Best inference provider: accuracy and latency
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.
What this page compares. There is no honest single winner when one workflow values a complete response at P99 and another values exact task success. This page keeps the benchmark's complete provider table together so latency, streaming behavior, accuracy, and operational failures can be read as one scorecard.
How to read the result. There is no single headline metric. Reject any provider that misses your task-success or failure-rate requirement, then select the relevant latency measures. For an interactive interface, add TTFA and generation speed; for an automated structured pipeline, task success should be the first gate.
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 ↗
The complete inference provider scorecard
This is the one SEO page intentionally carrying the full benchmark table. Every other intent, alternative, and comparison page uses a narrower projection or a computed delta.
| Provider | End-to-end latency | Time to first output token | Time to first visible answer token | Token generation speed | Task success | Failure rate | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| P50 | P95 | P99 | P50 | P95 | P99 | P50 | P95 | P99 | P50 | P95 | P99 | |||
| Basetenzai-org/GLM-5.3-Flash ↗ | 331 ms | 2.29 s | 3.23 s | 228 ms | 499 ms | 946 ms | 287 ms | 1.16 s | 2.65 s | 240.5 tok/s | 61.6 tok/s | 22.6 tok/s | 95.8% | 0.0%no operational failures |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 1.83 s | 11.0 s | 17.1 s | 808 ms | 3.78 s | 9.08 s | 1.15 s | 6.41 s | 13.2 s | 69.4 tok/s | 9.0 tok/s | 5.0 tok/s | 95.5% | 4.0%429 0.3% · transport 3.7% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 1.95 s | 6.72 s | 10.5 s | 586 ms | 5.67 s | 8.74 s | 1.06 s | 5.83 s | 8.74 s | 58.2 tok/s | 27.9 tok/s | 14.9 tok/s | 99.5% | 0.0%no operational failures |
| Modalzai-org/GLM-5.3-Flash ↗ | 574 ms | 1.90 s | 3.28 s | 376 ms | 812 ms | 2.72 s | 470 ms | 1.09 s | 2.79 s | 222.2 tok/s | 84.7 tok/s | 34.9 tok/s | 99.3% | 0.0%no operational failures |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 1.45 s | 7.45 s | 15.0 s | 925 ms | 7.11 s | 14.6 s | 1.14 s | 7.11 s | 14.6 s | 269.3 tok/s | 78.8 tok/s | 39.9 tok/s | 84.8% | 14.8%timeout 2.8% · other 4xx 11.5% · transport 0.5% |
| Novita AIzai-org/glm-5.3-flash ↗ | 1.74 s | 5.00 s | 7.25 s | 1.13 s | 1.79 s | 4.28 s | 1.40 s | 3.41 s | 5.58 s | 60.6 tok/s | 29.6 tok/s | 11.6 tok/s | 94.2% | 4.0%timeout 0.2% · 429 3.8% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 1.59 s | 7.74 s | 11.5 s | 856 ms | 4.96 s | 10.9 s | 1.11 s | 5.54 s | 10.9 s | 52.4 tok/s | 22.8 tok/s | 15.6 tok/s | 98.3% | 1.3%timeout 0.8% · 429 0.2% · transport 0.3% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 669 ms | 2.33 s | 3.48 s | 497 ms | 802 ms | 2.90 s | 574 ms | 1.34 s | 3.00 s | 184.3 tok/s | 67.5 tok/s | 47.3 tok/s | 99.5% | 0.0%no operational failures |
| Together AIzai-org/GLM-5.3-Flash ↗ | 609 ms | 3.66 s | 12.4 s | 377 ms | 2.23 s | 12.2 s | 393 ms | 2.26 s | 12.2 s | 151.6 tok/s | 34.0 tok/s | 21.7 tok/s | 98.5% | 1.0%timeout 0.3% · 429 0.5% · 5xx 0.2% |
| Z.AIglm-5.3-flash ↗ | 1.75 s | 4.83 s | 8.59 s | 1.05 s | 1.89 s | 4.76 s | 1.39 s | 3.14 s | 5.00 s | 56.3 tok/s | 27.8 tok/s | 11.5 tok/s | 97.5% | 0.2%timeout 0.2% |
Rows are listed alphabetically. Generation-speed P95 and P99 are coverage-based slow-tail floors: 95% and 99% of measured responses respectively generated at least that fast.
How to choose a provider from the full scorecard
- Latency-first product. Set an acceptable task-success floor, then compare E2E P95 and P99 among the providers that clear it.
- Accuracy-first automation. Sort mentally by task success, inspect failure composition, and use completion latency only after reliability is acceptable.
- Streaming user interface. Read TTFA for the visible start, generation P95 for slow-tail throughput, and E2E P99 for the complete response.
Balancing inference accuracy and latency
Is the lowest-latency inference provider also the most accurate?
Baseten led E2E P99 at 3.23 seconds, while Telnyx and Fireworks AI tied for the task-success lead at 99.5%. The results are reported separately because the benchmark does not average unlike units into a synthetic score.
Which three metrics should a real-time structured application combine?
For a real-time structured application, combine E2E P99, task success rate, and operational failure rate. Add TTFA and generation P95 when users consume the answer while it streams.
Why does this page keep the complete benchmark table?
This page serves the broad accuracy-and-latency decision and therefore preserves the complete scorecard. Narrow intent pages use derived deltas or smaller metric projections so they add analysis instead of duplicating this table.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →