Originally published at claudeguide.io/claude-api-streaming-batch

Claude API: Streaming vs Batch — Which Saves More (2026)

Batch API is 50% cheaper than real-time inference. Streaming adds perceived speed at zero cost premium. The decision rule is simple: if the user is watching, stream. If no one is waiting, batch. Most teams get this backwards — running batch jobs interactively and real-time jobs overnight — and pay 2–5× more than they should.

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The cost difference, in one table

Mode	Input / 1M	Output / 1M	When billed
Real-time (default)	$3.00	$15.00	Per request, immediately
Streaming	$3.00	$15.00	Same as real-time
Batch API	$1.50	$7.50	After batch completes

Streaming costs identical to non-streaming real-time — you pay the same per token whether you receive them word-by-word or all at once. The only difference is latency UX.

Batch API is exactly 50% off across all models and all token types. Cache read discounts stack on top.

A $1,000/month real-time workload costs $500/month on Batch API, if the task doesn't require an immediate response.

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When streaming makes sense

Streaming delivers tokens to the client as they are generated. The model is still running the same computation — you just receive results incrementally instead of waiting for completion.

Use streaming when:

• A human is reading the output in real time (chat, live completions, code editor autocomplete)
• Time-to-first-token (TTFT) matters to user experience — even a 2-second delay feels slow in interactive contexts
• You're building a UI that should feel responsive (streaming at 30–80 tokens/sec feels fast; waiting 15 seconds for 500 tokens feels broken)
• Multi-turn conversation where the user replies mid-response

What streaming does NOT do:

• Reduce total latency for the full response (the model generates at the same speed)
• Lower cost (same price as non-streaming)
• Improve output quality

The win is purely perceptual: TTFT drops from ~2–15s (full-response wait) to ~0.3–0.8s (first token arrives quickly).

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When Batch API makes sense

Batch API queues requests and processes them asynchronously, returning results within 24 hours (typically 1–4 hours in practice for standard workloads).

Use Batch API when:

• No user is waiting for the result right now
• Processing large volumes overnight or on a schedule (nightly classification, daily report generation, weekly data enrichment)
• Generating content assets in advance (product descriptions, alt text, email drafts queued for review)
• Running evals against a labeled dataset — 100+ samples run cheaply as a batch
• Any pipeline step that feeds the next step hours later, not seconds later

What Batch API does NOT do:

• Return results in real time (24h SLA, typically 1–4h)
• Support streaming output (results arrive as a completed file, not incremental tokens)
• Support multi-turn conversation per request (each batch item is a single-turn prompt+response)

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Illustrative cost comparison

Three illustrative workloads based on published Anthropic pricing, May 2026:

1 — Email draft generation

Task: Generate first-draft replies to 25,000 customer support tickets/month. Avg 1,500 input, 250 output tokens.

Mode	Cost/month	Latency
Streaming (real-time)	$125	2–3s TTFT
Batch API	$62	Results available next morning

Winner: Hybrid. Simple tickets (FAQ-answerable) → streaming for the agent to present immediately. Complex tickets (needing research) → batch overnight. Net: ~$80/month at full quality.

2 — Product description generation

Task: Generate alt text and short descriptions for 10,000 new product listings weekly. 800 input, 150 output tokens.

Mode	Cost/month	Notes
Real-time	$54	No user is waiting
Batch API	$27	Run nightly, results in catalog by morning

Winner: Batch. Descriptions don't need to be ready in seconds — they need to be ready before the product goes live. Batch saves 50% with no quality or UX tradeoff.

3 — Eval runs

Task: Score 500 model outputs against a rubric for a weekly regression eval.

Mode	Cost/run	Time
Real-time	$0.90	~12 minutes
Batch API	$0.45	~45 minutes

Winner: Batch. Evals run overnight in CI — 45 minutes is fine. Saves 50% on every regression run.

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The Batch API in practice

Python: submit a batch

import anthropic
import json

client = anthropic.Anthropic()

# Build batch requests
requests = [
    {
        "custom_id": f"item-{i}",
        "params": {
            "model": "claude-sonnet-4-5",
            "max_tokens": 256,
            "messages": [{"role": "user", "content": f"Summarize: {text}"}]
        }
    }
    for i, text in enumerate(texts)  # texts = your list of inputs
]

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

Poll and retrieve results

python
import time

def wait_for_batch(batch_id: str, poll_interval: int = 60) -

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