GPT-5.6 vs Grok 4.5 vs Claude: 12 AI models build 4 apps

Key Takeaways

• GPT-5.6 Sol achieved perfect 5/5 success on the raycaster task at $1.35 for five runs
• Claude Fable 5 dominated the Rubik's Cube challenge with 5/5 clean solves but at $2.03 per five attempts
• Open-weights models like Qwen 3.7 Plus cost under $0.15 but struggled with consistency

TryAI ran 12 AI models through the same gauntlet: build four apps, five attempts each, 240 total runs. The lineup included OpenAI's new GPT-5.6 in three tiers (Sol, Terra, Luna), xAI's Grok 4.5, Anthropic's Claude Opus 4.8 and Claude Fable 5, Meta's surprise drop Muse Spark 1.1, and four open-weights contenders. The results show clear winners for different budgets and use cases.

This test matters because vibe-based model comparisons tell founders nothing useful. Price per run, consistency across attempts, and actual output quality determine whether an AI coding assistant saves time or creates debugging headaches. TryAI published every single attempt, letting anyone verify the results.

What models were tested and how?

The proprietary models included GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna, GPT-5.5, Grok 4.5, Claude Opus 4.8, Claude Fable 5, and Muse Spark 1.1. The open-weights models, all served via Fireworks, were Qwen 3.7 Plus, DeepSeek V4 Pro, Kimi K2.6, and GLM-5.2.

Each model attempted the same four tasks five times. TryAI tracked cost per five runs, average completion time, and success rate. The methodology shift came directly from Hacker News feedback on their previous build-off: one attempt per model was too noisy. Five attempts reveal consistency, which matters more than a single lucky run.

How did GPT-5.6 perform against Grok 4.5 and Claude?

On Task 1, a Doom-style raycaster maze with WASD movement, shaded walls, and collision detection, GPT-5.6 Sol hit 5/5 playable attempts at $1.35 for all five runs. Grok 4.5 matched that perfect score at $0.27. Claude Opus 4.8 managed 4/5 at $0.54 but the outputs felt dry according to testers.

GPT-5.6 Luna, the cheapest tier at $0.15 for five runs, also hit 5/5 playable but with less detail than GPT-5.5. Muse Spark 1.1 surprised everyone: three of five attempts broke completely, but the two working runs matched Claude Fable 5 and GPT-5.6 Sol quality.

The open-weights models struggled. GLM-5.2 rendered good visual detail but movement never worked in any attempt. Qwen 3.7 Plus and Kimi K2.6 each managed only 2/5. DeepSeek V4 Pro hit 3/5 but took over five minutes per attempt on average.

Which model built the best 3D Rubik's Cube?

Task 2 required a colorful 3D Rubik's Cube with Scramble and Solve buttons that animate rotations smoothly. Claude Fable 5 dominated with 5/5 clean solves and no visual glitches. The cost: $2.03 for five attempts. GPT-5.6 Sol and GPT-5.6 Terra both hit 4/5, with Sol's failures including one all-black cube and one with broken animations.

Claude Opus 4.8 flopped here. Zero perfect results, with colors changing unexpectedly during solves. Grok 4.5 managed 3/5 at $0.65. GPT-5.6 Luna couldn't handle this task at all: 0/5, with scrambling immediately breaking the rendered cube.

Among open-weights models, only Qwen 3.7 Plus produced a clean solve, and only once in five tries. GLM-5.2 again failed entirely. The price-to-performance gap between open-weights and proprietary models widened significantly on this more complex task.

What do the cost differences mean for founders?

The pricing spread is dramatic. Five raycaster attempts cost $0.12 with GLM-5.2 (zero working outputs) versus $2.35 with Claude Fable 5 (three working). Grok 4.5 hit the sweet spot on Task 1: $0.27 for five perfect runs, roughly 5x cheaper than GPT-5.6 Sol with identical success rate.

Speed varies wildly too. GPT-5.6 Luna averaged 23 seconds per raycaster attempt. DeepSeek V4 Pro averaged 318 seconds. For rapid prototyping, that 14x speed difference matters more than the cost savings.

Which AI model should founders pick for code generation?

It depends on the task complexity. For simpler interactive apps, Grok 4.5 offers the best value: cheap, fast, and consistent. For complex UI animations like the Rubik's Cube, Claude Fable 5 justifies its premium. GPT-5.6 Sol sits in the middle, matching Grok's consistency while handling harder tasks better.

Open-weights models aren't ready for production use in this context. Qwen 3.7 Plus costs almost nothing but fails most attempts. The debugging time you save with a more reliable model quickly exceeds the API cost difference.

Meta's Muse Spark 1.1 is the wild card. When it works, output quality rivals the top tier. But with 60% failure rates on some tasks, you can't rely on it without human review of every output. If you're already reviewing all AI-generated code, the price savings might justify the inconsistency.

Frequently Asked Questions

Is GPT-5.6 better than Claude for coding?

GPT-5.6 Sol outperformed Claude Opus 4.8 on the raycaster task but Claude Fable 5 dominated the Rubik's Cube challenge. Neither is universally better. Task complexity and budget determine the right choice.

How much does GPT-5.6 cost compared to Grok 4.5?

For the raycaster task, GPT-5.6 Sol cost $1.35 for five attempts while Grok 4.5 cost $0.27 with identical success rates. GPT-5.6 Luna dropped to $0.15 but with less detailed output.

Are open-source AI models good enough for code generation?

Not yet for complex tasks. Qwen 3.7 Plus, DeepSeek V4 Pro, Kimi K2.6, and GLM-5.2 all showed significantly lower success rates than proprietary models, with GLM-5.2 producing zero working outputs on multiple tasks.

What is Muse Spark 1.1 and how does it compare?

Muse Spark 1.1 is Meta's new coding model. It produced top-tier results when working but failed 60% of attempts on some tasks, making it unreliable without human review.

Source: Hacker News: Best / TryAI