Best batch inference APIs for synthetic data generation
4 models · updated 2026-08-10
The verdict
OpenAI Batch API leads — 2 of 4 models rank OpenAI Batch API the top pick.
Not unanimous: ChatGPT picks Google Gemini Batch API; Grok picks Together AI.
As of 2026-08-10, ChatGPT, Claude, Gemini and Grok collectively rank OpenAI Batch API #1 for batch inference apis for synthetic data generation on ModelsAgree by aggregate score. The models' case: Best all-around quality-per-dollar for synthetic data — flagship and mini GPT models at a 50% batch discount, first-class structured outputs (JSON schema) that keep. The models' main caveat: 24h turnaround window with per-batch queue limits and no self-hosting — if you need tight iteration loops or on-prem data residency, it's the wrong. The strongest alternative is Anthropic Message Batches API — Particularly strong for nuanced, long-form, reasoning-heavy synthetic data. Not unanimous: ChatGPT picks Google Gemini Batch API; Grok picks Together AI. Source: https://modelsagree.com/best/best-batch-inference-apis-for-synthetic-data-generation (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #2Claude #1Gemini #1Grok —
Best all-around quality-per-dollar for synthetic data — flagship and mini GPT models at a 50% batch discount, first-class structured outputs (JSON schema) that keep generated records parseable, and the largest ecosystem of tooling/examples for distillation and dataset-building; the mini tier makes million-row generation cheap while the top tier handles hard reasoning traces.
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Claude Best all-around quality-per-dollar for synthetic data — flagship and mini GPT models at a 50% batch discount, first-class structured outputs (JSON schema) that keep generated records parseable, and the largest ecosystem of tooling/examples for distillation and dataset-building; the mini tier makes million-row generation cheap while the top tier handles hard reasoning traces.
Gemini Unmatched as a teacher-model source for high-quality synthetic dataset distillation (GPT-4o, o1, o3-mini) with guaranteed structured JSON outputs, massive rate limits, and 50% cost reduction. Assumes the practitioner prioritizes frontier reasoning quality over local data control. Near-tied with Anthropic Message Batches API.
GPT Excellent teacher-model quality with reliable structured outputs and a mature JSONL workflow across Responses, Chat Completions, embeddings, and moderation; 50% pricing makes it a strong low-friction default.
Where it falls shortper GPT It locks users into OpenAI-hosted models, making it unsuitable when open weights or model portability are requirements.
per Claude 24h turnaround window with per-batch queue limits and no self-hosting — if you need tight iteration loops or on-prem data residency, it's the wrong tool.
per Gemini Closed-source cloud API with an asynchronous 24-hour SLA, making it unsuited for strict on-premise data privacy mandates or real-time sub-minute generation needs.
- 2GPT #3Claude #4Gemini #3Grok —
Particularly strong for nuanced, long-form, reasoning-heavy synthetic data; supports vision, tools, extended thinking, prompt caching, up to 100,000 requests, unusually long outputs, and 50% pricing, with most batches reportedly finishing within an hour.
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GPT Particularly strong for nuanced, long-form, reasoning-heavy synthetic data; supports vision, tools, extended thinking, prompt caching, up to 100,000 requests, unusually long outputs, and 50% pricing, with most batches reportedly finishing within an hour.
Gemini Exceptional for generating complex, highly nuanced synthetic text and reasoning datasets using Claude models (3.5/3.7 Sonnet), featuring fast turnaround times (frequently under 1 hour), 100k request batch limits, and 50% discounts. Near-tied with OpenAI Batch API for top commercial teacher data generation.
Claude Top-tier generation quality and instruction-following at a 50% batch discount, with strong long-context and tool-use fidelity — excellent for high-fidelity, diverse, or safety-sensitive synthetic corpora where per-sample quality beats raw volume; near-tie with #1 on quality, ranked just below on breadth of structured-output tooling and cost at the cheap tier.
Where it falls shortper GPT It is ineligible for zero-data-retention treatment and can retain batch data for 29 days, ruling it out for some sensitive datasets.
per Claude No true low-cost "mini/flash" tier as aggressive as competitors, so bulk low-value generation costs more; weaker native warehouse/data-pipeline integration.
per Gemini Terms of service strictly prohibit using generated outputs to train competing commercial foundation models, limiting utility for general foundation model builders.
- 3GPT —Claude #2Gemini #2Grok —
The value leader when you own GPUs and use open weights — zero per-token cost, near-hardware-ceiling throughput via continuous batching and prefix caching (huge for the shared-prompt-template pattern of synthetic data), and total control over sampling, logprobs, and guided/structured decoding; scales to arbitrary volume without a vendor queue.
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Claude The value leader when you own GPUs and use open weights — zero per-token cost, near-hardware-ceiling throughput via continuous batching and prefix caching (huge for the shared-prompt-template pattern of synthetic data), and total control over sampling, logprobs, and guided/structured decoding; scales to arbitrary volume without a vendor queue.
Gemini The open-source standard for self-hosted, high-throughput synthetic data generation, leveraging PagedAttention and offline continuous batching (LLM.generate) for maximum VRAM efficiency and zero per-token markup. Assumes the user operates custom GPU infrastructure to generate data from open-weights models like Llama 3 or DeepSeek.
Where it falls shortper Claude Not an API you call — you provision, shard, and babysit GPUs yourself; useless to anyone without infra or the appetite to run it, and open-model output quality still trails frontier closed models on the hardest tasks.
per Gemini Requires significant engineering overhead for infrastructure management and cannot run proprietary state-of-the-art teacher models.
- 4GPT #5Claude #5Gemini —Grok #1
50% cost savings vs realtime on most serverless models with separate high rate limits and up to 30B enqueued tokens per model; broad access to latest open-weight models (Llama, Qwen, DeepSeek, Kimi, etc.); purpose-built for high-volume async jobs including synthetic data generation with reliable sub-24h completion and simple JSONL workflow
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Grok 50% cost savings vs realtime on most serverless models with separate high rate limits and up to 30B enqueued tokens per model; broad access to latest open-weight models (Llama, Qwen, DeepSeek, Kimi, etc.); purpose-built for high-volume async jobs including synthetic data generation with reliable sub-24h completion and simple JSONL workflow
GPT Accessible OpenAI-compatible batch infrastructure for many open models, with a separate rate-limit pool, up to 50,000 requests and 30B queued tokens per model, and fast completion for smaller jobs.
Claude Best open-model batch API — wide catalog (Llama, Qwen, DeepSeek, Mixtral and more) behind one OpenAI-compatible endpoint with a batch discount, letting you match model to task and license without running infra; strong sweet spot for permissively-licensed, redistributable synthetic datasets.
Where it falls shortper GPT The 50% discount applies only to selected models, while several desirable frontier open models are unavailable for batch processing.
per Claude You inherit open-model quality ceilings and must vet each model's license/behavior yourself; SLA and reliability at extreme scale trail the hyperscalers.
per Grok Not for sub-hour turnaround or workloads that demand the absolute lowest possible per-token rates on the cheapest models
- 5GPT #4Claude —Gemini —Grok #2
50% off serverless rates plus automatic prompt caching for additional savings; explicitly optimized and documented for synthetic data generation, data labeling, and distillation; strong inference engine performance across 1000+ models including fine-tunes; clean dataset-based job submission
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Grok 50% off serverless rates plus automatic prompt caching for additional savings; explicitly optimized and documented for synthetic data generation, data labeling, and distillation; strong inference engine performance across 1000+ models including fine-tunes; clean dataset-based job submission
GPT Best open-model-oriented option: supports hosted, uploaded, and fine-tuned models, applies a 50% batch discount, automatically uses prompt caching, and directly targets distillation and production-scale data generation.
Where it falls shortper GPT Batch compatibility varies by model and must be checked explicitly; unsupported jobs can remain pending instead of failing promptly.
per Grok Slightly less extreme scale headroom than the largest pure-batch specialists and not the absolute cheapest on every open model
- 6GPT #1Claude —Gemini —Grok —
Best overall cost-quality-scale mix for typical text and multimodal synthetic datasets: strong Gemini models, economical Flash variants, schema-constrained JSON, context caching, 2GB input files, webhooks, and 50% batch pricing. Near-tied with OpenAI; wins when value and scale matter most.
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GPT Best overall cost-quality-scale mix for typical text and multimodal synthetic datasets: strong Gemini models, economical Flash variants, schema-constrained JSON, context caching, 2GB input files, webhooks, and 50% batch pricing. Near-tied with OpenAI; wins when value and scale matter most.
Where it falls shortper GPT Batch creation is not idempotent, so careless retries can duplicate jobs and spend.
- 7GPT —Claude #3Gemini #5Grok —
Strongest at pure scale-and-cost — Gemini Flash-tier pricing is among the lowest per token, the very large context window suits long-document or many-shot synthetic prompts, and native BigQuery/GCS in-and-out makes million-row jobs a data-warehouse operation rather than a scripting chore.
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Claude Strongest at pure scale-and-cost — Gemini Flash-tier pricing is among the lowest per token, the very large context window suits long-document or many-shot synthetic prompts, and native BigQuery/GCS in-and-out makes million-row jobs a data-warehouse operation rather than a scripting chore.
Gemini Best-in-class for long-context and multimodal synthetic data synthesis (Gemini 1.5/2.0 Pro and Flash), offering seamless integration with BigQuery and Google Cloud Storage at 50% off standard API rates. Assumes the practitioner is synthesizing data from enterprise data lakes or ultra-long documents/video inputs.
Where it falls shortper Claude Heavy GCP lock-in and clunkier ergonomics (IAM, storage plumbing) than a plain REST batch endpoint; overkill and awkward if you aren't already on Google Cloud.
per Gemini Deeply locked into the Google Cloud ecosystem, introducing deployment friction for non-GCP infrastructure stacks.
- 8GPT —Claude —Gemini —Grok #3
Deepest effective
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Grok Deepest effective
- 9GPT —Claude —Gemini #4Grok —
Superior choice for multi-step, agentic, or heavily constrained synthetic data generation (JSON schemas and grammars) due to RadixAttention prefix caching, which dramatically accelerates batch jobs sharing prompt prefixes. Assumes synthetic workflows rely heavily on long system prompts or multi-turn agent execution traces.
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Gemini Superior choice for multi-step, agentic, or heavily constrained synthetic data generation (JSON schemas and grammars) due to RadixAttention prefix caching, which dramatically accelerates batch jobs sharing prompt prefixes. Assumes synthetic workflows rely heavily on long system prompts or multi-turn agent execution traces.
Where it falls shortper Gemini Steeper optimization curve and smaller ecosystem integration footprint compared to vLLM for straightforward, non-structured single-turn batch generation.
Rank history
Just missed the top 5
GPT Mistral Batch API — capable and inexpensive at a 50% discount, but offers a narrower teacher-model range and fewer differentiating batch features · Amazon Bedrock Batch Inference — broad enterprise model access and AWS controls, but S3/IAM overhead, smaller jobs, and no structured-output or tool-calling support make it weaker for typical synthetic-data pipelines
Claude Amazon Bedrock Batch Inference — solid multi-model batch with S3 I/O, but ergonomics and model-access gating make it a fit mainly for existing AWS shops, not a category leader
Gemini Fireworks AI Batch API — delivers solid 50% discounted batch inference for hosted open models, but lacks self-hosted zero-markup efficiency and frontier proprietary intelligence · Together AI — provides reliable bulk inference for open-weights models with separate rate limits, but loses to local vLLM/SGLang on cost at scale and OpenAI/Anthropic on raw teacher dataset quality
By model
ChatGPT
- 1.Google Gemini Batch API
- 2.OpenAI Batch API
- 3.Anthropic Message Batches API
- 4.Fireworks AI Batch API
- 5.Together AI
Claude
- 1.OpenAI Batch API
- 2.vLLM
- 3.Google Vertex AI Batch Prediction
- 4.Anthropic Message Batches API
- 5.Together AI
Gemini
- 1.OpenAI Batch API
- 2.vLLM
- 3.Anthropic Message Batches API
- 4.SGLang
- 5.Google Vertex AI Batch Prediction
Grok
- 1.Together AI
- 2.Fireworks AI Batch API
- 3.Doubleword
Common questions
What is the best batch inference apis for synthetic data generation according to AI models?
OpenAI Batch API leads. 2 of 4 models rank OpenAI Batch API the top pick. The current top 3: OpenAI Batch API, Anthropic Message Batches API, vLLM. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-10. Source: modelsagree.com.
Which batch inference apis for synthetic data generation did each AI model pick first?
ChatGPT: Google Gemini Batch API. Claude: OpenAI Batch API. Gemini: OpenAI Batch API. Grok: Together AI.
Do the AI models agree on the best batch inference apis for synthetic data generation?
Not unanimous. ChatGPT picks Google Gemini Batch API; Grok picks Together AI.
What changed in the latest batch inference apis for synthetic data generation ranking?
In the latest poll (2026-08-10): Together AI climbed 3 spots, Fireworks AI Batch API climbed 1 spot; Google Gemini Batch API dropped 2 spots, Google Vertex AI Batch Prediction dropped 2 spots, SGLang dropped 1 spot; Doubleword entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this batch inference apis for synthetic data generation ranking made?
ChatGPT, Claude, Gemini, Grok 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 synthetic data generation” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-10. https://modelsagree.com/best/best-batch-inference-apis-for-synthetic-data-generation (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand