J'ai testé Opus 4.6 et Codex 5.3... Le résultat m'a CHOQUÉ

J'ai testé Opus 4.6 et Codex 5.3... Le résultat m'a CHOQUÉ

I tested Opus 4.6 and Codex 5.3... The result SHOCKED me

Title history
  • J'ai testé Opus 4.6 et Codex 5.3... Le résultat m'a CHOQUÉ — observed on 20261101-0144 UTC (YouTube)

by Melvynx · View the channel on PSI

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 Melvynx 👥 67K 📅 February 6, 2026 ⏱ 39 min 👁 15K 📄 expert opinion 🧭 2026-11-01
Available in: English (current) Français

Keywords

AI codingOpus 4.6GPT-5.3 Codexbenchmarksdeveloper tools

Summary

In this video, developer Melvynx tests and compares the new AI models Claude Opus 4.6 and GPT-5.3 Codex for coding tasks. He begins by discussing recent AI news and benchmarks, highlighting Opus 4.6’s long context retrieval and 1M token support, as well as GPT Codex’s performance on Terminal Bench. He then performs a series of practical tests: cloning a YouTube thumbnail generator app, solving a physics simulation with bouncing balls in a heptagon, building a 3D SpongeBob scene, and implementing an audio chat feature in his own application. Throughout, he evaluates the quality of generated code, UI design, speed, and reliability. He finds Opus 4.6 generally superior in code quality, adherence to prompts, and visual output, but notes that Codex is faster in some tasks and handles auto-compaction better. He openly acknowledges his bias towards Claude and admits that the tests are subjective and not scientifically rigorous. The video ends with a final verdict favoring Opus 4.6, while encouraging viewers to follow his social media for more content.

168 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides a rich, hands-on evaluation of two cutting-edge AI models, which is valuable for developers considering these tools. The argumentation is largely based on personal experience and subjective observation, with little statistical rigor. The author clearly states his preferences and biases, which adds honesty but weakens objectivity. He offers detailed descriptions of each test and shows real code outputs, giving concrete evidence for his claims. However, he does not control for all variables (e.g., model settings, costs) and occasionally makes sweeping statements without systematic comparison. Overall, the value lies in the practical, relatable scenarios, but the argumentation is more anecdotal than scientific.

Scientific Rigor, Source Quality, Title Accuracy

The video cites official benchmarks from Anthropic and OpenAI, as well as community reactions, but does not provide direct links to these sources in the description; the description contains only the creator’s own links (courses, socials, etc.). The scientific rigor is moderate: tests are not fully reproducible, and the author relies on qualitative judgments. The title is sensationalized but not entirely misleading; it reflects the author’s strong reaction to the results, though the outcome is nuanced. The content aligns well with the title, offering a comparative test, but the ‘shock’ factor is exaggerated.

212 words

Title / Content Match

The title is clickbait but generally reflects the author's strong positive impression of Opus 4.6; the content is a genuine comparative test, though the 'shock' is subjective.

Quality & Reliability

6/10

The video is an honest hands-on comparison of two AI coding tools, but it is highly subjective and relies on personal testing rather than standardized methodology. The author admits his bias towards Opus and provides no quantitative cost or performance measurements beyond anecdotal timing. The benchmarks discussed are from external sources (Anthropic/OpenAI), but their reliability is not independently verified.

Chapters

Cited Sources

  • Formation IA — Lien de la description vers la formation de l'auteur
  • Blog — Lien de la description vers le blog de l'auteur
  • Discord — Lien de la description vers le serveur Discord de l'auteur
  • GitHub — Lien de la description vers le GitHub de l'auteur
  • Cours JavaScript — Lien de la description vers le cours JavaScript gratuit
  • Cours React — Lien de la description vers le cours React gratuit
  • Cours NextJS — Lien de la description vers le cours NextJS gratuit
  • Masterclass SaaS — Lien de la description vers la masterclass SaaS
  • One-to-one — Lien de la description vers la discussion en direct
  • Twitter — Lien de la description vers le Twitter de l'auteur

Contribution & Novelties

This video contributes a practical, developer-centric comparison of two state-of-the-art AI coding assistants, filling a gap between official benchmarks and real-world usage. It offers a wealth of qualitative observations about code quality, UI generation, and developer experience, which are often overlooked in academic evaluations. The author shares his prompts and workflow, adding transparency and reproducibility. However, the lack of controlled settings and cost analysis limits its scientific value. Pour aller plus loin :

  • Large language model — Provides background on the underlying technology.
  • Software engineering — Context for the coding tasks discussed.
  • SWE-bench — A standard benchmark for evaluating coding abilities of LLMs (note: Wikipedia page might not exist; if uncertain, remove URL).
  • Terminal-bench — A newer benchmark for terminal-based AI agents, mentioned but not linked.
  • Long context retrieval — A concept central to the video’s discussion of Opus 4.6’s capabilities, but no specific reference is provided.

147 words

Radar Profile

The radar profile shows high scores in information quantity and technical level, but lower in reliability and quality due to the subjective nature and lack of rigorous methodology. This suggests the video is informative and technically rich but may not be fully trustworthy for objective conclusions.

Reliability 6/10

💬 Sur les 30 commentaires analysés, le sentiment général est positif et équilibré, avec des éloges pour le travail de test, des demandes de comparaisons de coûts et de paramètres de modèles, et quelques remarques sur un biais apparent en faveur de Claude.