A predictable business workflow
Start with a narrow task and explicit success criteria. Check documented tool support and validate each output before downstream actions.
DECISION GUIDE
Choose an AI for automation
Evaluate the whole task loop: plan, call tools, recover from failure and finish within your cost and permission limits.
Start with the job you need done
Computer or browser tasks
Inspect the benchmark environment and available tools. A score in a controlled computer-use test does not guarantee success on your website or app.
Long-running autonomous work
Measure retries, recovery and cost per completed task. A single successful demonstration is not evidence of dependable unattended operation.
Read the numbers without overreading them
Agent benchmarks
Results depend on the harness, tools, task set and attempt count. Compare matching configurations before interpreting a gap.
Tool support versus performance
A documented tool-use capability means the feature is supported. It does not measure reliability across your workflow.
Cost per completed task
Cheap tokens can still produce expensive tasks if retries and long reasoning dominate. Estimate usage, then measure a real trial.
Inspect agents results and sources → · How scoring works → · Evidence gaps →
Models with comparable agents evidence
These are starting points from our current comparison pool. Category scores are relative to covered tests; they are not a universal recommendation.
Claude Opus 5.5
agents: 100/100 · Moderate confidence · 85% overall category coverage.
Read the practical guide → Follow updates →DeepSeek V4.1 Flash
agents: 100/100 · Moderate confidence · 85% overall category coverage.
Read the practical guide → Follow updates →Claude Sonnet 5.5
agents: 80/100 · Moderate confidence · 73% overall category coverage.
Read the practical guide → Follow updates →Gemini 3.8 Flash
agents: 60/100 · Moderate confidence · 85% overall category coverage.
Read the practical guide → Follow updates →Run a small, useful trial
- Use bounded tool permissions and explicit spending limits.
- Test failure cases: unavailable tools, bad input and ambiguous instructions.
- Record task completion, retries, latency and total cost.
- Require review for external messages, purchases and irreversible actions.
Keep the prompts, model version and settings fixed across candidates. Include failures in your notes. Choose using your task results alongside the published evidence.
Keep your decision current
Save your comparison and follow the shortlisted models. Return when independent results, prices or capabilities change; a new announcement alone does not establish better performance.
What changed for my models? ↗ This week’s verified changes →Explore another workflow: Choose an AI for coding →Choose an AI for research →