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  • ⚔️ GPT-5.5 vs Opus 4.7: Most Detailed Real-World Test Yet. Real Costs, Real Tasks, Real Winner!

⚔️ GPT-5.5 vs Opus 4.7: Most Detailed Real-World Test Yet. Real Costs, Real Tasks, Real Winner!

I pushed both models through 4 real tasks most people skip. You’ll see where each model breaks, where it adapts, and the one moment that changes the verdict.

Author

Neil Phan
April 28, 2026

TL;DR

GPT-5.5 won most coding tests against Opus 4.7. It finished 1.8x faster, used 3x fewer output tokens, and cost $2.87 less across four real tasks. This post breaks down a side-by-side test on a landing page, a solar system, a space shooter, and an ecosystem simulation.

You will see real timing, token counts, and dollar costs, not benchmarks. The deeper lesson is that token efficiency beats per-token price. Output cost shapes your real bill, and the model that writes less wins more often.

Key points

  • Fact: GPT-5.5 used 70,000 output tokens to Opus 4.7's 250,000.

  • Mistake to avoid: picking a model from the price card alone.

  • Takeaway: run one real prompt through both before switching.

Opus 4.7 still wins on visual taste, so for landing pages and creative front-end work, the slower run is worth the few extra cents.

I. Introduction: GPT-5.5 vs Opus 4.7

Both models target the same buyer: the founder, the indie builder, or the small team trying to ship faster without burning budget. OpenAI released GPT-5.5 just one week after Anthropic released Claude Opus 4.7.

GPT-5.5 is sold as a model that does more with fewer tokens. OpenAI release page calls it faster, sharper, and better at moving through messy multi-step tasks without hand-holding.

Opus 4.7 is sold as the king of long-running agent work, with a focus on coding that holds up across hours of context.

Both companies use the word "agentic." Both want your monthly bill. So we’re breaking down the GPT-5.5 vs. Opus 4.7 rivalry based on the metrics that actually hit your bank account:

  • Token Efficiency: Does the model solve the problem in 50 lines or 500?

  • Visual Logic: Can it interpret editorial minimalist without making it look like a 2010 tutorial?

  • Execution Speed: How many hours of waiting for output are you saving per week?

  • Logic Under Pressure: Does the code actually run, or does it break once the systems start interacting?

We ran both models through 4 real-world builds: a personal brand landing page, an interactive solar system, a 2D space shooter, and a complex ecosystem simulation.

If you want to see the raw logs and verify the token counts yourself, the full JSONL data and prompt sets are linked below.

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