Choosing an AI model is not only a benchmark decision. Teams also need to inspect the prompt, the task examples, latency or cost evidence, safety constraints, and the behaviour that reaches users. Strawberry can organise the Mistral AI console or documentation open in your browser with the evaluation material and product context you provide.
01
Compare model behaviour against the job, not a slogan.
A general model ranking cannot answer whether a model handles your extraction, coding, support, or multilingual task. It can prepare a decision matrix from the representative cases and criteria you select.
02
Turn prompt changes into testable evidence.
A prompt edit may improve a demo while harming edge cases.
Strawberry can turn the expected outputs and failure modes you provide into a review set that makes the trade-off visible before a team updates its application.
03
Investigate an output without losing the inputs that produced it.
When an output looks wrong, the useful trail includes the task, prompt version, source context, model setting, and observed result. It can assemble that incident packet from the tabs and logs you make available.
04
Re-run evaluations when the model or task changes.
Run an evaluation after a model switch, prompt revision, or material task change, rather than producing a ritual report with no decision behind it. Save the test definition as a skill and schedule the routine for those release moments.
Mistral AI Strawberry