# Cost Per Task: What a $0.10 Subagent Changes for AI Agents On October 7, Anthropic released Claude Haiku 5.5, its smallest model, at $0.10 per million input tokens and $0.50 per million output tokens. That is a headline price cut. The more useful question for anyone building agents is different: what happens to the cost of a whole task when a cheap model works next to an expensive one? This article does that arithmetic with Anthropic's published prices, shows where the savings are real, and shows the one condition that decides whether they survive contact with production. How to read it: prices, benchmark scores and customer quotes come from Anthropic's own pages and are the company's claims. The worked examples are my arithmetic on those prices, not measurements. I tested nothing. ## What Anthropic launched Documented on Anthropic's Haiku 5.5 page and pricing page: * Haiku 5.5 costs $0.10 input and $0.50 output per million tokens for prompts up to 100,000 tokens. Above 100,000 tokens, it costs $0.50 input and $2.50 output. * Anthropic says it costs around 75% less to run than Haiku 4.5 on average, and is priced 90% lower for requests up to 100,000 tokens and 50% lower above that. * It is the first Haiku-class model with adjustable effort controls, and Anthropic calls it its fastest model at standard speed. * Anthropic describes it as designed for high-volume, cost-sensitive tasks such as summaries, compaction, database queries and classification, and says it "pairs well with Opus 5.5 and Sonnet 5.5 as a subagent on coding work." * The same page says Sonnet 5.5 and Opus 5.5 "remain better choices for complex agentic coding tasks." For comparison, the pricing page lists Sonnet 5.5 at $2 input and $10 output, and Opus 5.5 at $4 input and $20 output, per million tokens. ## One subtask, three prices Take a simple subtask a lead agent might hand off: read a 50,000-token document and write a 1,000-token summary. * Opus 5.5: about $0.22. * Sonnet 5.5: about $0.11. * Haiku 5.5: about $0.0055. Same job, 20 times cheaper than Sonnet and 40 times cheaper than Opus. Because the prompt stays under 100,000 tokens, it lands in the cheaper tier. At 150,000 tokens the same job costs about $0.078 on Haiku, which is still cheaper than Sonnet's $0.31 but a much smaller gap, about 4 times. Keeping subagent prompts narrow is part of the saving. ## A whole task with a lead and eight subtasks Now an illustrative task: a lead model plans (10,000 tokens in, 2,000 out), then eight subtasks of the kind above run. * Sonnet 5.5 as lead and doing every subtask: about $0.92. * Sonnet 5.5 as lead, Haiku 5.5 on the eight subtasks: about $0.084, roughly 91% lower. * Opus 5.5 as lead and doing every subtask: about $1.84. * Opus 5.5 as lead, Haiku 5.5 on the subtasks: about $0.124, roughly 93% lower. Most of the bill was the repeated reading, so moving that work to the cheap model removes most of the cost. This is arithmetic, not a benchmark. It assumes the subtasks suit Haiku and that every one succeeds. ## The catch: cheap only helps if you can tell when it's wrong A cheap model that is sometimes wrong can still save money, if you can detect the wrong answers and rerun them on the bigger model. The break-even is simple. If Haiku costs a fraction h of the big model's cost for the same subtask, rerunning failures on the big model beats using the big model throughout as long as the failure rate is below 1 minus h. For Sonnet in the example, that is 95%. For Opus, 97.5%. That sounds generous, and it is, but it hides the condition: the failures must be detected cheaply. A wrong summary that nobody catches does not cost $0.0055. It costs whatever it does downstream. So the real design question is not "which model is cheap" but "what is my cheap check". Examples that work: a test that must pass, a schema the output must validate against, a number that must appear in the source text, a database query that must return rows. Examples that don't: "read it and see if it looks right." This is the same lesson I wrote about earlier this week for coding agents: trust the checker you did not let the agent write. ## What the capability gap looks like Anthropic's own table shows the trade. On Terminal-Bench 4.0, a command-line task benchmark, Anthropic reports 39.2% for Haiku 5.5 and 70.6% for Sonnet 5.5. That is why Anthropic says the bigger models remain better for complex agentic coding. The cheap model is for narrow, well-specified subtasks, not for the lead role. I read the benchmark table from a flattened page, so check the original before quoting it. Customer quotes on the page point the same way. Rogo describes a big model building a deck while a Haiku subagent pulls a revenue line out of a 10-K. Cognition says Haiku 5.5 as the sidekick in its Devin Fusion setup holds a FrontierCode score of 66.2 while cutting cost and latency. That score is for the combined system, not for Haiku alone. ## Two price changes landed the same week Anthropic also halved Sonnet 5.5 cache reads, from $0.20 to $0.10 per million tokens, which it says makes Sonnet 5.5 about 20% cheaper on most agentic work. So the lead model got cheaper too. If you quoted old cache prices anywhere, including in my own earlier posts, check them against the pricing page. One caution on the headline discount: Haiku 5.5 uses an updated tokenizer and, per Anthropic, "slightly more tokens per task" than Haiku 4.5. The exact increase depends on the workload, so a 90% lower price is not a 90% lower bill. ## Rules for using a cheap subagent 1. Give it narrow tasks with a clear definition of done. 2. Keep its prompt under 100,000 tokens to stay in the cheaper tier. 3. Make every output checkable by something cheaper than a person. 4. Escalate failures to the bigger model automatically, and count those reruns. 5. Measure cost per accepted task, not cost per token. 6. Keep the planning and hard reasoning on the lead model. ## What I could not verify I tested nothing. All prices are from Anthropic's pages as of October 8, 2026 and may change. Benchmark scores and customer results are the company's own. Anthropic's page does not state tokens-per-task figures or per-task dollar costs, so my worked examples are assumptions, not Anthropic's numbers. The page did not state a context window or consumer-app availability. Sources: Anthropic, Claude Haiku 5.5 (anthropic.com/claude-haiku-5-5); Anthropic API pricing (platform.claude.com/docs/en/about-claude/pricing). Accessed October 8, 2026. Where in your agent would a $0.10 model do the reading, and what would check its work? #AI #AIEngineering #LLM #Agents --- **Work with me:** I take 1:1 calls on mobile architecture reviews, mobile and AI questions, and startup tech consults. Book a slot on Topmate: [topmate.io/sairamkotha](https://topmate.io/sairamkotha)