Best LLM inference provider for AI agents: tool calling latency 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. For AI agents, the measured result is most relevant when a short model call blocks the next tool or workflow step. 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. An AI agent experiences inference as a blocking dependency: it sends a prompt, waits for a usable result, and only then decides whether to call a tool or continue. This page applies the complete benchmark scorecard to that decision instead of selecting a provider from advertised throughput alone.
How to read the result. For tool-calling agents, set a task-success floor first and compare end-to-end p95 and p99 among providers that clear it. For agents that stream prose to a user, add time to first token p95 and tokens per second p95. Failure rate remains a separate operational gate in both cases.
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 for AI agents on latency and exact results?
The full table is retained on this page because agent architectures can be blocked by first-token delay, complete-result latency, slow streaming, wrong structured output, or an API failure. No synthetic score hides those trade-offs.
| 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.
Which AI agent workflows does this benchmark represent?
- Tool selection and routing. Ticket triage approximates an agent choosing a category and priority before dispatching the next action; read exact task success with end-to-end p99.
- Retrieval-result interpretation. Meeting-note lookup approximates an agent extracting one grounded fact from supplied context without a second model turn.
- Structured tool arguments. Contract extraction approximates producing a typed object that downstream code must accept without repair or human intervention.
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 ↗.
Choosing LLM inference for an AI agent
Does one inference provider lead both AI-agent latency and exact task success?
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. For AI agents, the measured result is most relevant when a short model call blocks the next tool or workflow step.
Which benchmark metrics should an AI agent team combine?
For tool-calling agents, set a task-success floor first and compare end-to-end p95 and p99 among providers that clear it. For agents that stream prose to a user, add time to first token p95 and tokens per second p95. Failure rate remains a separate operational gate in both cases.
Why does this AI-agent page retain the complete provider scorecard?
The full table is retained on this page because agent architectures can be blocked by first-token delay, complete-result latency, slow streaming, wrong structured output, or an API failure. No synthetic score hides those trade-offs.
Which is the fastest LLM inference provider for AI agents?
Read the end-to-end p99 column: it is the wait an agent step pays at the slow edge, and sequential steps compound it. Time to first token only matters when the agent streams text to a person before it has the complete result. The tokens per second columns matter for agents that emit long tool arguments or narration.
Which is the most accurate LLM inference provider for AI agents?
Read the task success column. It counts only schema-valid, exactly correct answers across all 600 submissions per provider, which is the yield an autonomous agent can pass to its next step without repair. Failure rate is shown separately so delivery problems and formatting misses can be diagnosed on their own.
Same run, other questions
- LLM inference provider benchmark: complete scorecard and methodology →
- Fastest LLM inference provider for AI agents →
- Most accurate LLM inference provider for AI agents →
- Best LLM inference API for chatbots and copilots: time to first token and tokens per second →
- Best LLM inference provider for chat applications →
- Best LLM inference provider: speed, accuracy and failures compared →
- Fireworks AI vs Together AI →
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 →