I Tested GPT 5.5 vs Opus 4.7: What You Need to Know

I Tested GPT 5.5 vs Opus 4.7: What You Need to Know

by Nate Herk

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 Nate Herk 👥 964K 📅 April 23, 2026 ⏱ 19 min 👁 163K 📄 expert opinion 🧭 2026-08-28
Available in: English (current) Français

Keywords

GPT-5.5Opus-4.7benchmarkcodingcost

Summary

This video presents a hands-on comparison between OpenAI’s GPT-5.5 and Anthropic’s Opus 4.7, focusing on real-world coding tasks. The creator, Nate Herk, runs four experiments: building a personal brand website, a solar system simulation, a space shooter game, and an ecosystem simulation. He uses Codex for GPT-5.5 and Claude Code for Opus 4.7, noting that this also compares the harnesses. The results show GPT-5.5 is faster and more token-efficient, but Opus 4.7 sometimes produces better design quality. Cost-wise, GPT-5.5 is slightly cheaper overall, despite higher input token usage in some cases. The video also discusses benchmarks, pricing, and the strategic positioning of both models. The creator emphasizes the importance of testing models for specific use cases rather than relying solely on benchmarks. He concludes that GPT-5.5 is a strong contender, but Opus 4.7 still leads in certain real-world scenarios.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides valuable insights through practical experiments, offering concrete data on speed, token usage, and cost. The argumentation is solid, as the creator transparently explains his methodology (one-shot prompts, same harness) and acknowledges limitations. He also contextualizes the results with benchmark data and pricing, giving a balanced view. The subjective design assessments are clearly labeled, and the overall reasoning is coherent.

Scientific Rigor, Source Quality, Title Accuracy

The video references OpenAI’s release blog and benchmarks, but does not provide direct links in the description. The creator’s own experiments are the primary source, which adds practical value but lacks external validation. The title accurately reflects the content, and the video does not overhype the results, maintaining a critical tone. The description includes links to the creator’s courses and tools, but these are not scientific sources.

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Title / Content Match

The title accurately reflects the content: a direct comparison of GPT 5.5 and Opus 4.7 with practical tests and insights.

Quality & Reliability

7/10

The video provides a hands-on comparison with real-world experiments, but relies on subjective evaluation and lacks peer-reviewed sources. The methodology is transparent (one-shot prompts, same harness), yet the sample size is small and the author's expertise is not formally established.

Chapters

Cited Sources

  • OpenAI GPT-5.5 Release Blog — Referenced for release details, benchmarks, and pricing.
  • Anthropic Claude Opus 4.7 — Mentioned as the competing model.

Concurring Sources

  • OpenAI GPT-5.5 Release Blog — Supports the benchmarks and pricing mentioned in the video.

Dissenting Sources

External References

Contribution & Novelties

The video contributes a practical, hands-on comparison of two frontier AI models, focusing on real-world coding tasks rather than just benchmarks. It provides detailed statistics on speed, token usage, and cost, which are often missing from official announcements. The creator’s approach of using one-shot prompts and measuring actual performance is a valuable addition to the discourse.

Pour aller plus loin :

  • SWE-bench — A benchmark for evaluating AI models on real GitHub issues, relevant to the coding comparisons.
  • Agentic AI — Concept of autonomous agents, central to the video’s discussion of agentic coding.
  • Token efficiency — Understanding token usage is key to the cost analysis presented.

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Radar Profile

The radar profile shows a balanced performance across all dimensions, with slightly higher scores in information quantity and quality, reflecting the video's practical depth. The technical level is moderate, making it accessible to a broad audience, while reliability is adequate but not exceptional due to the lack of external validation.

Reliability 6/10

💬 Très positif. Sur les 30 commentaires analysés, la majorité exprime une forte appréciation pour la comparaison détaillée et les statistiques réelles, avec quelques demandes de tests supplémentaires et des retours sur la qualité du codage.