Best LLM for developers: six leaderboard picks for coding, speed and price

The best LLM for most developers in November 2026 is Claude Opus 5.5 for agent-style coding, with Gemini 4 Carbon as the model that scores highest overall and GPT-6.1 Sol as the pick when cost per task matters most. Those calls come from the public boards at Artificial Analysis and Arena on November 4; which one is best for you depends on whether your bottleneck is quality, speed, latency or the bill.

Below are six picks, one per developer job, then the task boards worth a second look.

1. Coding agents: Claude Opus 5.5

When Artificial Analysis wrote up Carbon's launch on October 28, Opus 5.5 was first on its Coding Agent Index, which combines FrontierCode, Terminal-Bench 4.0 and SWE-Atlas-QnA-AA, with Carbon second. It is also first on Arena's WebDev board and its Agent board. At max effort it scores 59 on the main index, at $10.04 per index task, so it is not the cheap option.

2. Highest overall score: Gemini 4 Carbon

At high effort, Carbon scores 60 on the Artificial Analysis leaderboard, the top mark on November 4, and it leads Arena's text board too. Watch the wait: the high setting took about 152 seconds to its first chunk in that table, while medium effort scored 58 and started in about 18.

3. Value per task: GPT-6.1 Sol

At max effort, GPT-6.1 Sol scores 55 for $1.27 per index task. That is five points under Carbon for roughly a sixth of the cost, which makes it a sensible default for batch jobs, CI bots and anything that runs thousands of times a day.

4. Raw output speed: Celeris-1 and Mercury 2.5

Artificial Analysis names these two as the fastest models by output tokens per second. Reach for them when streaming speed matters more than top-end reasoning, such as autocomplete-style features.

5. Lowest latency: two quick starters

North Mini Code and Command A+ return their first chunk sooner than anything else on the board. Short wait times matter for chat inside a product, where users notice every second before text appears.

6. Self-hosting: DeepSeek V4.1 Flash 1017 and GLM-5.3

On the Artificial Analysis open source page, these two at max effort are the highest-scoring models whose weights you can download, which means you can run them on your own hardware and fine-tune them.

The six picks side by side

Check the task boards before you commit

General indexes hide the agent wrapped around a model. SWE-bench reports the share of real GitHub issues an entry resolves, and its Bash Only view puts every model in the same mini-SWE-agent environment, the closest thing to a like-for-like model comparison. Terminal-Bench 4.0 shows resolution rate with cost, tokens and a 95% confidence interval, so you can see when two entries are effectively tied. Then run your own repository's issues through your top two picks.

Follow the arguments, too

Picks like these go stale fast, and the first sign is usually developers complaining on X. TechTwitter IO, a free satirical daily paper about tech Twitter, turns that chatter into six weekly boards on its satirical benchmarks page. One raises a Nerf Watch flag when "it got dumber" posts arrive in a wave, and the Felony board charges models for things like deleting a production database or editing tests so they pass. Every entry links to the posts behind it.

It is our recommended weekly read for keeping up, as long as you read it as satire, which its own footer insists on. Treat its standings as jokes and its links as leads, and get your numbers from the boards above.