Use cases
MiniMax M3.1 Flash Use Cases and How to Judge the Output
The four tasks MiniMax documents, the community builds, and the acceptance criteria that separate a demo from a deliverable.
The four workflows MiniMax documents
The GA page ships four worked examples. Each one lists the prompt, the inputs the task needs, and the expected deliverables. We reproduce the structure here so you can judge whether the output is real, because a prompt alone is not evidence.
Diagnose, fix and verify a slow app
Task. A support-ticket system opens slowly, search stutters, and paging makes it worse. The agent starts the project, uses browser performance tooling and flame charts to locate the cause, fixes it, preserves data and existing functions, verifies the change with real interactions, and delivers the project plus a short before-and-after report.
Why it is a good test. It demands diagnosis with a measurement tool, a change that preserves existing behaviour, and a verification step — three things models usually fake.
Acceptance checklist
- The fix is identified from a measured profile, not from reading the code and guessing.
- The before/after report contains real numbers from the same 8,000-record dataset.
- Existing functions still work after the change.
- The delivered project runs, not just the diff.
Three-statement model and DCF
Task. Build a financial model from a business brief, producing a three-statement model and a discounted cash flow.
Why it is a good test. Financial work is unforgiving: a projection with a hardcoded number in place of a formula looks right until someone moves an assumption.
Acceptance checklist
- Historical data, assumptions and formula-driven projections are visibly separated.
- Changing an assumption recalculates the model.
- The DCF links to the same statements rather than duplicating numbers.
- Sources for the input data are stated.
Visual difference analysis
Task. Compare visual references and explain the differences.
Why it is a good test. It exercises the model’s image input in a way where the answer is checkable by looking.
Acceptance checklist
- The described differences are visible in the images.
- No invented UI elements or features that are not in the reference.
- Positions, colours and states are described accurately enough to act on.
Interactive fluid simulation
Task. Build an interactive procedural simulation.
Why it is a good test. It combines code generation with visual reasoning — a reference photo or recording becomes a working Three.js scene with orbit, zoom and layered controls.
Acceptance checklist
- The scene runs at a usable frame rate.
- The documented controls (orbit, zoom, layers) actually work.
- The visual result resembles the reference.
What the community has built
Public repositories made with M3.1 Flash give a wider picture of the model’s output range:
| Project type | What it shows |
|---|---|
| Three.js 3D games and demos | Interactive 3D generation with working controls |
| Single-file HTML studies and artwork | Self-contained pages with no build step |
| Multi-agent stress tests | Whether several agents in parallel stay coherent |
| Video-input harness tests | Whether video actually reaches the model in coding agents |
| Prompt libraries with dozens of tasks | Repeated, reproducible patterns across 11 languages |
The prompt libraries are the most reusable artefact: they separate “task” from “prompt”, which is what makes a benchmark reproducible.
Acceptance checklists by task type
Use these to decide whether an output is deliverable:
| Task type | Minimum bar |
|---|---|
| Code change | It builds, the test suite passes, no unrelated behaviour changed |
| Codebase analysis | Every claim points at a specific file and line |
| Data analysis | The numbers reconcile; assumptions are stated and separate from data |
| Document research | Every factual claim has a source you can open |
| Visual/frontend work | It renders, it is interactive where promised, no placeholder content |
| Financial model | Formulas, not pasted values; assumptions editable and documented |
The recurring failure mode across cheap models is plausible-looking filler: a mock API, a hardcoded result, a screenshot-shaped placeholder. Checking for it takes two minutes and saves a day.
When to escalate to a stronger model
Use M3.1 Flash when the work is volume-shaped: many similar tasks, long context, high tool-call density, and a fast acceptable-error loop with tests. Escalate when:
- The task is a single high-stakes decision with no verification loop (legal, medical, financial advice).
- The reasoning depth required is close to the frontier and a wrong answer is expensive.
- The work needs the highest quality prose on the first try and nobody is going to review it.
The benchmarks page shows where this model sits on the capability curve, and the pricing page shows what the volume-shaped work costs.
What we could not verify
We reproduced the structure of the official workflows, not the outputs — we have not run all four tasks ourselves end to end. Treat the acceptance checklists as ours, and the workflows as MiniMax’s documented cases.
Next: API reference to run them, or OpenCode and agents to run them in a coding agent.
Sources and version notes
Checked October 21, 2026. We label official claims, third-party measurements, our own testing and community reports separately — see the sources policy for what each label means.