OpenAI vs Anthropic vs Gemini Batch API: Which to Use in 2026

TL;DR

All three major AI providers offer batch APIs with 50% cost savings on standard pricing — but they differ significantly in rate limits, turnaround guarantees, and ergonomics. OpenAI's Batch API is the most flexible with a higher batch creation rate (2,000/hour) and a clean async SDK. Anthropic's Message Batches API has explicit 100k request limits per batch but often completes in under an hour despite the 24-hour SLA. Google's Gemini Batch API on Vertex AI integrates tightly with GCS but has higher setup overhead. If you're spending more than $500/month on AI inference for non-realtime workloads, switching to batch processing should be your first optimization.

Key Takeaways

• All three offer 50% off standard per-token pricing for async batch workloads
• OpenAI: 2,000 batches/hour creation rate, 24-hour completion window, 50,000 requests or 200MB per batch
• Anthropic: 100,000 requests or 256MB per batch, typically completes in under 1 hour despite 24-hour SLA
• Gemini (Vertex AI): GCS-based input/output, tight GCP integration, Gemini 1.5 Pro/Flash supported
• Separate rate limit pools — batch API calls don't consume your realtime quota on any provider
• Not all use cases qualify — batch is for async workloads only; anything requiring sub-second responses needs the realtime API

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What Is a Batch API?

Standard AI API calls are synchronous: you send a request, you wait for the response, you process the next request. This model is necessary for real-time applications — chatbots, copilots, streaming responses — but it's wasteful for offline processing tasks.

Batch processing use cases:

• Classifying 50,000 customer support tickets overnight
• Generating product descriptions for an entire catalog
• Running evals on a model across thousands of test cases
• Extracting structured data from a corpus of documents
• Sentiment analysis on historical data

For these workloads, you don't need a sub-100ms response. You need the results by tomorrow morning. Providers offer this as a bulk discount: send thousands of requests in a file, get results back asynchronously within 24 hours, pay half price.

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OpenAI Batch API

OpenAI launched the Batch API in April 2024 and it's matured significantly. The workflow:

1. Upload a JSONL file where each line is an API request
2. Create a batch object pointing to the file
3. Poll for completion (or use webhooks)
4. Download and process the results file

Pricing

OpenAI Batch API pricing is exactly 50% of the standard API price:

Model	Standard (input/output per 1M)	Batch (input/output per 1M)
GPT-5.4	$15 / $60	$7.50 / $30
GPT-5.4 mini	$0.30 / $1.20	$0.15 / $0.60
text-embedding-3-large	$0.13 / —	$0.065 / —

Limits and Constraints

• Batch creation rate: 2,000 batches per hour (high — suitable for systems creating many small batches)
• Per-batch request limit: 50,000 requests or 200MB per batch file
• Completion window: 24 hours (most complete within 2–6 hours in practice)
• Supported endpoints: /v1/chat/completions, /v1/embeddings, /v1/completions
• Expiry: Uncompleted batches expire after 24 hours and are charged for completed requests only

Code Example

import OpenAI from "openai";
import * as fs from "fs";

const client = new OpenAI();

// 1. Prepare batch file
const requests = products.map((product, idx) => ({
  custom_id: `req-${idx}`,
  method: "POST",
  url: "/v1/chat/completions",
  body: {
    model: "gpt-5.4-mini",
    messages: [
      {
        role: "user",
        content: `Write a 50-word product description for: ${product.name}. Category: ${product.category}.`
      }
    ],
    max_tokens: 100
  }
}));

const batchFile = requests.map(r => JSON.stringify(r)).join("\n");
fs.writeFileSync("/tmp/batch.jsonl", batchFile);

// 2. Upload and create batch
const file = await client.files.create({
  file: fs.createReadStream("/tmp/batch.jsonl"),
  purpose: "batch"
});

const batch = await client.batches.create({
  input_file_id: file.id,
  endpoint: "/v1/chat/completions",
  completion_window: "24h"
});

console.log(`Batch created: ${batch.id}`);

// 3. Poll for completion
let status = await client.batches.retrieve(batch.id);
while (status.status !== "completed" && status.status !== "failed") {
  await new Promise(r => setTimeout(r, 30000)); // poll every 30 seconds
  status = await client.batches.retrieve(batch.id);
}

// 4. Download results
const results = await client.files.content(status.output_file_id!);

OpenAI Batch API Strengths

• Mature SDK — first-class support in the official OpenAI Python and TypeScript libraries
• High batch creation throughput — 2,000 batches/hour means you can parallelize across many small batches
• Webhook support — set completion_window to get notified instead of polling
• Generous token limits — no explicit per-batch token cap beyond file size

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Anthropic Message Batches API

Anthropic's Message Batches API delivers the same 50% discount with one meaningful difference: tighter per-batch limits but faster actual completion in practice.

Pricing

Anthropic Batch API pricing is 50% off standard Claude pricing:

Model	Standard (input/output per 1M)	Batch (input/output per 1M)
Claude Opus 4.6	$15 / $75	$7.50 / $37.50
Claude Sonnet 4.7	$3 / $15	$1.50 / $7.50
Claude Haiku 3.5	$0.80 / $4	$0.40 / $2

Limits and Constraints

• Per-batch request limit: 100,000 requests OR 256MB (whichever is smaller)
• Completion window: 24-hour SLA, but batches typically complete in under 1 hour
• Supported models: All current Claude models (Opus, Sonnet, Haiku families)
• Rate limits: Separate pool from Message API limits — batch doesn't consume your tier's TPM/RPM
• Results storage: Kept for 29 days after batch completion

Code Example

import anthropic
import time

client = anthropic.Anthropic()

# 1. Create batch
requests = [
    {
        "custom_id": f"ticket-{ticket_id}",
        "params": {
            "model": "claude-haiku-3-5-20251001",
            "max_tokens": 100,
            "messages": [
                {
                    "role": "user",
                    "content": f"Classify this support ticket as bug, feature-request, or question:\n\n{ticket_text}"
                }
            ]
        }
    }
    for ticket_id, ticket_text in tickets.items()
]

batch = client.messages.batches.create(requests=requests)
print(f"Batch ID: {batch.id}")

# 2. Wait for completion
while batch.processing_status == "in_progress":
    time.sleep(60)
    batch = client.messages.batches.retrieve(batch.id)
    print(f"Status: {batch.processing_status} — {batch.request_counts}")

# 3. Process results
for result in client.messages.batches.results(batch.id):
    if result.result.type == "succeeded":
        content = result.result.message.content[0].text
        print(f"{result.custom_id}: {content}")

Anthropic Batch API Strengths

• Higher per-batch request limit — 100,000 requests vs OpenAI's 50,000
• Fast actual completion — often done in under 30 minutes despite 24-hour SLA
• Claude's reasoning quality — for complex classification/extraction, Claude Sonnet often outperforms GPT on nuanced tasks
• Streaming-style results — iterate over results as a stream, no need to download a full file

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Google Gemini Batch API (Vertex AI)

Google's batch processing for Gemini runs through Vertex AI and has a different architecture from OpenAI and Anthropic. Instead of a REST-first API with file uploads, Gemini batch jobs read from Google Cloud Storage (GCS) and write results back to GCS.

Pricing

Gemini Batch API pricing on Vertex AI:

Model	Standard (input/output per 1M)	Batch discount
Gemini 1.5 Pro	$3.50 / $10.50	50% off
Gemini 2.0 Flash	$0.10 / $0.40	50% off
Gemini 2.5 Pro	$7 / $21	50% off

Note: Gemini batch pricing is often quoted as "50% off standard" but verify against current Vertex AI pricing, which updates frequently.

Architecture Difference

Gemini batch jobs are closer to BigQuery jobs than to REST API calls:

import vertexai
from vertexai.preview.generative_models import GenerativeModel

vertexai.init(project="your-project", location="us-central1")

# Input must be in GCS as JSONL
# gs://your-bucket/batch-input/requests.jsonl

batch_prediction_job = GenerativeModel("gemini-2-0-flash").batch_predict(
    dataset="gs://your-bucket/batch-input/requests.jsonl",
    destination_uri_prefix="gs://your-bucket/batch-output/",
)

batch_prediction_job.wait()

Each request in the input JSONL follows the Gemini content format, with results written to the specified GCS output location.

Gemini Batch API Strengths

• Massive context window — Gemini 1.5 Pro's 1M token context makes it viable for processing extremely long documents in batch
• Tight GCP integration — if you're already in GCP, GCS-to-GCS workflow is clean
• Lowest price per token on Flash — Gemini 2.0 Flash batch at $0.05/$0.20 per 1M tokens is among the cheapest batch options available
• BigQuery integration — can write results directly to BigQuery for analysis

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Head-to-Head Comparison

Dimension	OpenAI Batch	Anthropic Batches	Gemini (Vertex)
Discount	50% off	50% off	50% off
Requests per batch	50,000	100,000	Unlimited (GCS)
File size limit	200MB	256MB	GCS (no limit)
Typical completion	2–6 hours	Under 1 hour	1–6 hours
SLA window	24 hours	24 hours	24 hours
SDK ergonomics	★★★★★	★★★★★	★★★☆☆
Setup complexity	Low	Low	High (GCP/Vertex)
Rate limit pool	Separate	Separate	Separate
Results storage	30 days	29 days	GCS (your storage)

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Choosing the Right Batch API

Choose OpenAI Batch if:

• You're already on GPT models and want the simplest migration to batch
• You need high batch creation throughput (2,000/hour) for systems that create many small batches
• Your workloads benefit from GPT-5.4 specifically (code generation, reasoning tasks)

Choose Anthropic Message Batches if:

• You're doing complex classification, extraction, or analysis where Claude's reasoning quality matters
• You need faster actual completion — Anthropic's batches often finish in under an hour
• Per-batch request volume exceeds OpenAI's 50,000 limit (Anthropic allows 100,000)

Choose Gemini Batch (Vertex AI) if:

• You're already in the GCP ecosystem and want tight integration
• You're processing extremely long documents and need Gemini's 1M context window
• Cost is the primary driver — Gemini 2.0 Flash batch is among the cheapest at scale

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Real-World Cost Example

Scenario: Classify 500,000 customer support tickets per month using a short prompt (~200 input tokens, ~50 output tokens each).

Provider	Model	Monthly cost (realtime)	Monthly cost (batch)	Savings
OpenAI	GPT-5.4 mini	$37.50	$18.75	$18.75
Anthropic	Claude Haiku 3.5	$62.50	$31.25	$31.25
Google	Gemini 2.0 Flash	$7.75	$3.88	$3.88

For this workload, Gemini 2.0 Flash batch is dramatically cheaper. But if you need Claude's reasoning quality for nuanced ticket classification, the higher cost of Anthropic batch may be justified by fewer misclassifications.

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When Batch Processing Isn't the Answer

Batch APIs are not appropriate for:

• Real-time user interactions — chatbots, copilots, live search
• Streaming responses — batch returns complete responses, not streams
• Workloads with <1 second latency requirements
• Very small volumes — the ergonomic overhead of batch setup isn't worth it under ~1,000 requests/day

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Methodology

• Sources: 8 — OpenAI Batch API documentation, Anthropic Message Batches guide, Google Vertex AI batch prediction docs, Finout.io pricing comparison, Vantage AI cost analysis
• Pricing verified: March 2026 (verify current pricing before production use — AI pricing changes frequently)
• Date: March 2026

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Compare real-time pricing across all major LLMs in our LLM API Pricing Comparison 2026, or see which provider wins for specific tasks in the DeepSeek vs OpenAI vs Claude comparison.

Related: Claude API Extended Thinking Mode · Groq API Review 2026