Best batch inference APIs for offline LLM evaluation
2 models · updated 2026-09-05
The verdict
OpenAI Batch API leads — 1 of 2 models rank OpenAI Batch API the top pick.
Not unanimous: Claude picks vLLM.
As of 2026-09-05, Claude and Gemini collectively rank OpenAI Batch API #1 for batch inference apis for offline llm evaluation on ModelsAgree by aggregate score. The models' case: Near-tie with Anthropic Message Batches API. The models' main caveat: Proprietary vendor lock-in with a strict 24-hour async queue model that is unusable for rapid iterative prompt debugging, alongside file size and. The strongest alternative is vLLM — The default engine for large-scale offline eval on open-weight models — continuous batching, PagedAttention, and automatic prefix caching give the. Not unanimous: Claude picks vLLM. Source: https://modelsagree.com/best/best-batch-inference-apis-for-offline-llm-evaluation (modelsagree.com, CC BY 4.0).
Combined ranking
- 1Claude #2Gemini #1
Near-tie with Anthropic Message Batches API; takes the top spot assuming the typical practitioner prioritizes broad evaluation tooling integration, seed-based reproducibility, and token logprobs. Delivers a 50% cost discount across frontier and reasoning models (GPT-4o, o1/o3), reliably beats its 24-hour SLA, and has native, first-class support in virtually every major eval framework.
+ model takes & fixes− hide details
Gemini Near-tie with Anthropic Message Batches API; takes the top spot assuming the typical practitioner prioritizes broad evaluation tooling integration, seed-based reproducibility, and token logprobs. Delivers a 50% cost discount across frontier and reasoning models (GPT-4o, o1/o3), reliably beats its 24-hour SLA, and has native, first-class support in virtually every major eval framework.
Claude The cleanest way to run offline evals against frontier closed models — 50% discount versus sync pricing, a dead-simple JSONL submit/poll workflow, generous per-batch limits, and access to the strongest OpenAI models plus structured-output/logprobs support that judge-style evals rely on. Near-tie with #3.
Where it falls shortper Claude 24-hour turnaround ceiling and no throughput guarantee within it, plus lock-in to OpenAI models only — bad for interactive eval iteration or anyone needing a specific non-OpenAI model.
per Gemini Proprietary vendor lock-in with a strict 24-hour async queue model that is unusable for rapid iterative prompt debugging, alongside file size and request count caps that require splitting massive benchmark datasets.
- 2Claude #1Gemini #3
The default engine for large-scale offline eval on open-weight models — continuous batching, PagedAttention, and automatic prefix caching give the best throughput-per-dollar when you run tens of thousands of prompts on your own or rented GPUs; native integration with lm-evaluation-harness and OpenAI-compatible server modes means you can point existing eval pipelines at it unchanged, and running locally gives exact control over sampling seeds, logprobs, and reproducibility that hosted APIs won't guarantee. Assumes the practitioner is evaluating open-weight models and can provision a GPU.
+ model takes & fixes− hide details
Claude The default engine for large-scale offline eval on open-weight models — continuous batching, PagedAttention, and automatic prefix caching give the best throughput-per-dollar when you run tens of thousands of prompts on your own or rented GPUs; native integration with lm-evaluation-harness and OpenAI-compatible server modes means you can point existing eval pipelines at it unchanged, and running locally gives exact control over sampling seeds, logprobs, and reproducibility that hosted APIs won't guarantee. Assumes the practitioner is evaluating open-weight models and can provision a GPU.
Gemini The undisputed open-source standard for evaluating open-weights models (Llama, Qwen, DeepSeek). Bypasses HTTP server and network overhead via its native Python batch engine, maximizing raw GPU throughput using PagedAttention and chunked prefill while providing total control over logprobs, hidden states, and custom decoding. Assumes the practitioner possesses dedicated GPU compute.
Where it falls shortper Claude You own the ops — GPU provisioning, memory tuning, and multi-node sharding are on you, and it cannot touch closed frontier models (GPT/Claude/Gemini), so it's not for teams evaluating proprietary APIs or those without infra skills.
per Gemini Requires provisioning, configuring, and maintaining dedicated GPU hardware, and cannot run proprietary frontier models often required as reference judges.
- 3Claude #3Gemini #2
Near-tie with OpenAI Batch API; Claude 3.5 Sonnet and Opus serve as the gold standard for qualitative LLM-as-a-judge evaluation due to superior rubric following, lower sycophancy, and reduced position bias. Provides a 50% discount and robust per-query error handling over large batches.
+ model takes & fixes− hide details
Gemini Near-tie with OpenAI Batch API; Claude 3.5 Sonnet and Opus serve as the gold standard for qualitative LLM-as-a-judge evaluation due to superior rubric following, lower sycophancy, and reduced position bias. Provides a 50% discount and robust per-query error handling over large batches.
Claude The equivalent best-in-class path when Claude is the model under test or the judge — 50% discount, up to 100k requests / large payloads per batch, prompt caching that compounds savings on shared eval-instruction prefixes, and strong instruction-following that makes it a reliable LLM-as-judge backend. Near-tie with #2.
Where it falls shortper Claude Claude-only and same ~24h asynchronous window; not for cross-vendor benchmarking or latency-sensitive loops.
per Gemini Completely lacks token logprobs and deterministic seed parameters, making it unsuitable for probabilistic evaluation, calibration analysis, or multiple-choice log-likelihood scoring.
- 4Claude #4Gemini #4
Best raw scale and cost economics for very large eval corpora — long context windows, cheap Flash-tier pricing, batch jobs that read/write directly from Cloud Storage/BigQuery, and tight coupling to data warehouses where eval datasets often already live.
+ model takes & fixes− hide details
Claude Best raw scale and cost economics for very large eval corpora — long context windows, cheap Flash-tier pricing, batch jobs that read/write directly from Cloud Storage/BigQuery, and tight coupling to data warehouses where eval datasets often already live.
Gemini Best-in-class pricing and context capacity for high-volume benchmark sweeps. Combines a 50% batch discount on already low-cost Gemini Flash models with an unmatched 1M-2M token context window, making it the premier option for evaluating long-context retrieval, large-document extraction, and multi-turn agent histories. Assumes evaluations emphasize cost-per-token or extreme context length.
Where it falls shortper Claude GCP setup, IAM, and job-config friction are heavy; the tooling is clunkier and less documented than OpenAI/Anthropic, so it's overkill for small or ad-hoc eval runs.
per Gemini High operational friction requiring Google Cloud Storage (GCS) staging and complex IAM setup, alongside opaque default safety filters that can silently refuse evaluation prompts unless explicitly overridden.
- 5Claude #5Gemini —
The strongest open-source alternative to vLLM for eval workloads that lean on constrained/structured decoding — RadixAttention prefix reuse is excellent for the repeated system prompts and rubric templates typical of judge evals, and throughput is competitive-to-better on those patterns. Near-tie with vLLM on the OSS side.
+ model takes & fixes− hide details
Claude The strongest open-source alternative to vLLM for eval workloads that lean on constrained/structured decoding — RadixAttention prefix reuse is excellent for the repeated system prompts and rubric templates typical of judge evals, and throughput is competitive-to-better on those patterns. Near-tie with vLLM on the OSS side.
Where it falls shortper Claude Smaller ecosystem and fewer turnkey eval-harness integrations than vLLM, and still fully self-hosted — not for teams wanting a managed or closed-model path.
- 6Claude —Gemini #5
The premier managed serverless batch API for open-weight architectures, delivering a 50% cost discount and full OpenAI-compatible batch schema for Llama, Qwen, and DeepSeek variants without requiring GPU cluster management. Assumes the practitioner needs open-model evaluations but cannot justify self-hosting infrastructure.
+ model takes & fixes− hide details
Gemini The premier managed serverless batch API for open-weight architectures, delivering a 50% cost discount and full OpenAI-compatible batch schema for Llama, Qwen, and DeepSeek variants without requiring GPU cluster management. Assumes the practitioner needs open-model evaluations but cannot justify self-hosting infrastructure.
Where it falls shortper Gemini Turnaround latency and throughput are dependent on shared multi-tenant queue depth, and it lacks the bare-metal cost efficiency and low-level kernel customization of self-hosted vLLM.
By use case
How this board's leaders rank when the same four models are asked a more specific question.
| Product | This board | API large-scale processing | synthetic data generation |
|---|---|---|---|
| OpenAI Batch API | #1 | #1 | #1 |
| vLLM | #2 | #3 | #3 |
| Anthropic Message Batches API | #3 | #2 | #2 |
| Google Vertex AI Batch Prediction | #4 | #7 | #7 |
| SGLang | #5 | #6 | #9 |
| Together AI Batch API | #6 | #5 | — |
Just missed the top 5
Claude Together AI / Fireworks AI batch — hosted batch across many open-weight models with no infra burden, but per-token cost accumulates fast at full-eval scale versus self-hosting vLLM/SGLang
Gemini SGLang — Matches or exceeds vLLM throughput on structured JSON evaluation via RadixAttention, but missed the top 5 due to narrower community integration across standard evaluation harnesses · Fireworks AI Batch Inference API — Delivers exceptional throughput via custom speculative decoding kernels, but missed due to a smaller open-model catalog and fewer turnkey evaluation harness connectors than Together AI
By model
Claude
- 1.vLLM
- 2.OpenAI Batch API
- 3.Anthropic Message Batches API
- 4.Google Vertex AI Batch Prediction
- 5.SGLang
Gemini
- 1.OpenAI Batch API
- 2.Anthropic Message Batches API
- 3.vLLM
- 4.Google Vertex AI Batch Prediction
- 5.Together AI Batch API
Common questions
What is the best batch inference apis for offline llm evaluation according to AI models?
OpenAI Batch API leads. 1 of 2 models rank OpenAI Batch API the top pick. The current top 3: OpenAI Batch API, vLLM, Anthropic Message Batches API. Ranked by asking Claude, Gemini the same buying question and merging their top-5 picks, updated 2026-09-05. Source: modelsagree.com.
Which batch inference apis for offline llm evaluation did each AI model pick first?
Claude: vLLM. Gemini: OpenAI Batch API.
Do the AI models agree on the best batch inference apis for offline llm evaluation?
Not unanimous. Claude picks vLLM.
How is this batch inference apis for offline llm evaluation ranking made?
Claude, Gemini are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best batch inference APIs for offline LLM evaluation” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-09-05. https://modelsagree.com/best/best-batch-inference-apis-for-offline-llm-evaluation (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand