Claude Opus 4.5, Gemini 3 Pro or GPT-5.1: which should you use for work?

In less than a month, the three biggest AI labs have each shipped a new top model. OpenAI updated ChatGPT to GPT-5.1 on November 12, Google released Gemini 3 Pro on November 18, and Anthropic released Claude Opus 4.5 on November 24. Each announcement came with charts showing it ahead on some tests. If you use AI at work, the practical question is simpler: which one should you open on Monday morning?

The honest answer is that the gaps between them are small on paper and larger in practice, depending on the job. A model that writes the best code may not write the best client email. So start with what the vendors say, then test on your own work.

What each vendor says

The table below summarizes each company's own claims and figures. None of these numbers comes from an independent test, and each company picks the benchmarks it presents.

Two terms help here. SWE-bench Verified is a test of fixing real bugs in open-source code; GPQA Diamond is a set of graduate-level science questions. Both say something about reasoning, but neither measures whether a model writes a good meeting summary for your team.

A worked bake-off on three work tasks

Pick three tasks you do every week. Here is a typical set for an office role:

1. Summarize for a decision. Paste a real 6–10 page report and ask: "List the three decisions this report asks us to make, the options for each, and the risks the authors mention. Quote page numbers."
2. Draft from notes. Paste rough meeting notes and ask for a follow-up email to a client: under 200 words, friendly, with the agreed next steps and dates.
3. Work with data. Upload a small spreadsheet export (remove anything confidential) and ask: "Which three products had the biggest drop in sales from Q2 to Q3? Show the calculation."

Use the same prompt and the same files with each model, in its strongest everyday mode. Copy each answer into a document labeled only A, B and C, so you don't know which model wrote which. Then score:

Check task 3 by hand or in your spreadsheet; it is the only one with an objectively right answer, and the one where a confident mistake costs the most.

An illustrative result might look like this: one model wins the summary, another writes the most natural email, and two get the numbers right while the third makes a rounding error. That pattern is common, and it is useful. It may tell you to use one model by default and a second for a specific job.

Cost and fit matter too

Price and access often settle a close race. If your organization already pays for one of these assistants, or for office software that has one built in, it may win on convenience alone. Developers should compare the API prices on each vendor's pricing page for their own volume, and note Anthropic's effort setting, which it says can reach Sonnet 4.5's best coding score while using far fewer output tokens. Privacy terms, data retention and admin controls deserve the same scrutiny as quality.

How do you run a fair comparison?

Keep everything the same except the model. Use identical prompts, identical files and comparable modes; comparing one model's quick mode with another's deep-thinking mode tests the setting, not the model. Start each test in a new chat, so earlier conversations and memory do not give one model extra context.

Score blind and decide your criteria before you read the answers. If you know which answer came from your favorite, you will read it more kindly. Ask a colleague to label the answers A, B and C if you can.

Run each task more than once. These models give different answers to the same prompt, so a single run can mislead. Two or three runs per task show whether a win is consistent. Use real tasks with known good answers where possible, such as a report you have already summarized yourself, so you can judge accuracy rather than style. Finally, repeat the test when a vendor ships an update; the ranking on your work can change within weeks.