Can Mistral AI read a cleanly scanned document?
Mistral AI accurately recovered text from a clean scanned document. It placed the results table’s first data row, the sampling point, in the Markdown header without losing text.
1 of 1 test case passed
Every test case in this scenario has a result.
The test case
Each test case is judged on its own: Pass, Fail, Not gradable, or Untested. The scenario result above is based on this test case.
Clean scan — text recoveredPassEvidence
# Kirkhollow Analytical Laboratories Ltd Braithe Road, Vardenne BR7 2HD 01632 960118 · reception@kirkhollow-labs.example Ms R. Sandiman Braithe Estuary Partnership Estuary House, Vardenne Quay Braithe BR3 4LN
Markdown output: the certificate’s text and results table
Copied from Output file (Markdown), lines 1–13
Open the Markdown file ↗Thank you for the four water samples delivered to this laboratory on 28 January 2026. They were booked in as batch KAL-11938 and analysed by the methods shown. The results below relate only to the samples as received. | Sampling point | Vardenne Channel, station VQ1 | | --- | --- | | Date and time of sampling | 27 January 2026, 09:40 | | Total suspended solids | 41.6 mg/l (method KAL-M04) | | Chloride | 18,200 mg/l (method KAL-M11) | | Ammoniacal nitrogen | 0.42 mg/l (method KAL-M12) | | Dissolved organic carbon | 5.9 mg/l (method KAL-M22) | The suspended solids figure is higher than those reported for this station in December and January, which were both below 30 mg/l. The samples were taken on the ebb, two days after the last placement at Kellow Sands, and the result is consistent with that. The remaining samples will be held until 4 March 2026 and destroyed after that date unless you ask us to keep them. Yours sincerely  Dr A. J. Prosser
Copied from Output file (Markdown), lines 23–41
| ✓ | Expected: Accurately recover the visible text from the scan.Found: It recovered the printed text accurately. The signature is an image reference, and the first data row, the sampling point, is in the table header without text loss. |
The supplied scan is the same document sent to the API, so the source and output can be compared. The text was recovered accurately, despite a table formatting defect.
Table parsing effect. The sampling point occupies the table’s header row, so a Markdown table parser would read one fewer data row.
Configuration and setup
How this tool was set up for the run and what the test needed in place. Each row is a fact from the run's records; a fact the records do not hold is left out, not guessed.
How this scenario is graded
How we decide Pass, Fail and Not gradable. The same rules apply to every tool tested on this scenario.
Each test case gets one result per tool: Pass, Fail or Not gradable. A test case we haven't run yet shows Untested. There are no partial results.
- Pass: the tool did everything the test expected, and nothing it said contradicts the correct answer.
- Fail: at least one expected behaviour clearly didn't happen; the test case's result says which one and quotes the tool.
- Not gradable: our evidence couldn't settle the outcome (for example a record we needed is missing). It is never counted as a fail, and the test case's result says what's missing.
Where this sits in the benchmark
This page is one cell of a larger study: one tool, one scenario. Only this benchmark's frame appears here.
| Level | Name | Scope |
|---|---|---|
| Benchmark | Converting a complex PDF into clean Markdown with a hosted API → | 12 scenarios · 13 tools |
| Capability | Scanned Document OCR → | |
| Scenario | A cleanly scanned document → | |
| Tool | Mistral AI → |
Global scenario definition → · Global capability definition → · Mistral AI product page →
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