February: Claude Opus 4.6

Anthropic launched Claude Opus 4.6 with upgrades in coding accuracy and performance. It is the model family tied to the teams of coding agents and the very large memory (a million tokens) that I mention in the talk. Business Today covered the launch.

April: Claude Mythos, withheld

Anthropic said it had trained a frontier model too capable at finding security weaknesses to release publicly. It described the project on its Project Glasswing page: a general-purpose, unreleased model, with the aim of helping secure critical software while defenders still have the advantage. I cover this story in detail in another post.

April: GPT-5.5 and DeepSeek V4

Fortune reported that OpenAI released GPT-5.5 to paid subscribers only six weeks after GPT-5.4, with the pitch of more agentic work and fewer hallucinations. In the same month DeepSeek released V4, an open-weight rival with a one-million-token context window. Two points worth noticing: the release cadence is now weeks, not years, and open-weight models keep close behind the closed ones.

May: an AI-assisted result on an 80-year-old maths conjecture

An OpenAI model disproved Erdős's unit-distance conjecture, a problem from the 1940s. The mathematician Tim Gowers called the result a milestone. This one matters because proving or disproving something new is different from answering exam questions someone already solved. I could not load the original report while writing this, so I am treating the details as secondhand until I can read it.

September: GPT-6 "Astra"

The New Stack reported on the GPT-6 launch and its benchmarks. On my slide I noted the claim that it operates tools like Excel, Blender and Power BI the way a person does, and that OpenAI said we are now in the AGI era. That last line is a company claim, and I put it in quotes on purpose.

September: Gemini 4 and Claude Opus 5.5

TechCrunch reported that Google released Gemini 4 Argon on 30 September, calling it a workhorse for coding and cybersecurity work. Claude Opus 5.5 arrived around the same time as a near-frontier model at lower cost.

How to read all of this

Announcement is not evidence. Wait for independent tests before you change a project because of a benchmark.
Cheaper near-frontier models usually matter more to students than the single best model, because you can actually afford to use them.
Security is now part of every release. Several of these models are marketed for finding and fixing vulnerabilities.

Sources

Part of the talk "The AI Era" by Faisal Adnan, Riphah International University, 6 November 2026.

AI in 2026 So Far: Models That Find Bugs, Run Software and Do Research