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Claude Haiku 5.5 is the low-cost, high-throughput choice; Claude Opus 5.5 is the premium choice for difficult, multi-step reasoning and agentic coding. At Anthropic’s published October 2026 rates, Haiku costs $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens. Prompts above that threshold cost $0.50 and $2.50. Standard Opus costs $4 per million input tokens and $20 per million output tokens, while Opus Fast costs $8 and $40. Your actual bill depends on prompt and completion volume, prompt length, caching, batch processing, provider and region—not simply on the model’s name.
This guide explains the price tiers, shows transparent cost calculations, maps each model to appropriate workloads, and gives an implementation and benchmarking plan for choosing between claude-haiku-5-5 and claude-opus-5-5.
What are Claude Haiku 5.5 and Opus 5.5?
They are two Claude models announced in September and October 2026. Anthropic announced Claude Opus 5.5 on September 22, 2026, and Claude Haiku 5.5 on October 7, 2026. Both are exposed through the Claude Platform API and are also listed for AWS, Google Cloud and Microsoft Foundry deployments. A provider listing does not guarantee identical prices, regions or quotas, so verify the current price sheet and regional availability where you plan to run production traffic.
The Tool Desk
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|---|---|---|---|
| Claude Haiku 5.5 | Fastest standard-speed Claude model; optimized for volume and cost sensitivity | Summaries, classification, compaction, routing, database queries, customer support, browser use and subagent tasks | claude-haiku-5-5 |
| Claude Opus 5.5 | Highest-capability option in this comparison for complex agentic coding and knowledge work | Long-running codebase migrations, audits, difficult planning and other multi-step tasks | claude-opus-5-5 |
Anthropic’s Applied AI executive Alex Wang describes Haiku as a model that works alongside larger Claude models for “summarization, classification, routing, and compaction” in products and agent systems. That is a positioning statement, not an independent quality benchmark. Anthropic also publishes company and tester evaluations for Opus; treat those as vendor-reported results and validate them on your own prompts before making a quality or reliability claim.
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How much does each model cost per million tokens?
The following are Anthropic-published API rates per one million tokens. Haiku has a prompt-length tier; Opus’s standard rate does not use the same 100,000-token breakpoint.
| Model and mode | Input price | Output price | Pricing qualification |
|---|---|---|---|
| Haiku 5.5, prompt up to 100K tokens | $0.10 | $0.50 | Applies to prompts at or below 100,000 tokens |
| Haiku 5.5, prompt above 100K tokens | $0.50 | $2.50 | Higher tier for prompts over 100,000 tokens |
| Opus 5.5, standard mode | $4 | $20 | Standard Anthropic API mode |
| Opus 5.5, Fast mode | $8 | $40 | Anthropic’s separately priced Fast mode |
At the low Haiku tier, one million input tokens costs one-fortieth of standard Opus input and one million output tokens costs one-fortieth of standard Opus output. That ratio is useful for budgeting, but it does not predict total savings unless both models receive comparable prompts and produce comparable completion lengths.
Anthropic says Haiku 5.5 is about 75% cheaper to run on average than Haiku 4.5 under its stated mix of prompt lengths and tokenization changes. For prompts up to 100K, Anthropic lists a 90% reduction versus Haiku 4.5; above 100K, it lists a 50% reduction. Those percentages compare Haiku 5.5 with the previous Haiku model, not Haiku with Opus.
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What does Haiku’s 100,000-token pricing threshold mean?
The breakpoint is based on prompt length, not on the number of output tokens you request. A request with a 120,000-token context falls into Haiku’s above-100K tier, so both its input and output rates use the higher figures shown in the table. Keep the qualification attached to every estimate: the tier is determined per request, and your provider may expose additional billing rules.
Why long context can change the bill
Large system instructions, retrieved documents, conversation history, tool results and code files all contribute to the prompt. A workflow that normally costs cents can move into the higher tier when it attaches a complete repository or an untrimmed transcript. Track prompt-token counts in production rather than assuming that a nominally “small” user message means a small request.
How to stay below the breakpoint when appropriate
- Summarize old conversation turns before appending new ones.
- Retrieve only the files or passages relevant to the current task.
- Remove duplicate tool output and repeated system instructions.
- Split independent jobs into separate calls instead of sending an entire archive each time.
- Use prompt caching where your provider supports it; Anthropic identifies caching as a possible saving method, but the amount saved depends on request details.
How do I calculate an API request’s cost?
Convert each token count to a fraction of one million and multiply by the applicable input or output rate. For Haiku, select the tier from the prompt length first; for Opus, select standard or Fast mode.
Formula: (input_tokens ÷ 1,000,000 × input_rate) + (output_tokens ÷ 1,000,000 × output_rate).
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| Example request | Calculation | Estimated API charge |
|---|---|---|
| Haiku, 80K input and 2K output | (80,000 ÷ 1,000,000 × $0.10) + (2,000 ÷ 1,000,000 × $0.50) | $0.009 |
| Haiku, 150K input and 3K output | (150,000 ÷ 1,000,000 × $0.50) + (3,000 ÷ 1,000,000 × $2.50) | $0.0825 |
| Opus standard, 50K input and 5K output | (50,000 ÷ 1,000,000 × $4) + (5,000 ÷ 1,000,000 × $20) | $0.30 |
| Opus Fast, 50K input and 5K output | (50,000 ÷ 1,000,000 × $8) + (5,000 ÷ 1,000,000 × $40) | $0.60 |
These examples assume the listed Anthropic rates, no cached-token adjustment, no batch adjustment, and no third-party markup. They are per-request estimates, not monthly bills. To estimate a month, multiply the representative request cost by the number of requests, then add retries, tool calls and any other billable model calls.
A workload-level example
Suppose 10,000 Haiku requests each contain 80K input tokens and 2K output tokens. At the low-tier example cost of $0.009, the model portion would be about $90 before any caching, batch, platform or regional adjustments. If the same request shape used standard Opus, the cost would be $3,600 because the calculation is $0.20 of input plus $0.04 of output per request. The comparison is illustrative: changing the model can also change answer length, retries and the number of calls your application needs.
When should I use Claude Haiku 5.5?
Start with Haiku when the job is frequent, latency-sensitive, constrained by a clear output format or easy to verify automatically. Its low per-token rates make it practical for large request volumes, while its standard-speed positioning targets fast interactive and background workflows.
Good fits for Haiku
- Summarization and compaction: Reduce long conversations, logs or documents before passing the result to a larger model.
- Classification and routing: Assign labels, priorities, queues or tool routes when the taxonomy is defined.
- Customer-support automation: Draft short replies, detect intent and retrieve the next approved action.
- Database and browser tasks: Turn a structured request into a query or navigate a known workflow with validation around the model.
- Subagents: Handle repetitive subtasks inside a larger coding or research agent, leaving the harder synthesis to Opus or another larger model.
- High-volume extraction: Pull fields from invoices, tickets or forms when a schema and confidence checks are available.
Haiku is not automatically the right choice merely because it is cheaper. If a wrong classification triggers an expensive downstream action, include validation and measure the error cost. For open-ended reasoning, ambiguous requirements or code changes that span many interacting files, test whether Haiku can maintain the required context before committing to it.
When is Claude Opus 5.5 worth the higher price?
Choose Opus when the primary requirement is difficult reasoning across many steps, especially when a failed attempt costs more than a premium model call. Anthropic positions Opus 5.5 for complex agentic coding and knowledge work, including long-running codebase migrations and audits.
Typical Opus candidates
- Planning and executing a migration that touches many services, interfaces and tests.
- Auditing a large codebase for subtle security, reliability or compatibility issues.
- Resolving ambiguous requirements that need clarification, decomposition and verification.
- Producing a detailed technical analysis where omissions are more costly than latency.
- Supervising tools and subagents over a long sequence while preserving a coherent goal.
Opus Fast costs twice the standard Opus input and output rates. It can make sense for an interactive product where waiting is materially harmful, but price alone does not establish a fixed latency improvement for every provider, region or prompt. Measure time to first token, total completion time and successful-task rate in your deployment.
What is the practical difference in speed, reasoning and task economics?
Haiku optimizes the economics of many smaller calls; Opus optimizes the chance that a complex call reaches a useful result without extensive orchestration. The best system may use both rather than treating the models as mutually exclusive.
| Decision factor | Haiku 5.5 | Opus 5.5 |
|---|---|---|
| Per-token cost | Lowest; two tiers based on prompt length | Much higher standard rate; Fast mode doubles it |
| Throughput strategy | Many short or medium calls, often in parallel | Fewer, more consequential calls |
| Reasoning profile | Constrained, repeatable tasks and subagent work | Complex, open-ended, multi-step work |
| Latency positioning | Anthropic’s fastest model at standard speed | Fast mode available at a premium |
| Best cost control | Keep prompts below 100K when feasible; use caching or batch where applicable | Control call count, output length and Fast-mode usage |
There is no established independent head-to-head benchmark that proves one model is universally better. Define “better” as a measurable outcome—accepted code patches, extraction accuracy, escalation rate, latency or cost per successful task—and test both models on the same workload.
How should I choose between Haiku and Opus?
Use a staged decision rather than selecting by model prestige or input price alone.
- Describe the unit of work. Record the user goal, required tools, acceptable latency, output schema and failure impact.
- Measure tokens. Capture input and output tokens for representative requests, including the largest prompts that occur in production.
- Start with Haiku for constrained work. Add schema validation, deterministic checks or a human-review queue where errors matter.
- Escalate hard cases. Route requests with ambiguity, repeated failures, long plans or cross-file dependencies to Opus.
- Compare successful-task cost. Divide total model spend by tasks that meet your acceptance criteria, not by raw request count.
- Recheck regional pricing. A provider’s price, tax, quota and location rules may differ from Anthropic’s direct platform.
A simple routing policy
For example, a support pipeline can use Haiku to classify intent and summarize history, then invoke Opus only when the issue requires a multi-system diagnosis or a migration plan. Keep the routing signal explicit—such as a class, confidence score or escalation reason—so you can audit why a premium call occurred.
How can I benchmark the models fairly?
Build a fixed evaluation set from real, anonymized tasks and run both models with the same context, tools, output limits and stopping rules. Do not compare a short Haiku prompt with a much longer Opus prompt and then attribute every difference to model capability.
- Sample easy, typical and worst-case requests, including prompts near and above Haiku’s 100K threshold.
- Freeze the system instructions, retrieved documents, tool definitions and temperature-related settings that your API configuration supports.
- Score task success with objective checks where possible: tests passing, fields correct, policy decisions accurate or human review accepted.
- Record input tokens, output tokens, retries, tool calls, time to first token and total latency.
- Calculate cost per successful result for Haiku, Opus standard and, where latency matters, Opus Fast.
- Inspect failure modes manually. A model that is cheaper per call may be more expensive if it causes repeated retries or human intervention.
Repeat the test after prompt, tool or model-version changes. Anthropic’s launch claims and tester results can guide hypotheses, but they are not substitutes for measurements on your data.
What API and deployment details should I verify?
Use the exact model identifiers claude-haiku-5-5 and claude-opus-5-5 in the provider’s current API documentation. Anthropic lists direct Claude Platform access plus AWS, Google Cloud and Microsoft Foundry. Confirm that the identifier, region, context limit, rate limit, logging policy and billing unit are supported in the account and location you will use.
Before production
- Pin the model identifier in configuration rather than scattering it through application code.
- Log token usage and latency without storing sensitive prompt content unnecessarily.
- Set application-level budgets and per-request output limits.
- Handle rate-limit, timeout and transient provider errors with bounded retries.
- Validate structured output before executing database writes, shell commands or other consequential actions.
- Document whether traffic uses standard or Fast mode and whether it is direct or routed through a cloud provider.
Anthropic states that Opus inference restricted to the United States is available at 1.1 times the input and output pricing. Treat that as a region-specific Anthropic statement and check whether it applies to your account and chosen platform before using it in a budget.
How do caching and batch processing affect the comparison?
Prompt caching can reduce the cost of repeatedly sending stable instructions or documents, while batch processing can change the economics of non-urgent workloads. Anthropic identifies both as possible savings mechanisms for Haiku, but the exact discount and eligibility depend on request shape, cache hits, batch terms and provider. Do not apply an assumed percentage to a forecast without confirming the current terms.
For either model, caching is most useful when a large prefix is reused across many requests. If every prompt is unique, a cache may add complexity without reducing billed tokens. Batch processing is appropriate only when your product can tolerate asynchronous completion and the platform’s service-level behavior matches your requirements.
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- Comparing sticker prices only: A lower rate can lose if it causes extra retries, longer prompts or manual review.
- Ignoring the 100K tier: A small increase in retrieved context can move an entire Haiku request into the higher rate.
- Assuming Fast mode is a guarantee: Fast pricing is clear; the latency benefit must be measured in your region and workload.
- Using a larger model for every subtask: Classification, compaction and routing often do not need premium reasoning.
- Using a smaller model without safeguards: Add validation and escalation when an incorrect answer has operational consequences.
- Mixing provider invoices with Anthropic rates: Cloud-platform billing, taxes, regional constraints and account terms may differ.
- Treating vendor evaluations as universal: Reproduce your important tasks and report the acceptance criteria.
Which Claude model should you use?
Use Haiku 5.5 first for high-volume, speed-sensitive and narrowly defined work, especially when prompts stay within the 100K tier and outputs can be checked automatically. Use Opus 5.5 when complex reasoning, long-running coding work or difficult knowledge tasks justify a much higher per-token price. A mixed architecture—Haiku for preprocessing and routine subagents, Opus for escalation and synthesis—often gives a better cost-and-quality balance than routing every request to one model. Revisit the choice whenever Anthropic or your cloud provider changes prices, availability or model behavior.
Frequently Asked Questions
Can I switch from Haiku 5.5 to Opus 5.5 by changing only the model name?
Often the request shape is compatible, but do not assume identical output limits, latency, tool behavior or provider-specific parameters. Put the model identifier in configuration, run your schema and safety tests against both models, and review any prompts that rely on a particular response style.
Does the 100,000-token Haiku threshold include the model’s answer?
The published breakpoint is described in terms of prompt length. Output tokens still incur the tier’s output rate, so a prompt above 100,000 tokens can make even a short answer use the higher Haiku rates.
Is Opus Fast available everywhere Opus 5.5 is available?
The published information lists Fast pricing for Opus but does not establish identical availability across every cloud, region or account. Confirm the mode in the specific provider console and API documentation you will deploy.
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Recalculate after changes to prompt templates, retrieval volume, output limits, routing rules or provider. Also recheck official pricing and availability before a major launch because token rates and regional terms can change.
Quick Recap
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