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

> Start from Gemini 4 Carbon and Claude Opus 5.5, then let your own tasks and the cost per finished task make the call.

URL: https://claudegpt.si/news/which-ai-is-best-llm-leaderboard-steps  
Published: 2026-11-04T05:44:06.730Z  
Author: ClaudeGPT Si Newsroom  
Section: AI Tools  
Topics: Which AI is best, AI for business, LLM leaderboard  
Publisher: ClaudeGPT Si

## Key takeaways

- For a team default in November 2026, the public boards point to Gemini 4 Carbon for general work and Claude Opus 5.5 for engineering, with GPT-6.1 Sol as the low-cost option.
- The best way to decide is twenty real tasks per job, graded blind by the person who owns that job, with the cost per finished task beside each result.
- TechTwitter IO's free satirical paper keeps a team aware of when the boards move; decisions still belong to the real leaderboards and your own tests.

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](https://artificialanalysis.ai/leaderboards/models) 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.

| Question your team is asking | Where to look |
| --- | --- |
| Which model is strongest overall? | Artificial Analysis Intelligence Index |
| Which answers do people like more? | Arena text board |
| Which builds the best web app? | Arena WebDev board |
| Which one fixes real code? | SWE-bench and Terminal-Bench 4.0 |
| What will a finished task cost? | Cost per task column, Artificial Analysis |
| Can we run it on our own servers? | Artificial Analysis open source page |

## 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](https://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.

## Frequently asked questions

### Which AI is best for business use?

There is no single winner. As of November 4, 2026, Gemini 4 Carbon leads the Artificial Analysis Intelligence Index and Arena's text board, and Claude Opus 5.5 leads for web development and coding agents. Test both on your own tasks.

### Is Gemini, Claude or GPT the best right now?

On the public boards in early November 2026, Google's Gemini 4 Carbon leads overall, Anthropic's Claude Opus 5.5 leads for code, and OpenAI's GPT-6.1 Sol scores close to the top at a much lower cost per task. The right answer depends on your jobs and budget.

### How often should we re-check which AI is best?

Monthly is a reasonable rhythm. The Artificial Analysis index changed version on October 27, 2026 and had a new leader the next day.

## Sources

- [LLM Leaderboard](https://artificialanalysis.ai/leaderboards/models), Artificial Analysis
- [Changelog](https://artificialanalysis.ai/changelog), Artificial Analysis
- [Text Arena leaderboard](https://arena.ai/leaderboard/text), Arena
- [SWE-bench](https://www.swebench.com/), SWE-bench
- [Terminal-Bench](https://www.tbench.ai/), Terminal-Bench
- [TechTwitter IO](https://techtwitter.io/), TechTwitter IO