GPT-5.5 vs Claude Opus 4.8

How we compared them
One task at a time

We are not claiming one of these two is the better model. Which one leads depends on whether the hard part of a job is reading a long input or following a narrow instruction.

Three jobs have a full write-up behind them, and eight further articles put one of these two models against a different one. The cards below open all three, and the index further down lists the rest, so nothing here has to be taken on trust. Where a figure is one the vendor computed itself, the row says so.

What we compared is set out underneath. First the published rates and limits both models bring to any job, then the model-level dimensions where a graded board separates these exact versions. Those hold whatever you are doing. Which of the two to reach for does not, which is why the jobs come first.

One thing to be clear about before the tables. This page compares the two models through their APIs in one neutral setup, not the apps around them, so an IDE integration or a document add-on is not part of anything here.

By the job
Which model wins which work

Neither model wins in general, so this pair is settled one job at a time. Each card names a job we tested, says which model took it and why, and opens the full test behind that answer. Three jobs on this pair have that test, and the index further down carries the rest of the library.

The shared facts
What each one costs and holds

The published figures both models bring to any job. The last column reads them for the pair rather than for one task.

Head to head
How they compare beyond one task

The general layer, underneath the jobs above. These are model-level dimensions graded on the exact versions, so they hold whatever the job is. Two of the strongest rows rest on figures the vendor computed itself and the evidence column says so.

Everything we tested
Both models across the library

Every article on this site that puts one of these two models under a graded test, grouped by model. The three on this exact pair are the cards higher up the page.

What this cannot tell you
Where the evidence runs thin

Two of the largest gaps on this page were computed by one of the two vendors, and the pair itself is a generation behind what either vendor now recommends.

The long-context and knowledge-work figures come from Anthropic's own system card, with the GPT-5.5 numbers produced by Anthropic rather than published by OpenAI. The coding row is not a head-to-head at all: GPT-5.5 has a score on that board and Claude Opus 4.8 has no entry. The editorial test behind the prose row ran eight assignments and was graded by a model, and the legal revision figures come from one vendor of legal software rather than a neutral board.

The bigger caveat is the pair. OpenAI now points to the GPT-5.6 family and Anthropic ships Claude Opus 5 at the same price as Opus 4.8, so a team choosing today is choosing between two previous flagships. Everything here still describes those two versions accurately, and both are still served, but the honest reading is that this page settles an old question rather than the current one.

Playgram is not the right buy for everyone either. If one person needs one model and nothing else, a single vendor subscription is simpler and cheaper than a workspace built for a team.

The safest last step is to test the shape of your own material rather than a generic prompt from the internet. A fair test needs the same setup on both sides: the same sources, the same prompt and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, because most teams end up running one model in one app and the other somewhere else on a separate subscription, which tilts the comparison before the first answer arrives. The cleaner the setup, the more the difference you see is really GPT-5.5 against Claude Opus 4.8.

Frequently asked
questions

Related comparisons