scorecard adds deltas from both the latency leader and accuracy leader. It includes response start, slow-tail generation, and failure rate without reproducing every percentile from the main benchmark.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →