Best LLM inference provider: speed, accuracy and failures compared
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. 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. 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, tokens per second, 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 time to first token and tokens per second; 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 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 LLM inference provider is best on speed, and which on accuracy?
This is the one 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.
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.
How do I choose an LLM inference provider from the full scorecard?
- Latency-first product. Set an acceptable task-success floor, then compare end-to-end 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 time to first token for the visible start, tokens per second p95 for slow-tail throughput, and end-to-end p99 for the complete response.
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 ↗.
Balancing inference accuracy and latency
Is the lowest-latency LLM inference provider also the most accurate?
Baseten led end-to-end p99 latency 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 end-to-end p99 latency, task success rate, and operational failure rate. Add time to first token and tokens per second 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.
How was LLM inference provider latency measured here?
One client sent identical streaming chat-completions requests to each provider's serverless endpoint with concurrency one, temperature 0, reasoning low, a 256-token completion ceiling and a 20-second timeout. Time to first token, end-to-end latency and tokens per second are taken from the client's own clock and a pinned tokenizer; task success is scored against a fixed rubric; failures are counted, never dropped.
Same run, other questions
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 →