Mistral Large 4… ENFIN ! (Test complet)

Mistral Large 4… ENFIN ! (Test complet)

Mistral Large 4… FINALLY! (Full Review)

Title history
  • Mistral Large 4… ENFIN ! (Test complet) — observed on 20261031-2314 UTC (YouTube)

by DevArt · View the channel on PSI

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 DevArt 👥 19K 📅 October 9, 2026 ⏱ 28 min 👁 23K 📄 expert opinion 🧭 2026-10-31
Available in: English (current) Français

Keywords

Mistral Large 4LLMtestbenchmarkAI

Summary

In this 28-minute video, DevArt presents a comprehensive hands-on test of Mistral Large 4, a new open-weight French AI model released in October 2026. The creator evaluates the model across five practical scenarios: building a LinkedIn banner SaaS, creating an iPhone emulator, developing a 2D game, generating motion design with Remotion, and a web project. He compares its performance against Chinese models like GLM and Kimi K2.7, noting significant gaps in complex tasks like game development but praising its strength in Remotion and React integration. The video also covers pricing, cost overruns (about €60 in API fees), and the model’s official benchmarks, which the creator argues are somewhat overstated. He expresses disappointment in the model’s coding abilities for intricate scenarios, but acknowledges a major improvement over previous Mistral models. Overall, the test reveals a capable but uneven model, with notable success in specific use cases.

145 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video offers significant value through its practical, hands-on approach, testing Mistral Large 4 in real-world scenarios that users might encounter. The creator provides detailed walkthroughs of each test, including prompts, outcomes, and costs, which adds a degree of transparency often lacking in AI model reviews. The argumentation is grounded in direct experience, with clear examples of strengths and weaknesses. While the evaluation is subjective, the creator offers balanced criticism, acknowledging where the model excels (e.g., Remotion) and where it falls short (e.g., game development). The cost analysis is particularly useful for budget-conscious developers, though the reliance on personal testing rather than standardized benchmarks limits the objectivity.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is moderate; the creator uses his own testing methodology rather than controlled, reproducible experiments. He cites the official Mistral benchmarks but critiques their credibility, noting that the source is the vendor itself. The description includes numerous links to related videos and an academy page, which serve as additional resources but not as direct sources for the claims made. The title ‘Mistral Large 4… AT LAST! (Full test)’ accurately represents the content: a thorough test of the model. The chapter structure and timestamped tests help viewers navigate, but the lack of external verification for performance claims is a limitation. No user comments were provided, so no public sentiment analysis could be performed.

236 words

Title / Content Match

The title accurately reflects the content: a comprehensive test of Mistral Large 4.

Quality & Reliability

7/10

The creator conducts multiple hands-on tests and provides comparative insights, but the evaluation is subjective and lacks independent verification. Scores reflect the detailed but personal nature of the testing.

Chapters

Cited Sources

External References

Contribution & Novelties

The video provides a timely, practical evaluation of Mistral Large 4, highlighting its real-world performance across diverse tasks. It offers unique cost estimates and compares it directly with competitive models, contributing to the community’s understanding of the model’s strengths and weaknesses. The emphasis on Remotion and React integration is particularly novel, as few reviewers focus on that aspect.

Pour aller plus loin :

  • Mistral AI — Official company background and model lineage.
  • Large language model — Overview of LLM architecture and capabilities.
  • Remotion — Official documentation for the motion design tool used in the test.
  • Terminal-Bench — Reference for the coding benchmark mentioned in the video.
  • Open-weight AI models — Discussion of open-weight distribution and implications.

116 words

Radar Profile

The radar shows strong performance in quantity and technical depth, with moderate quality and reliability. This implies a highly informative and technically rich review, but one that relies on subjective testing rather than rigorous scientific validation.

Reliability 6/10