MBZUAI-backed IFM released six K2 Horizon models under Apache 2.0 on September 3. The 7B beats Qwen3.5-9B on SWE-bench Verified at 70.6%, but the training-code repos and dataset links are still closed.
OpenAI shipped GPT-6 Astra on September 3 at $10 and $50 per million tokens, 2.5x GPT-5.6 Sol. On coding work it burns a third of the tokens Sol needed, and the independent Intelligence Index puts both at 61.
Google shipped Gemini 3.8 Flash on September 2 at the same $0.75 per million input tokens as 3.7 Flash. DeepSWE climbs from 65.3% to 73.7%, but general agent work lands at 19.1% against Claude Opus 5 at 51.8%.
The anonymous ox-alpha model that appeared free on August 20 and topped OpenRouter usage within six days is GLM-5.3-Flash. Zhipu released the 320B-A18B weights under MIT the same day, and the free window is now closed.
Cerebras unveiled CS-4 on August 18, packing three wafers into one rack. It claims 4,400 tokens per second per user on gpt-oss-120B and up to 30x faster than GPUs, without naming the GPU product it measured against.
Anthropic shipped Claude Opus 5 on July 24 at the same $5/$25 per million tokens as Opus 4.8, claiming more than double the coding score of the previous generation and near-Fable 5 results at half the cost. Every number is Anthropic-measured.
MAI-Code-1-Flash is rolling into GitHub Copilot for VS Code, putting Microsoft’s own 5B coding model inside Copilot’s model picker and Auto routing path.
MiniMax M3 combines 1M context, multimodality, and coding-agent benchmarks, but its weights and technical report are still pending verification.
Anthropic published Claude Mythos Preview exploit evaluations and a CVD dashboard. V8 21/41 ACE and 1,596 disclosed flaws reset security triage expectations.
A new paper turns Claude Constitution and OpenAI Model Spec into testable audit targets, showing how model policies are becoming benchmarks.
Joule Index V0.1 adds dollars, joules, and public traces to coding-agent benchmarks, shifting the question beyond accuracy alone.
Artificial Analysis ITBench-AA shows that even leading SRE agents remain below 50% on Kubernetes root-cause analysis, exposing the reliability gap in operational AI.