ChatGPT 5.5 vs Claude Opus 4.7: I Tested Both

Key Takeaways

• Claude Opus 4.7 released April 16, supports higher‑resolution images
• GPT‑5.5 launched April 23, native multimodal across text, image, audio, video
• GPT‑5.5 output cost $30 per million tokens, ~17% higher than Claude
• Claude’s new tokenizer packs 1‑1.35× more tokens per prompt
• Claude feels smarter on complex tasks; GPT‑5.5 runs faster

Pulse Analysis

The rapid succession of Anthropic’s Claude Opus 4.7 and OpenAI’s GPT‑5.5 underscores a sharpening rivalry in the frontier‑model market. Both firms are pushing the envelope: Claude adds three‑times higher image resolution and a tokenizer that squeezes more content into each token, while OpenAI’s "Spud" model consolidates text, image, audio, and video processing into a single architecture. This convergence of capabilities signals that next‑generation AI will be less about siloed specialties and more about unified, flexible assistants that can handle diverse workloads without external stitching.

For businesses, the headline‑grabbing features matter only insofar as they translate into operational efficiency and cost predictability. GPT‑5.5’s $30 per million output‑token price represents a 17% premium over Claude, but OpenAI argues the model uses roughly 40% fewer tokens to complete the same task, potentially offsetting the higher rate. Meanwhile, Claude’s unchanged per‑token pricing masks a subtle shift: its new tokenizer can inflate token counts by up to 35%, nudging actual spend upward. Companies deploying large‑scale generative workflows need to monitor token utilization closely, especially for long‑context applications where even small pricing differentials compound quickly.

The practical takeaway from the author’s side‑by‑side test is a nuanced model‑selection rule rather than a blanket recommendation. Claude’s deeper reasoning shines on intricate coding and reasoning challenges, whereas GPT‑5.5’s speed advantage benefits rapid prototyping and real‑time agent execution. Enterprises should align model choice with task priority—accuracy versus latency—and factor in the evolving pricing structures. As both providers iterate, staying agile with usage analytics will be key to maximizing the value of these powerful, yet cost‑sensitive, AI engines.