Which AI model should you use for which coding task?

Every few weeks a new model tops a coding benchmark, and every few weeks a thousand developers switch their default. Then they go back, because the new one was worse at the thing they actually do all day.

The question "what's the best AI model for coding?" has no answer, and chasing one costs you real time. The useful question is narrower: which model should run this task?

The benchmark gap is smaller than the brief gap

Here's the uncomfortable part. Among the current flagship models, the difference on your everyday work is usually smaller than the difference between a well-scoped task and a vague one.

Hand any of them "make the checkout flow better" and you'll get three flavours of confidently wrong. Hand them a written brief with acceptance criteria and a test to satisfy, and they mostly converge on something reasonable.

So before you re-litigate your model choice, check whether the thing you're actually short on is a specification. That's the lever with the bigger arm.

That said, once your briefs are decent, model routing is worth real money and real minutes. Here's how to think about it.

Route by task shape, not by leaderboard

Sort your work into three buckets. Not by importance, but by how much judgement the task needs versus how much typing.

Mechanical work. Renaming a symbol across ninety files. Converting a test suite to a new syntax. Adding null checks the compiler already told you about. Bumping a dependency and fixing the obvious breakages. There's no ambiguity here, only volume. A small, fast model does this well and finishes sooner, and the speed genuinely matters, because you're watching it.

Judgement work. Designing a schema change that has to survive three future features. Tracking a race condition through async code. Deciding where an abstraction should live. This is where the frontier model earns its price. The failure mode of a cheap model here isn't slowness, it's a confident wrong turn you don't notice for two hours.

Long-horizon work. A migration across hundreds of files, or a refactor that has to hold one plan in mind for forty tool calls. What matters here isn't raw reasoning so much as staying coherent: not forgetting the constraint from step three by step thirty. If a model drifts on your codebase, it will drift worse on a long job, so test that before you trust it with one.

Most people's mistake isn't picking the wrong flagship. It's running everything on the flagship, then wondering where the budget went: one of the biggest leaks in agent spend.

Two models on one problem beats one model twice

When you're genuinely stuck, switching model is more useful than re-prompting the same one. A second model brings a different prior, not just a different roll of the dice: it will read your stack trace and reach for somewhere else to look.

Two patterns that pay off:

• Second-opinion review. One model writes, a different one reviews. Author bias is real for models too; a fresh reader catches what the writer talked itself into, and it costs a fraction of what writing the code did.
• Race the hard ones. For a genuinely uncertain task, run two agents on the same brief in separate branches and keep the better answer. Wasteful on easy work, cheap insurance on the design decision you'll live with for a year.

The costs nobody mentions

Being honest about the downsides, because switching isn't free.

You lose the session. A model swap mid-task means the new one starts cold: it has none of the context the last one built up. Switch at task boundaries, not when an agent is halfway through and confused.

You lose muscle memory. Each agent CLI has its own flags, rules-file conventions and permission prompts. Running three means learning three, and keeping your project instructions readable to all of them.

And you lose consistency. Models have house styles. Rotating them across one codebase without a shared rules file gives you three dialects in the same repo, which is exactly what an AGENTS.md is for.

None of this argues for one model forever. It argues for switching deliberately, at boundaries, with your conventions written down.

Make switching cost nothing

The reason most developers don't route work by task is friction. Trying a different agent means another terminal, another window, another mental context, so the default wins by inertia. The same inertia operates one level up, between the agent tools themselves: Claude Code vs Codex vs Gemini CLI is a routing decision too, not a marriage.

That's the problem SanuDesk was built for. It's a desktop app where your Claude Code, Codex and Gemini sessions tile into one grid, side by side, each on its own task. Comparing two models on the same brief is watching two panes instead of opening two apps.

The Kanban board makes the routing explicit: cards carry their own brief, and you deploy each one to whichever agent suits it: the mechanical batch to the fast one, the schema design to your best. Every session starts fresh from the card, so switching models never means re-explaining the task. Loops handle the recurring jobs on a schedule, journaled run to run.

And because it's bring-your-own-model, you're driving accounts you already pay for. Your provider bills you directly: there's no second meter, and no incentive for us to steer you toward a particular model. The free tier doesn't need an account of ours at all.

Try it on your next three tasks

Take tomorrow's task list and label each item mechanical, judgement or long-horizon. Send the mechanical ones somewhere cheap and fast. Keep the judgement calls on your best model. Notice how much of your week was in the first bucket all along.

Download SanuDesk free and point a workspace at your repo, or see how the grid, the board and Loops fit together on the features page.