Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Both models stream tokens. The differences in time-to-first-token, tokens-per-second, and total-task-latency change which one wins for which workload. A practical breakdown.
GPT-5.5 ships natively omnimodal with Realtime API. Opus 4.7 is text-only — voice means STT + Opus + TTS. Here is the production trade-off for voice agent teams.
GPT-5.5 ships natively omnimodal — text, image, audio, video in one model. Opus 4.7 brings substantially better vision resolution. The strengths point in different directions.
GPT-5.5's biggest under-the-hood change is output token efficiency. Here is what that means for cost, latency, and how you should architect prompts for both models.
Both models are excellent at function calling. The differences are in error recovery, schema strictness, and how each handles concurrent tool calls. A production-focused comparison.
GPT-5.5 leads FrontierMath Tiers 1-3 at 51.7%; Opus 4.7 leads GPQA Diamond at 94.2%. Two of the hardest reasoning benchmarks point in different directions. Here is why.
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.
