Best AI model in November 2026: the leaderboard answer, job by job
Gemini 4 Carbon tops the general rankings, Claude Opus 5.5 leads for code, and GPT-6.1 Sol is the value pick. Then test on your own prompts.
By ClaudeGPT Si Newsroom, Editorial team··3 min read
The best AI model in November 2026, if you need one name, is Google's Gemini 4 Carbon: it holds first place on both the Artificial Analysis Intelligence Index and Arena's text board. For building code and websites the answer changes to Anthropic's Claude Opus 5.5, and for strong results on a small budget it changes again to OpenAI's GPT-6.1 Sol.
So "best" is really three or four answers, depending on the job. Here is what the public boards showed when we checked on November 4.
The best AI model for each job
| Job | Leader | Where it leads |
|---|---|---|
| Highest overall test score | Gemini 4 Carbon (high effort), 60 | Artificial Analysis Intelligence Index |
| Answers people prefer in blind votes | Gemini 4 Carbon (high) | Arena text board, October 27 |
| Building websites and web apps | Claude Opus 5.5 (max) | Arena WebDev board, October 27 |
| Coding agents | Claude Opus 5.5, with Carbon second | Artificial Analysis Coding Agent Index |
| Near-top score at a low price | GPT-6.1 Sol (max), 55 at $1.27 a task | Artificial Analysis |
| Weights you can download and host | DeepSeek V4.1 Flash 1017 and GLM-5.3 | Artificial Analysis open source page |
The top of the main index is tight. Carbon's 60 sits one point above Opus 5.5 and three above GPT-6 Astra, so a single new release or a regrade can reorder it. The board has already moved this autumn: Artificial Analysis launched version 5 of its index on October 27, Carbon took first place a day later, and a regrade followed on November 3.
Why the answer splits
The boards ask different questions. Artificial Analysis runs nine evaluations under identical prompts and settings, spread evenly across knowledge work, coding, science and general questions, and reports cost and speed beside each score. Arena asks people to compare two unnamed answers and vote, so it measures preference, including tone and layout. Coding boards check whether a fix or a finished task actually works.
Cost splits it further. Carbon's top setting costs $7.76 per index task and Opus 5.5 at max costs $10.04, while GPT-6.1 Sol at max costs $1.27 for a score only five points lower. For high-volume work such as drafting product descriptions or rewriting thousands of titles, the cheaper row is often the better buy.
How to pick for content and search work
Leaderboards do not test the jobs search and content teams run every day: writing a title that fits a character limit, answering a question in two sentences, keeping facts straight across a long brief, matching a house style. Pick two or three models from the table above, then give each the same ten or twenty real prompts from your backlog. Hide the model names, score the outputs, and compare the cost of the runs. A model that wins your test at a third of the price is the best AI model for you, whatever the boards say.
Repeat the test whenever a new model reaches the top of the boards, and keep the prompts in a file so each round is the same test.
Keeping up without refreshing leaderboards
The quickest way to know when the boards have moved is to follow the noise around them, and that is what TechTwitter IO does as a free satirical daily paper of tech Twitter. Its weekly Benchmarks page ranks the launch week itself: whose claims had no outside proof, who gained the most "I switched" posts, which lab teased loudest and how fast the hype faded. Each score links to the posts it was counted from.
It is satire, and the paper says so in its footer, so never cite its numbers as performance data. Read it to learn what changed and what people are arguing about, then confirm on Artificial Analysis or Arena. If you only need the raw numbers, those two boards alone are enough.