Best LLM inference provider for RAG answer extraction
What the benchmark found. Fireworks AI, Modal, Telnyx, and Z.AI tied for the task-success lead at 100.0%. The leading score represents approximately 200 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Modal 0.0%; Telnyx 0.0%; Z.AI 0.0%. This is an answer-extraction result with relevant context already supplied, not an end-to-end retrieval benchmark. Measured across 10 LLM inference providers serving GLM 5.3 Flash, 200 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. The benchmark does not run retrieval infrastructure. It tests the inference step after a relevant document is already supplied: each provider reads a meeting note and returns one requested grounded fact. This page isolates those 200 lookup questions rather than claiming to rank embeddings, vector databases, or full RAG systems.
How to read the result. Use task success to compare answer extraction from provided context, then use end-to-end latency when that extraction blocks a user or agent. Do not use this table to choose a retriever: retrieval recall, reranking, citations, and context assembly are outside the measured path.
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.
Task-specific table. The table below uses only the published meeting-notes-lookup aggregate rather than the three-family pooled summary. Each provider therefore contributes 200 submitted requests to this view.
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.
- Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Z.AI:
glm-5.3-flash· API docs ↗ - Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗ - Novita AI:
zai-org/glm-5.3-flash· API docs ↗ - Nebius:
zai-org/GLM-5.3-Flash· API docs ↗
Which LLM inference provider extracts answers from supplied documents most reliably?
Only meeting-note lookup aggregates appear here. The table adds exact-success counts, failure rate, and end-to-end p99 to show answer yield and wait for 200 provided-context questions per provider.
| Provider / API | Task success | Exact successes | Failure rate | End-to-end p99 |
|---|---|---|---|---|
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 100.0% | 200 / 200 | 0.0% | 7.54 s |
| Modalzai-org/GLM-5.3-Flash ↗ | 100.0% | 200 / 200 | 0.0% | 1.63 s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 100.0% | 200 / 200 | 0.0% | 2.92 s |
| Z.AIglm-5.3-flash ↗ | 100.0% | 200 / 200 | 0.0% | 7.57 s |
| Basetenzai-org/GLM-5.3-Flash ↗ | 99.5% | 199 / 200 | 0.0% | 1.43 s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 99.0% | 198 / 200 | 1.0% | 11.1 s |
| Together AIzai-org/GLM-5.3-Flash ↗ | 98.5% | 197 / 200 | 1.5% | 12.8 s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 98.0% | 196 / 200 | 2.0% | 15.7 s |
| Novita AIzai-org/glm-5.3-flash ↗ | 94.5% | 189 / 200 | 5.5% | 6.13 s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 84.5% | 169 / 200 | 15.5% | 18.6 s |
Provider by provider, in the order above
- Fireworks AI: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 7.54 seconds.
- Modal: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 1.63 seconds.
- Telnyx: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 2.92 seconds.
- Z.AI: 100.0% task success (200 of 200 exact), 0.0% failures, end-to-end p99 7.57 seconds.
- Baseten: 99.5% task success (199 of 200 exact), 0.0% failures, end-to-end p99 1.43 seconds.
- Parasail: 99.0% task success (198 of 200 exact), 1.0% failures, end-to-end p99 11.06 seconds.
- Together AI: 98.5% task success (197 of 200 exact), 1.5% failures, end-to-end p99 12.84 seconds.
- DeepInfra: 98.0% task success (196 of 200 exact), 2.0% failures, end-to-end p99 15.69 seconds.
- Novita AI: 94.5% task success (189 of 200 exact), 5.5% failures, end-to-end p99 6.13 seconds.
- Nebius: 84.5% task success (169 of 200 exact), 15.5% failures, end-to-end p99 18.63 seconds.
Which RAG stages can this evidence inform?
- Answer extraction after retrieval. Use the result when your system has already selected a compact relevant passage and needs one exact fact.
- Document-grounded agent steps. Compare task success and completion latency when a supplied record controls the agent's next action.
- Not retrieval quality. Run a separate retrieval evaluation for embeddings, chunking, search recall, reranking, and citation coverage.
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 ↗.
RAG inference and document-lookup boundaries
Which provider extracted the most correct facts from supplied meeting notes?
Fireworks AI, Modal, Telnyx, and Z.AI tied for the task-success lead at 100.0%. The leading score represents approximately 200 exact successful results from 200 requests; operational failure rates were Fireworks AI 0.0%; Modal 0.0%; Telnyx 0.0%; Z.AI 0.0%. This is an answer-extraction result with relevant context already supplied, not an end-to-end retrieval benchmark.
How does the benchmark normalize a document-lookup answer?
The lookup evaluator reconstructs the complete visible answer, parses the answer string, trims and collapses whitespace, and compares case-insensitively with the canonical answer and permitted aliases.
Does this page benchmark retrieval, embeddings, or a complete RAG pipeline?
This task supplies the relevant document directly in the prompt. It measures answer extraction after retrieval, not embedding quality, vector search recall, reranking, citation faithfulness, or an end-to-end RAG stack.
Same run, other questions
- LLM inference provider benchmark: complete scorecard and methodology →
- Same model, different accuracy: LLM inference provider endpoint accuracy →
- Most reliable LLM API provider: failure rate, errors, timeouts and usable answers →
- Most reliable LLM API for structured output and JSON schema extraction →
- 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 →