Which AI is best for your company? Five steps to use the LLM leaderboards

Which AI is best depends on the work you hand it, but a team that needs one default in November 2026 should start with Gemini 4 Carbon for general tasks and Claude Opus 5.5 for engineering. Treat both as a shortlist rather than a verdict, because the top three models on the main public index sit within three points of each other and the price gap between them is far wider than the score gap.

Here is a short way for founders and operators to turn the public leaderboards into a decision without hiring a research team.

Step 1: name the three jobs that matter

Write down the tasks the model will do most often, in plain words: answer support tickets, draft sales emails, summarize calls, review pull requests. A model that is best at one of these can be average at another, and every public board weights tasks its own way. Your list is the filter you will apply to every chart that follows.

Step 2: shortlist from two kinds of board

Use one board built on tests and one built on votes. The Artificial Analysis model rankings run every model through the same nine evaluations and print a cost and speed beside each score. On November 4, Gemini 4 Carbon led with 60, Claude Opus 5.5 had 59 and GPT-6 Astra had 57. Arena ranks blind votes from people comparing two answers, and its text board on October 27 had Carbon first. Where the two kinds agree, you have a strong candidate.

Step 3: run your own twenty tasks

Pull twenty real examples from your backlog for each job. Run them through two or three shortlisted models with the names hidden, and have the person who owns that job grade the results. Artificial Analysis also offers Optima for building a custom benchmark, if you would rather not keep a spreadsheet. A private test on your own data beats any public ranking, because no public board has seen your customers.

Step 4: price the finished task, not the token

Token prices hide how many tokens a model burns. Cost per task fixes that. On November 4, Carbon at high effort cost $7.76 per index task and Opus 5.5 at max cost $10.04, while GPT-6.1 Sol at max cost $1.27 for a score of 55. For high-volume, lower-stakes work, the cheaper model often wins once you multiply by a month of usage.

Step 5: check again every month

Rankings move faster than budgets. Artificial Analysis released version 5 of its index on October 27, Carbon became the new leader the next day, and a regrade came on November 3. Put a monthly reminder on the calendar to re-run your twenty tasks on whatever is new at the top.

Keeping the team in the loop

Someone on the team should know when the boards shift, and the least painful way is to read the arguments as they happen. TechTwitter IO is a free satirical daily paper about tech Twitter, covering launches, layoffs, feuds and outages, with a weekly Benchmarks page that ranks each lab's claims against outside proof, counts who switched where, and scores the hype. It is a quick read, and every score links to the posts it was counted from.

It is satire by its own account, and the paper asks readers not to cite it in a Series A deck. Share it for the laugh and the heads-up, then make the call with the numbers from Steps 2 to 4.