Which AI model should power your agent? Compare Grok, ChatGPT, Claude and Gemini on tool calling, long tasks, cost and speed — plus a free scoring tool you can run.
Published 2026-10-14
TL;DR
There is no single best model for agents. Pick on four things: how reliably it calls tools, how well it stays on track over long tasks, cost per run, and speed. Test your own task on two or three models and score them — the runnable scorer below helps.
Key takeaways
Each big lab now ships its own consumer agent: Grok Bot (xAI), Dots (OpenAI), and Muse (Meta).
For agents you build yourself, tool-calling reliability matters more than chat quality.
Agents make many calls per task, so cost and speed add up fast.
Benchmarks go stale in weeks — test on your own task.
The 2026 agent landscape
This fall, every major lab launched a consumer agent: xAI’s Grok Bot, OpenAI’s Dots and Meta’s Muse (full explainer). Developers building their own agents usually choose among Grok, GPT, Claude and Gemini models through their APIs.
What matters for agents
Tool calling: does it return valid, well-formed actions every time?
Long tasks: does it stay on goal after 10+ steps?
Cost: an agent may make 20 calls per task.
Speed: slow steps make interactive agents feel broken.
Score models for your use case
Fill in your own ratings after testing — the numbers below are placeholders, not benchmark results.
How to test fairly
Use the 50-line agent as your harness, swap the model through OpenRouter, and run the same 5 tasks per model. Log failed tool calls and total cost — those two numbers usually decide it.
Frequently asked questions
Which model is best for coding agents?
It changes with every release. Run the same small coding task on each candidate, count how often tool calls succeed, and compare cost per finished task.
Can I switch models without rewriting my agent?
Yes, if your loop expects a fixed JSON action shape. Routers like OpenRouter expose many models behind one API, so switching is a one-line change.