AI for Coding That Runs the Code, Not Just Writes It

Pick the model per task, not per subscription

Different labs are genuinely better at different parts of the job, and the difference is large enough to matter on a normal working day.

• Architecture and refactoring across many files — the long-context models handle whole modules in one pass.
• Tricky algorithmic logic and edge cases — the flagship reasoning models earn their credit cost here.
• High-volume boilerplate, tests and docstrings — cheaper models do this fine and cost a fraction.
• Reviewing code you did not write — a second lab catches what the first one rationalised.
• Anything touching a library that changed recently — turn on web search so the model reads current docs instead of recalling old ones.

A debugging loop that converges

• Paste the failing code and the actual traceback — not a paraphrase of it.
• Ask for the diagnosis before the fix. A model that explains the cause is a model that understood it.
• Run the proposed fix in the code interpreter in the same thread and read the real output.
• If two attempts fail, switch models rather than re-prompting. A second lab is not carrying the first one's wrong assumption.
• Once it passes, ask for the regression test that would have caught it.

Working across a whole codebase

Upload source files, logs, schemas and config directly. The model reads them as context rather than as a wall of pasted text, which means you can ask questions that span files: where is this state actually mutated, which callers break if this signature changes, what does this migration do to existing rows.

For larger jobs, hand it to an agent. "Audit this module for unhandled error paths and write a report" is a multi-step task — the agent plans it, works through the files, and comes back with structured findings rather than a single guess.

Things that reliably go wrong

• Trusting an unrun snippet. If it did not execute, it is a hypothesis.
• Asking for the whole feature at once. Scope to one function or one file and iterate.
• Not saying which language version, framework version and runtime you are on.
• Letting a thread run for 200 messages — old wrong turns keep influencing the answer. Start fresh and paste the current state.
• Pasting secrets, keys or customer data into a prompt. Redact before you send, on any platform.

What you get on Clade

• Code interpreter that writes, executes and debugs across major languages
• 50+ models with mid-thread switching, so a second opinion costs one click
• File and repo-file upload for real codebase context
• Cited web search for current library documentation and changelogs
• Artifacts — share a working script or a written-up fix as a link
• Bookmarks and full searchable history so a solved problem stays solved

Models you can use for this

MiMo-V2-Flash256KDeepSeek-V3.2 (Reasoning)128KDeepSeek-V3.2128KQwen3-Max256KQwen3.5-Plus1MCommand A Reasoning256KCommand A256KMiniMax M2197K

Frequently asked questions

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