Current Limitations and Best Practices

Update November 2, 2026 — Le Chat is now Vibe. As of May 28, 2026, Le Chat has been rebranded as Vibe (still at chat.mistral.ai): a single agent for work and code, carrying over your conversations, settings, and subscriptions. The legacy Le Chat experience remains accessible in Chat mode; in Work mode, agents have become skills (invoked with @), memories form the knowledge base, and deep search is now the Deep Research skill. Plans are now called Mistral Free, Pro, Team, and Enterprise. This lesson uses the name Vibe; note that some menus described here may have changed names or locations.

Know the limits to use the tool better

Mistral AI’s multimodal capabilities are impressive, but they aren’t infallible. A savvy user isn’t someone who blindly trusts the tool, nor someone who distrusts it on principle. It’s someone who knows exactly where the tool excels and where it falters. This final lesson reviews the limitations of each modality and the cross-cutting best practices that make a difference in daily work.

Vision limitations

Vision works best on sharp, well-lit, high-resolution images: charts with legible labels and high-contrast colors, clearly visible printed text, and photos of common objects and familiar scenes. Conversely, a blurry or heavily compressed image loses details the model cannot invent—it will never reconstruct an illegible number; at best it will guess, which is worse. Very small text in a corner of an image is often misread or ignored. Dark, overexposed, or highly abstract images yield less reliable results.

Two pitfalls deserve special attention. The first is visual hallucination: the model may occasionally describe elements it thinks it sees but that aren’t there. It’s rare, but possible, especially with ambiguous images—if an answer surprises you, check the image yourself before reusing it. The second is precise counting: accurately counting the number of objects in an image remains a challenge for vision models; don’t base an inventory on such a count. In short, reserve vision for quick analyses, descriptions, visual comparisons, and basic OCR on isolated images; for complex multi-page documents, prefer Document AI.

Audio limitations

On the transcription side, things go smoothly with good-quality recordings featuring a primary speaker, in any of the 13 officially supported languages, even for recordings several hours long; diarization works correctly as long as speakers don’t talk over each other. Difficulties start with intense background noise—loud music, construction sites, crowds—which drops accuracy. Overlapping speech, when multiple people talk simultaneously, degrades both transcription and diarization: that’s the reality of lively meetings, and one more reason to enforce turn-taking. Very strong accents and non-standard dialects may be mistranscribed. Proper nouns and obscure acronyms are often misspelled—always proofread names of people and companies in a transcript before sharing. Finally, outside the 13 main languages, quality drops significantly.

Text-to-speech has its own limits. A cloned voice isn’t a real voice: cloning is impressive, but subtle differences remain, especially on long sentences. The model handles basic tones well—calm, energetic—but can struggle with very fine emotional nuances. And rare technical terms or uncommon foreign names may be mispronounced. In practice: use transcription for meetings, classes, and interviews in a reasonably quiet environment; use voice mode for quick interactions and brainstorming; use text-to-speech for content meant to be listened to, narrations, and accessibility.

Document AI limitations

Document AI shines on high-quality native PDFs, tables with clear borders and distinct headers, structured documents—reports, contracts, articles—and extraction of well-identified numerical data. It struggles, however, with very poor-quality scans: a document scanned at 72 DPI with stains will be hard to process, hence the recommendation to aim for at least 150 DPI. Illegible handwriting remains out of reach—if a human has trouble deciphering it, so does the model. Very complex layouts, with nested multiple columns, overlapping text boxes, or busy colored backgrounds, can disrupt extraction. Forms with checkboxes remain a challenge: scanned checkboxes and radio buttons are still poorly handled, even though progress is constant. Finally, a 200-page PDF can be processed, but the quality of answers to specific questions decreases as the document grows longer—split it up. So reserve Document AI for multi-page PDFs, scanned documents, table extraction, and document Q&A; for an isolated image with text, vision is enough.

Cross-cutting best practices

First rule, non-negotiable: verify critical data. Whatever your level of trust in the model, systematically check financial amounts, contractual dates, proper nouns, and figures extracted from charts. AI is a productivity tool, not a substitute for your judgment—the person who signs remains responsible.

Second rule: provide context. “This is an invoice from an IT hardware supplier” is better than uploading the file without a word, and “This recording is a management meeting with 4 participants” directly helps diarization. Third rule: iterate. If the first result disappoints, rephrase the question, provide a higher-quality image, split the long document into sections, or ask the model to focus on a specific aspect.

Fourth rule: choose the right modality for each situation.

Fifth and final rule: respect confidentiality. Any file uploaded to Vibe is sent to Mistral’s servers for processing. Avoid uploading documents containing sensitive personal data, trade secrets, or classified information, and for sensitive professional use cases, inquire about the private deployment options offered by Mistral.

What’s improving

The multimodal field is evolving rapidly, and several improvements are expected in the coming months: better accuracy on handwriting and complex forms, support for additional languages for audio, video processing capabilities, deeper integration between modalities in Vibe, and even faster processing times. The limitations described here are therefore a snapshot, not a fate—which is why it pays to periodically retest what didn’t work before.

Key takeaways

• Each modality has its strengths and limits—choose the right one for each task
• Systematically verify critical data extracted by AI
• Provide context, iterate, and refine to get the best results
• Input quality (sharp image, clean audio, native PDF) determines output quality
• Multimodal capabilities are advancing quickly—stay up to date with new features

Test your knowledge

Vision, audio, documents: validate your multimodal reflexes.

Multimodal doesn’t replace rigor: good sources, good prompts, verification—and the course workflows do the rest.