Red-Teaming GPT-5.1: What We Learned
Notes from the teams shipping long context to real users.
For most of the last year, the conversation around long-context workflows has been louder than the evidence. That is starting to change.
Inside HubSpot, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
The cost curve matters here. Llama 4 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.
The skeptical read is that we are watching a feature, not a platform. The optimistic read is that long context is exactly the kind of feature that becomes a platform when nobody is paying attention.
The cost curve matters here. GPT-5.1 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.
Inside Atlassian, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
Teams that win with tool-first agents tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
None of this guarantees a clean story. Alibaba Qwen could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.