Claude Haiku 5.5vsClaude Opus 5.5

Haiku 5.5 does not have one flat publisher list price for every prompt size. Anthropic publishes $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens, then $0.50 and $2.50 for prompts over 100,000 tokens. Opus 5.5’s published list price is $4 and $20 per million input and output tokens. Caching and batch pricing have separate rules; the numbers above describe standard input and output, not a total application bill.

For example, at publisher list prices, a request with 10,000 input tokens and 1,000 output tokens costs about $0.0015 on Haiku 5.5 and $0.06 on Opus 5.5, before caching or any platform-specific pricing. That is a pricing illustration, not a performance comparison. If an Opus call avoids retries or completes a harder task that Haiku cannot, cost per successful task may tell a different story.

GPTProto publishes live Haiku 5.5 and Opus 5.5 model pages, each with its own rate. Availability on GPTProto may vary by model. Check GPTProto’s Claude model catalog. The catalog shows the Claude models and live rates currently available through GPTProto.

Both models accept text and images, produce text, support a 1-million-token context window, and have a standard maximum output of 128,000 tokens, according to Anthropic’s model specifications. A matching context limit does not mean matching results on a long document. Test whether the model finds the right evidence, follows instructions, and stays reliable as prompts grow. Likewise, the maximum output is a limit, not a recommendation to request a 128,000-token response.

Both models support adaptive thinking. Haiku’s effort setting lets you trade more reasoning for additional time and cost. Opus 5.5’s adaptive thinking cannot be disabled, although effort can be adjusted. If you are migrating an existing agent, validate reasoning and tool-call behavior instead of assuming that changing a model ID preserves every behavior.

Use a small evaluation set drawn from your real workload: routine cases, long-context cases, and failures that matter to users. Send the same input, instructions, tools, and grading criteria to both models. Track task success, retry rate, total input and output tokens, and end-to-end latency. Then compare cost per successful task, not only cost per million tokens. Anthropic has published benchmark results for each release, but the release materials do not provide a single controlled, head-to-head result for every task your application may run. Avoid declaring an overall winner from unrelated benchmark charts.