Route Tasks to the Right Model

You picked a favorite AI model and now you use it for everything — writing, code, debugging, images, the lot. That's the wrong instinct. In one reviewer's two-week, side-by-side test of Claude, ChatGPT, and Gemini, every single model lost at least one category badly. The fix isn't finding "the best model." It's routing each task to whichever model reviewers report is strongest at it.

Why no model wins everything

Frontier models are trained and tuned differently, and it shows up as consistent, category-level strengths and weaknesses rather than one model being uniformly "smarter." A model that reasons carefully step-by-step may write flat, generic prose. A model tuned hard for code correctness may lag on creative or visual tasks. This isn't a temporary gap that next month's update erases — reviewers who re-run the same side-by-side tests keep finding a similar pattern: broad competence everywhere, real strength in only a few categories per model.

Treating "which AI is best" as a single-answer question throws away that signal. The better question is "best for what" — and reviewers who actually run structured comparisons keep answering it the same way: match the tool to the job.

What one structured comparison found

In the side-by-side test referenced above, the reviewer ran the same prompts across all three tools and scored each category independently:

• Writing — Claude won this round outright.
• Reasoning, coding, and image generation — Gemini won all three.
• Broadest feature coverage — ChatGPT covered the widest range of use cases but didn't top any single category outright.

A separate reviewer, working day-to-day rather than running a formal test, arrived at a compatible split from lived experience: Gemini for front-end and creative polish, an Opus-class Claude model as the default coding workhorse, and a GPT-based tool specifically for the moments when a bug won't die and the conversation is going in circles. A third reviewer, evaluating a newer GPT-5.5-era release, drew the line differently again — reaching for the newer model on high-volume backend execution work, but sticking with Opus when a task needed "blank canvas" visual taste.

The exact winners will keep shifting as vendors ship updates — these are one-off, reported results from specific runs, not permanent rankings. What's stable is the shape of the finding: differences are real and they cluster by category, not by vendor loyalty.

How to route in practice

1. Keep two or three tools open at once, not just installed — actually available in a tab or terminal so switching costs you seconds, not a subscription signup.
2. Before starting a task, name its category — writing, reasoning/planning, coding, debugging, visual/UI — and start with whichever tool reviewers currently report as strongest for that category.
3. Watch for the "stuck bug" case separately. Several reviewers independently reach for a different model specifically when they're in a debugging loop and the first model keeps proposing the same fix.
4. Re-check your routing periodically. These are reported results from specific test runs, not lab-verified rankings — revisit your defaults every few months rather than locking them in forever.

A rule of thumb, not a leaderboard

Treat this table as one data point, not gospel — it reflects specific reviewers' specific runs on specific dates. Build your own version of it from your own tasks; that's the whole method.

Power tricks

• Log which model won which task type for you, even informally. After a few weeks you'll have your own routing table, more reliable than anyone else's because it's built on your actual workload.
• Don't switch mid-task just because a model stumbles once. Reviewers who route by category still expect occasional misses — the routing is about where a model tends to be strong, not a guarantee on every single prompt.
• Treat "broadest coverage, no category wins" as its own useful signal — a generalist tool is exactly what you want for one-off or unfamiliar task types where you don't yet know which specialist to reach for.

Takeaway

Your team already has a best writer and a best debugger — nobody expects one person to be great at everything. Run your AI stack the same way: two or three tools, each task routed to whoever reviewers currently say is strongest at it, and your own results treated as the tie-breaker over anyone else's leaderboard.

Resources

• Don't Trust Plan Mode Blindly — Yeda AI Tips #053
• Test Models on Hard Tasks — Yeda AI Tips #054