This video provides a critical analysis of OpenAI’s recent GPT-5 launch, arguing that while it’s an interesting release, it’s ultimately underwhelming and lacks the revolutionary impact of GPT-4. The speaker critiques the staged, cringy presentation and points out factual errors in the provided benchmark slides, suggesting a lack of polish.
A System, Not Just a Model
The core argument is that GPT-5 isn’t a single, monolithic model but rather a complex system. OpenAI appears to be using a router that directs prompts to different models based on their complexity. Simpler queries are handled by faster, cheaper models, while more difficult tasks are sent to a deeper reasoning model. This strategy is primarily a cost-saving measure for OpenAI, given its massive user base. The system also features agentic capabilities, such as the model testing its own code, which enhances its performance in tasks like coding and mathematics.
Performance, Benchmarks, and Key Features
The speaker expresses skepticism about the performance claims. While OpenAI touts state-of-the-art results, the analysis points out that some benchmarks are manipulated (e.g., excluding difficult instances from the SWE-bench test) and that on others, like the ARC challenge, GPT-5 trails competitors. The release heavily promotes creative writing and health-related advice, which the speaker commends as a positive step. A significant improvement is the large output token limit (128k), allowing for extensive content generation and editing tasks.
Pricing and Model Tiers
A major focus of the release is a significant reduction in cost and an increase in speed. The speaker speculates this is achieved through the router system and potentially by using lower-precision computing (e.g., FP4). The new pricing structure is very competitive and introduces several tiers:
- GPT-5 (Main System): The most capable system, priced aggressively compared to competitors like Claude Opus.
- GPT-5 Mini: A mid-tier model that is cheaper than Gemini 2.5 Flash.
- GPT-5 Nano: An extremely cheap model for high-volume, simpler tasks.
However, none of the currently announced models support audio or real-time API access.
Conclusion: Underwhelming but Practical
Overall, the release is seen as an incremental, practical evolution rather than a groundbreaking leap. The wow factor of the GPT-4 launch is absent. The primary benefits are speed and cost-efficiency, which will be a huge plus for developers and high-volume users, especially in coding applications. However, the speaker questions whether users will be frustrated by the router system and whether GPT-5 can truly outperform top-tier specialized models like Claude Opus in demanding fields like software development.
Mentoring question
The new GPT-5 uses a system of models to balance cost, speed, and capability. When evaluating tools or strategies for your own projects, how do you weigh these factors against aiming for the absolute highest performance on every single task?
Source: https://youtube.com/watch?v=GkjfWkMpkTA&si=Ji4EUSUevkPRELec
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