Hi everyone! This time, instead of using a single-shot prompt, I compared the models using a Planning → Development workflow with OpenCode + High Effort. The judge was GPT-6 Astra Pro. 🔥🦾
Models:
- DeepSeek v4.1-1017
- Fable 5.2
- Gemini 4 Carbon
- GPT 6.1-Sol
Winner in terms of quality/cost balance:
- GPT 6.1-Sol — 91/100
For the independent evaluation, I focused on physics accuracy, cargo and flooding behavior, measurement reliability, code structure, and Canvas rendering quality.
Raw Technical Performance and Costs:
- Fable 5.2 — 93/100 — $25.80
- GPT 6.1-Sol — 91/100 — $1.68
- DeepSeek v4.1-1017 — 80/100 — $0.692
- Gemini 4 Carbon — 76/100 — $1.26
(Performance + Quality) / Price Scores:
- DeepSeek v4.1 Flash 1017: 100/100
- GPT Sol 6.1: 52.2/100
- Gemini 4 Carbon: 46.9/100
- Fable 5.2: 3.1/100
Comparisons:
- Most comprehensive simulation and flooding model: Fable 5.2
- Best cargo/collision engineering: GPT 6.1-Sol
- Best cinematic ship presentation: Fable 5.2
- Best ocean and storm atmosphere: DeepSeek
- Best quality/cost balance: GPT 6.1-Sol
Fable's 2-point advantage in this evaluation comes at approximately 15.4× the cost of GPT 6.1-Sol.
I wanted to test Fable 5.2's quality with one-shot prompts without human feedback.
Everything in this game is from scratch: models, sounds, textures. ~$550 at API pricing, ~2.7M output and 445M input tokens, heavily cached. Took 8 hours!
Play it here: https://t.co/fazOCcyNIH