GPT-5.6 Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark

Key takeaways

GPT-5.6 Sol is the most effective and practical model among the GPT-5.6 variants for a variety of real-world tasks.
The 'Claire Weighted Index' benchmark indicates that GPT-5.6 Sol significantly outperforms competitors like Claude Fable and Sonnet 5 in prototype development, PRD writing, debugging, and agentic communication.
Sol is notably better at collaborative, design-oriented, and productivity-enhancing tasks due to its clarity, creativity, and flexibility.
While Fable excels in precise, technical domains such as cybersecurity, its pedantry and rigidity make it less suited for collaborative product development and conceptual tasks.
Key use cases for GPT-5.6 Sol include full app prototyping, generating complex web content, automating browser tasks, and creating engaging educational and social media content.
Practical effectiveness—getting work done efficiently—is prioritized over theoretical or pedantic precision in choosing the right AI model.

Summary

Introduction to GPT-5.6 and Model Overview

The episode begins with the host expressing their excitement about the return of GPT-5.6 Sol, their favorite model, after a period of limited access. The host introduces three new versions of GPT-5.6: Sol (frontier model), Terra (balanced for efficiency), and Luna (mini/nano for high-volume, affordable tasks). The discussion highlights the nuances between these models, emphasizing why Sol is particularly favored for creative and complex tasks.

Benchmarking Methodology and Results

The host discusses their unique 'How I AI Vibreview Benchmark,' which evaluates models based on tasks like PRDs, prototyping, wireframing, debugging, and agentic voice. They detail their approach of blending human judgment with automated evaluation, and reveal that GPT-5.6 Sol significantly outperformed other models, including Claude Fable and Sonnet 5, across these metrics. The episode explains how this benchmark provides a scientific yet personalized assessment of AI capabilities.

Performance on Practical Applications and Use Cases

The episode dives deep into specific use cases where GPT-5.6 Sol excels. These include generating detailed prototypes, building full apps—like a gamified homework tracker for children—and automating browser tasks such as replying to LinkedIn messages. The host shares firsthand experiences illustrating how Sol’s clarity, creativity, and efficiency surpass other models like Fable, especially due to its practicality and better collaboration potential. The discussion also covers the importance of design, communication, and real-world productivity boosts achieved with Sol.

Model Limitations and Comparative Insights**

A comparison between GPT-5.6 Sol and Claude Fable highlights the strengths of Sol in communication, prototyping, and flexibility. Fable’s precision and pedantic nature, while excellent for cybersecurity research, hinder its collaboration and adaptability in product development. The host emphasizes that practical effectiveness—getting things done—is more valuable in product scenarios than theoretical sophistication, favoring Sol’s approach.

Additional Use Cases and Final Reflections

Examples include video editing for social media clips, automating web interactions via Chrome, and building engaging educational tools. These scenarios showcase Sol’s versatility and polish. The host concludes by reaffirming their preference for Sol, especially in tasks requiring efficiency, creativity, and adaptability, and invites feedback from listeners about these insights.

Show notes

GPT-5.6 Sol is back, and I ran it through my full How I AI vibe benchmark against GPT-5.6 Terra, Luna, Claude Fable 5, and Sonnet 5 across five categories: PRDs, prototypes, wireframes, debugging, and agentic voice. Sol won by a meaningful margin on my Claire Weighted Index (70% my taste, 30% Terminal Bench 2.1), and I also tested two use cases I can't stop thinking about: building a gamified homework tracking app for my kids in one shot with Codex, and browser automation with Chrome that burned through 500 LinkedIn replies while I did literally nothing.

What you’ll learn:

How I scored five AI models (including GPT 5.6 Sol, Fable 5, and Sonnet 5) using my “Claire Weighted Index” benchmark across PRDs, prototypes, code, and agentic voice
The difference between GPT-5.6 Sol (Terra) and Sol for PRD writing
How Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuck
Why Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmark
How I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shot
The video editing use case that saved me hours clipping a talk I gave at Cursor’s event
How to use Codex plus GPT-5.6 and Chrome for browser automation, and why this is my single most-loved use case right now

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In this episode, I cover:

(00:00) Intro

(01:10) The three GPT-5.6 models: Sol, Terra, Luna

(02:17) Pricing: Sol vs. Fable API costs

(03:24) The How I AI benchmark

(05:03) Claire-weighted Index results

(07:00) Per-task winners: prototypes, PRDs, agentic voice

(11:59) What Claire actually rewards

(13:20) Full-fidelity prototype side-by-sides (Sol vs. Fable)

(17:45) Wireframes

(18:19) Agentic voice