This page compares two current models through their API on one job: proofreading a finished business document without rewriting it. It covers errors caught, wording left alone, cost and prompting, and a fair way to test both.
Oct 6, 2026 · 10 min read
Opus 5.5 has the best public evidence for fixing real errors and leaving approved wording alone. GPT-6.1 Sol is the cheaper first pass when a person reviews every edit.
The split comes from a small Polish-language test where Opus 5.5 corrected all thirty seeded errors in one test and GPT-6.1 Sol corrected twenty-eight of thirty in a separate one1, 2. A separate controlled test found that Opus made no word changes beyond the reference correction1. On the English ErrataBench, Opus 5.5 had a 0.4% incorrect-repair rate, though Sol has no published result there4.
Public evidence does not show that Sol routinely rewrites text or invents fixes. Its clearer weakness in the proofreading examples is missed errors, and in a nineteen-error test it corrected eighteen with no unrequested style changes3. For important documents the safest workflow is Opus 5.5 proposing a minimal change list while a person accepts or rejects each edit. Both are current API models, Opus 5.5 released on September 22, 2026 and Sol on September 29, 202612, 13.
Approved clauses and defined terms must stay word for word. Opus 5.5 has the best evidence for fixing real errors while leaving valid wording alone, so pair it with a person who approves each change.
Investor-facing documents cost the most when a proofreader changes approved wording. Opus 5.5 corrected every seeded error in the closest test without touching other words.
You check many routine documents and a person reviews every edit. Sol costs $2 / 1M in and $10 / 1M out up to 272,000 input tokens and supports Structured Outputs for a machine-readable change list.
You want cheap coverage first and strict restraint where wording is locked. Use Sol as the low-cost first pass and Opus where wording is locked, with a person approving every edit.
This page compares the two models through their API in one neutral setup, not one model inside one app against the other inside another.
Proofreading here means typos, punctuation, grammar, accidental word omissions, agreement errors and inconsistent terminology in an otherwise finished document. We did not judge drafting, fact-checking or style rewrites. The score that matters is real errors corrected minus valid wording changed. Official docs come first, then small independent proofreading tests, ErrataBench and broader professional-work benchmarks, each with its caveats1, 4, 7.
We left tools out of the specs table on purpose. File upload, track-changes add-ons and similar features belong to the app around the model, so the same model can behave differently in a chat product, the API or a workspace. Judging them here would compare wrappers rather than proofreading.
The model facts that affect a proofreading job. Tool features are left out, since they depend on the app around the model.
Figures from Anthropic and OpenAI documentation, checked October 2026. The two vendors price and tokenize differently, so treat any cross-model cost comparison as directional, not exact.
The answer changes by job, so this table gives the better choice for each part of proofreading and the evidence behind it.
Better-choice calls map to dimensions the sources actually evaluated. Where the evidence is indirect or vendor-reported, the row says so.
Use the same document, glossary, prompt and effort setting for both. Ask for a list of proposed changes, then score what each fixed, missed and changed for no reason.
Include a clean document with approved but unusual wording, one with known typos and grammar errors, one with inconsistent names and capitalization, and one with ambiguous sentences that should be flagged. Add a long document with defined terms if you have one.
One prompt for both models that asks for a table of proposed changes instead of a silently rewritten document. Include the glossary and a do-not-change list. If you change the prompt mid-test, apply the change to both.
Give both the same document, glossary and effort setting, and run them through the API your team will use. API and chat-product results can differ because system prompts, tools and surrounding instructions differ, as OpenAI notes for its own evaluations13.
Do not edit the outputs first. Count real errors found, real errors missed, correct text changed, new errors introduced, format compliance and uncertain cases flagged instead of fixed, plus the human time to approve the changes. For commercial work hide the model names and use at least two reviewers.
No large public test runs both exact models on English business documents. Here is what each source helps judge and how much weight it deserves.
Small independent tests and single-site write-ups are a secondary signal and never a replacement for official docs. GPT-6.1 Sol was about a week old when we checked, so public testing of it is still thin.
Both models do better when the prompt says what counts as an error, what to leave alone and what to do when unsure.
Claude Opus 5.5 does best with explicit boundaries, the approved document kept apart from the instructions, and uncertainty defined as a reason not to edit. Test medium and high effort. ErrataBench found high strongest for this task, while medium is the API default4, 10.
GPT-6.1 Sol does best when the priority order and the output format are explicit. In the API, Structured Outputs can enforce one atomic correction per record. OpenAI recommends stating the exact writing style and structure needed rather than relying on defaults11, 15.
A Claude Opus 5.5 prompt: clear limits and a change table
You are performing final proofreading, not copy-editing.
Correct only objective spelling, punctuation, grammar,
missing-word and terminology-consistency errors.
Preserve approved wording, tone, sentence structure and
valid stylistic variants. Do not improve clarity, concision
or elegance. If a possible change is debatable, flag it
without changing it.
Return a table with location, original text, proposed
replacement, error type and confidence. If there is no
objective error, propose no change.A GPT-6.1 Sol prompt: priority order and a JSON list
Objective: find errors without rewriting.
Priority one is preserving every correct word.
Make a change only when you can name the violated rule or
show an inconsistent approved term elsewhere in the document.
Never substitute synonyms or improve style.
For uncertainty, return flag_only: true and leave the text
unchanged. Return JSON objects containing location, original,
replacement, reason, confidence and flag_only.Neither model is perfect. The useful question is where each one adds cleanup work, and what to change in the prompt or the workflow.
One question first. Would a needless change do more harm than a missed typo? Then follow the branch that matches most of your documents.
A starting point for your own test on your own documents
If approved wording is locked, or the document is legal, regulatory, investor-facing or contractual, start with Claude Opus 5.5. Ask for proposals only and require a person to approve each change1, 4.
If you process many routine documents and every edit will be reviewed anyway, start with GPT-6.1 Sol, ideally with a structured change list and an automated diff11. Up to 272,000 input tokens it costs $2 / 1M in and $10 / 1M out against Opus's $4 and $209, 11. Above that size Sol rises to $4 in and $15 out, so price alone is a weaker reason to pick it and a sample run is worth the time11.
For specialized terminology, either model can work if you supply a glossary, and Opus is the pick when restraint matters most. For non-English business prose, start with Opus 5.5 on the strength of the Polish and Spanish examples and verify it in your exact target language1, 5.
Playgram is not the right buy if your pipeline already sends documents to a single model from a script and nobody on the team compares outputs by hand. It helps when people want to send the same draft to more than one model and keep the shared context.
Claude Opus 5.5 has the strongest public evidence for fixing real errors while keeping valid wording. GPT-6.1 Sol is the cheaper alternative and does not look inclined to rewrite text, though it fixed fewer errors in the one exact-model test.
The limits are real. GPT-6.1 Sol was a week old when we checked on October 6, 2026, and no large English proofreading benchmark had published a result for it. The direct comparison is small and in Polish. Vendor professional-work benchmarks test other skills, reasoning settings change outcomes, and behavior shifts with prompts and setup. Treat this as a starting point and run a blind test on your own approved documents4, 7, 13.
The safest final step is to test on the shape of your own documents, with the glossary and house rules you actually use. A fair test needs the same setup for both models: the same document, the same prompt and the same place to run them, so the result reflects the models themselves. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first list of edits comes back. The cleaner the setup, the more the difference you see is really Claude Opus 5.5 vs GPT-6.1 Sol, and the less it depends on which one happened to be easier to reach that day.
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