Message Batches API Cost: Caching, Batching, Cheap Runs

There is a moment in every agentic project where the prototype works and the bill arrives. A workflow that felt free at ten test rows costs real money at a hundred thousand, and the latency that was invisible in a demo becomes a multi-hour wait in production. The good news is that processing at scale with Claude has two purpose-built levers — the Message Batches API for throughput and prompt caching for repeated context — and using them together can cut both your cost and your wall-clock time dramatically. This post is a practical guide to keeping large Claude runs cheap and fast without degrading quality.

Key takeaways

• The Message Batches API trades latency for a meaningful per-token discount — use it for any work that does not need an instant answer.
• Prompt caching reuses a stable prefix across requests, so shared instructions, tool definitions, and reference docs are billed at a fraction of the input rate.
• Order your prompt so the cacheable, unchanging part comes first and the variable part comes last.
• Pick the smallest model that passes your evals; Haiku and Sonnet handle most classification and extraction at a fraction of Opus cost.
• Cap max_tokens per request — it is your hard ceiling on the worst-case output bill.
• Measure cost per successful row, not cost per call, so retries and failures are counted honestly.

Two levers: batching for throughput, caching for repetition

These optimizations attack different parts of the bill. Batching reduces the price per token for work you are willing to wait on. Caching reduces how many tokens you pay full price for in the first place. They compose: a batched request whose prompt shares a cached prefix benefits from both.

Prompt caching works by letting you mark a stable prefix of your prompt as cacheable. The first request pays to write that prefix into the cache; subsequent requests that share the identical prefix read it back at a steep discount instead of reprocessing it. For agentic workloads this is enormous, because the expensive parts — a long system prompt, a big set of tool definitions, a reference document the agent consults — are exactly the parts that repeat unchanged across thousands of rows.

The Message Batches API, meanwhile, is built for volume. You hand Claude a large collection of requests, it processes them asynchronously within roughly a day, and you pay a reduced rate per token relative to synchronous calls. For nightly enrichment, backfills, bulk classification, or any job where "answer within hours" is fine, batching is the default, not the exception.

flowchart TD
  A["Large job"] --> B{"Need answer now?"}
  B -->|Yes| C["Synchronous + caching"]
  B -->|No| D["Message Batches API"]
  D --> E["Shared prefix cached?"]
  E -->|Yes| F["Cheap cached reads per row"]
  E -->|No| G["Reorder prompt -> cache prefix"]
  F --> H["Pick smallest passing model"]
  G --> H
  H --> I["Cap max_tokens & measure cost/row"]

Structure your prompt so caching actually fires

Caching only helps if the cached prefix is genuinely identical across requests. The mistake teams make is interleaving variable content into the part they hoped to cache — a timestamp in the system prompt, the row's data mixed into the instructions — which busts the cache on every call. The fix is layout discipline: put everything stable first, mark the cache breakpoint, and put the per-row variable content after it.

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 512,
  "system": [
    {
      "type": "text",
      "text": "You are a support-ticket classifier. Rules: ...long stable instructions... Tool catalog: ...",
      "cache_control": { "type": "ephemeral" }
    }
  ],
  "messages": [
    { "role": "user", "content": "Ticket #48213: my login is broken since the update" }
  ]
}

Everything above the cache_control marker is the stable prefix; only the user message changes per row. With this shape, ten thousand tickets reuse the same cached instructions and you pay full input price for that block essentially once per cache lifetime instead of ten thousand times. The savings scale directly with how much shared context your agent carries.

Right-size the model

The most overlooked cost lever is model choice. Teams reach for the most capable model out of caution and pay for it on every row. But a large fraction of agentic work — routing, tagging, extraction, yes/no judgments — is comfortably within reach of Haiku or Sonnet. The discipline is to define a quality bar, then pick the smallest model that clears it.

A practical pattern is tiered routing: run the whole batch through a small model, and only escalate the rows it flags as low-confidence to a larger one. You pay Opus prices for the genuinely hard 5%, not for the easy 95%. Combined with batching and caching, this three-way stack is where the big cost reductions come from.

Be deliberate about where the escalation boundary sits. If the small model is over-confident, it will pass bad rows through; if it is timid, it escalates too much and erodes your savings. Calibrate the confidence threshold against your eval set: find the point where escalated volume is small but the rows that should have been escalated almost always are. A good first-pass model that knows when to say "I'm not sure" is worth far more than a marginally smarter one that never admits doubt, because doubt is what routes work to the expensive tier only when it is actually warranted.

Watch out for multi-agent token multiplication

If your batched workflow spawns subagents, remember that multi-agent runs typically consume several times more tokens than a single-agent run doing the same job, because every subagent carries its own context and its outputs feed back into a coordinator. That multiplier is fine when the parallelism buys you real quality or speed, and wasteful when it does not. Before you fan out, ask whether the task genuinely decomposes into independent pieces, or whether a single well-prompted agent would reach the same answer for a fraction of the tokens. At batch scale, an unnecessary subagent is not a rounding error — it is a multiplier applied to every row in the job.

When you do use subagents, give each the narrowest context it needs rather than the full shared prompt, and cache the parts they have in common. The same prefix-caching discipline that helps single-agent runs compounds across a fan-out, because every subagent that shares the coordinator's instructions can read them from cache instead of reprocessing them.

Common pitfalls

• Caching a prefix that is not actually stable. A single varying token before the cache breakpoint defeats the whole cache. Audit your prefix byte-for-byte across rows.
• Setting max_tokens sky-high "to be safe." That value is your worst-case bill per row, and a looping agent will spend all of it. Set it to the smallest value your real outputs need.
• Using Opus for everything. Most extraction and classification does not need it. Benchmark Haiku and Sonnet first; escalate only what fails.
• Measuring cost per call. A 6% failure rate with retries can double your real cost. Track cost per successful row so the number reflects reality.
• Batching latency-sensitive work. Batches can take hours; do not put a user-facing path on them. Reserve batching for offline jobs.

Cut your bill in six steps

1. Separate offline work from interactive work and move all offline work onto the Message Batches API.
2. Reorder every prompt so the stable instructions, tools, and reference text form a single cacheable prefix.
3. Add a cache_control breakpoint at the end of that prefix and confirm cache reads are firing.
4. Re-run your evals on Haiku and Sonnet; downgrade every task that still passes.
5. Set max_tokens per task to the 95th-percentile real output length, not an arbitrary ceiling.
6. Instrument cost per successful row and watch it across prompt and model changes.

Lever	What it reduces	Best for
Message Batches API	Price per token	Offline bulk jobs that tolerate hours of latency
Prompt caching	Tokens billed at full rate	Repeated system prompts, tools, reference docs
Smaller model	Per-token rate	Routing, tagging, extraction within quality bar
Tiered routing	Volume hitting the expensive model	Mixed-difficulty workloads

Frequently asked questions

How much does the Message Batches API actually save?

Batch processing is offered at a discount relative to standard synchronous pricing, in exchange for asynchronous delivery within roughly a 24-hour window. The exact percentage is set by Anthropic's pricing, so confirm the current rate, but the trade is consistent: you accept latency and receive a meaningfully lower per-token cost. For workloads that do not need an immediate answer, that discount is effectively free money.

Can I use prompt caching and batching together?

Yes, and you should. They optimize different things — caching cuts the tokens you pay full price for, batching cuts the rate on the rest — so combining them stacks the savings. Structure your batched requests with a shared cacheable prefix and you get both benefits at once.

Does caching change the model's output?

No. Caching is a billing and performance optimization; the model sees the same tokens and produces the same response it would without caching. You are only changing how the input is processed and priced, not what Claude reasons over.

What is the single biggest lever for most teams?

Model right-sizing, usually. Teams habitually over-provision capability. Running your evals against Haiku and Sonnet and downgrading everything that still passes often beats every other optimization, because it changes the rate on every single token, not just the repeated or batched ones.

Bringing agentic AI to your phone lines

CallSphere uses the same cost discipline — caching shared context, right-sizing models, and batching what can wait — to run voice and chat agents that answer every call and message and book work 24/7 without runaway bills. Hear it for yourself at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.