Claude Haiku 5.5 vs Sonnet 5.5

What changes from Haiku to Sonnet?

The models have the same published context and output capacities, so context size alone does not decide this comparison. The differences to investigate are task quality, responsiveness and cost at your chosen effort setting.

Data checked October 8, 2026: Haiku specifications and Sonnet specifications. Latency categories are the publisher's descriptions, not measured response times in HaikuChat. Comparing both models at their defaults also compares different effort settings.

The 20× base-rate gap needs context

At the base rates, Sonnet's input and output token prices are twenty times Haiku's. With the same uncached 20,000 input and 2,000 billable output tokens, the model-token arithmetic is $0.003 for Haiku and $0.06 for Sonnet.

That ratio is not universal. Above 100,000 input tokens, Haiku's higher rates make the same-token price gap fourfold instead of twentyfold. Caching also changes the mix: the published Sonnet cache-read rate is $0.10 per million tokens, so a workload dominated by reused prefixes is not well described by the uncached ratio.

Reasoning tokens, longer answers and repeated failed attempts affect the cost of a usable completion. Choose a fixed output requirement, then measure what each model actually consumes. These are model-rate calculations, not the price of a HaikuChat message. See HaikuChat pricing for this workspace's plans and usage rules.

Evidence for testing Sonnet on harder work

The Haiku release evaluation reports the following side-by-side scores:

Source: October 7 published evaluations. GDPval-AA is presented as a rating, not a percentage. The table comes from the model publisher; HaikuChat has not independently reproduced these runs.

The larger terminal-task gap is a reason to test Sonnet on complex coding workflows. It is not proof that Sonnet will improve every short summary. Tool access, evaluation scaffold, effort and the exact task all matter. A summary task with a known answer gives you a more relevant check than a coding leaderboard.

Three workloads, three different decisions

Meeting notes with explicit owners and dates: try Haiku first. Ask for key points, decisions, action items and unresolved issues. A stronger model is unnecessary if the smaller one already preserves every required fact and the result is easy to verify.

An inconsistent brief: test Sonnet when several emails contradict one another, requirements depend on earlier decisions, or the output needs a defensible recommendation. Require the model to separate facts from assumptions and list unresolved conflicts. Judge whether that added interpretation is correct, rather than rewarding extra prose.

A code change across a repository: split a local transformation from a multi-file debugging problem. Haiku is a candidate for a focused explanation or a small, well-specified edit. Test Sonnet when the change requires tracing behavior, coordinating several files and checking the consequences. This is a model-selection example; HaikuChat does not run your code or edit a repository.

If Sonnet still misses dependencies or needs too much correction on a complex task, the next comparison is Haiku vs Opus. If your work is mostly small-model tasks, Haiku vs GPT-6 Luna is the closer comparison.

Upgrade after identifying the failure

Before changing models, make the requested output and missing-data rules explicit. If an extraction prompt never tells the model what to do with absent dates, a more expensive model is not a substitute for defining the task.

Then collect the failures that remain: a wrong amount, a missed contradiction, an invalid table, or an unsupported conclusion. Run those examples through Sonnet with the same source material. Keep easier examples too, so the comparison does not consist only of cases selected to favor the upgrade.

If Sonnet fixes the important failures, use it for that class of work. If both produce acceptable answers, favor the workflow with less waiting and checking. For a simple browser task, open the Haiku workspace; for the full set of choices, return to the model comparison guide.

Questions about this comparison