GLM 5.3 Flash API providers compared: latency, tokens per second and accuracy
What the benchmark found. Baseten led end-to-end p99 latency at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The scorecard keeps latency, tokens per second, 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. 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. Every row delivers the same requested GLM 5.3 Flash model, Zhipu's (Z.AI) open-weight release, under one logical request contract, so the provider layer can be compared on latency, tokens per second, exact task success, and operational reliability. Z.AI's own API is one of the measured rows.
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 end-to-end 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 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 providers host GLM 5.3 Flash, and how do they compare?
This focused scorecard adds deltas from both the latency leader and accuracy leader. It includes time to first token, slow-tail tokens per second, and failure rate without reproducing every percentile from the main benchmark.
| Provider / API | End-to-end p99 | Δ vs fastest | TTFT p95 | Tokens/s 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% |
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.
Which GLM 5.3 Flash workloads can this comparison inform?
- Short structured generation. Use task success and end-to-end p99 for small JSON responses that block application code.
- Streamed user interfaces. Use time to first token p95 for the visible start and tokens per second 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.
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 ↗.
Selecting a GLM 5.3 Flash provider
Does one provider lead every GLM 5.3 Flash benchmark metric?
Baseten led end-to-end p99 latency at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The scorecard keeps latency, tokens per second, 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 end-to-end 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 time to first token, slow-tail tokens per second, and failure rate without reproducing every percentile from the main benchmark.
Which provider serves GLM 5.3 Flash fastest?
Read the end-to-end p99 column for the complete-answer tail and the tokens per second column for streaming speed; the leaders can differ. The fastest GLM 5.3 Flash API for an application that waits on a complete JSON object is the lowest p99 row that also clears its task-success requirement.
Which GLM 5.3 Flash provider is the most accurate?
Read the task success column. Every provider received the same 600 prompts and the same requested model; the column counts only schema-valid, exactly correct answers, so a difference between rows is a provider-layer difference, not a model difference.
Same run, other questions
- LLM inference provider benchmark: complete scorecard and methodology →
- Fastest GLM 5.3 Flash API provider →
- 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 →