AI models · Updated 2026-10-31

Claude Haiku 5.5 vs GPT-6 Luna for bulk AI tasks

Compare two low-cost model APIs for classification, extraction, and other repeated jobs, with a cost example and a quality test.

The short answer

Start with both on a small, labeled sample. Claude Haiku 5.5 and GPT-6 Luna are positioned for focused, high-volume tasks, and their listed base token prices are close at short prompt lengths. The winner is the model that meets your quality and latency target on your own examples, after counting the full cost of errors and retries.

What the current pages say

Anthropic describes Haiku 5.5 for classification, extraction, routing, and subagent tasks. It lists a 1-million-token context window, up to 128,000 output tokens, and base API prices of $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens. Prompts over that threshold use higher rates. Its tokenizer also differs from earlier Haiku models, so a text may count as more tokens than before.

OpenAI describes GPT-6 Luna as its most efficient model for focused, high-volume work. Its catalog lists a 1.05-million-token context window, 128,000 maximum output, and $0.10/$0.50 per million input/output tokens at the listed rate. Pricing varies by context length and processing mode, so check the current table for your request size.

Put the bill in context

At those base rates, 10 million input tokens and 2 million output tokens cost about $2 for either model: $1 of input plus $1 of output. That assumes Haiku requests stay within the lower prompt-length tier and ignores caching, batches, tools, long-context pricing, and retries. If a Haiku request exceeds 100,000 prompt tokens, the higher rate applies to that request, including cached input. For Luna, check the relevant context and processing tier rather than treating the catalog headline as every request’s price.

The tiny base bill can hide an expensive error. If a wrong classification sends a customer request to the wrong queue, calculate review and correction time as well as API cost. For extraction, measure field-level accuracy and how often a human must repair the result. A slightly more expensive model can win overall if it substantially reduces those downstream costs.

A practitioner’s bake-off

Build a sample that includes common cases, borderline cases, messy formatting, and a few examples where guessing is unacceptable. Have a person label the expected answer before either model runs. Use the same instructions and output schema for both; make invalid JSON, missing fields, and unsupported claims count as failures.

Measure exact-match or task-specific accuracy, invalid-output rate, p50 and p95 response time, and total cost including retries. Then inspect every failure category. If one model is more reliable on ambiguous inputs, route only those cases to it while leaving easy cases on the cheaper path. Keep a confidence threshold only if your validation set proves that it separates reliable from unreliable answers.

Which should you try first?

Use Haiku 5.5 when your workload matches Anthropic’s documented high-volume examples and your prompt lengths stay in its lower pricing tier. Try GPT-6 Luna when your stack already uses OpenAI’s API or you want to compare its focused-task route. Neither vendor’s positioning proves a benchmark winner for your data. Re-run the sample after a model alias, tokenizer, prompt, or price changes. The broader AI model suite guide explains how to keep a stronger fallback for the cases that need it.