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Both models advertise 1M-token context. On the OpenAI MRCR v2 8-needle 512K-1M test, GPT-5.5 retrieves 74.0% vs Opus 4.7's 32.2%. Why context size and retrieval quality are different problems.
GPT-5.5 hit 82.7% on Terminal-Bench 2.0, leading Claude Opus 4.7's 69.4% by over 13 points. What the test measures, why GPT-5.5 wins, and what it means for agentic coding.
Opus 4.7 hits 87.6% on SWE-bench Verified and leads SWE-bench Pro at 64.3%. GPT-5.5 trails on Pro but wins on adjacent agentic benches. Here is what the numbers mean.
GPT-5.5 lists at $5/$30 per 1M tokens and Opus 4.7 at $5/$25, but token efficiency flips the math. Here is the real per-task cost comparison.
Computer use moves the job from clicking to specifying, supervising, and verifying. The exact skills and hiring shifts teams need to ship it in production.
Agentic AI in Education in Australia: a 2026 field report on what production agentic AI teams are shipping, where the stack is converging, and the regulatory + ma...
Agentic AI in Education in Canada: a 2026 field report on what production agentic AI teams are shipping, where the stack is converging, and the regulatory + marke...
Agentic AI in Education in Brazil and Latin America: a 2026 field report on what production agentic AI teams are shipping, where the stack is converging, and the ...
Agentic AI in Education in Singapore and Southeast Asia: a 2026 field report on what production agentic AI teams are shipping, where the stack is converging, and ...
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