# 🤖 AI Agents Weekly: Claude Duet, Kimi K3.1 Pro, MiniMax M3.1 Flash, Recursive Flint, DeepSeek DeepInfer, Ramp Laminar, Mercury 3, and More

> Source: <https://nlp.elvissaravia.com/p/ai-agents-weekly-claude-duet-kimi>
> Published: 2026-10-24 16:49:07+00:00

In today’s issue:

- Moonshot launches Kimi K3.1 Pro
- Anthropic previews Claude Duet
- OpenAI coordinates critical PHP fixes
- MiniMax opens M3.1 Flash weights
- Recursive ships local coding model Flint
- DeepSeek open-sources DeepInfer
- Ramp details its Laminar harness
- Inception launches diffusion LLM Mercury 3
- Databricks launches MemCurrent agent memory
- Naive RAG beats agentic memory

And all the top AI dev news, papers, and tools.

## Top Stories

### Kimi K3.1 Pro

Moonshot AI introduced Kimi K3.1 Pro, its most capable 3T-class model and the first in the K3.1 series, with full weights due by November 4. It is a multimodal Mixture-of-Experts model with 2.8T total and 104B active parameters and a 1M-token context, post-trained from Kimi K3 with substantially more RL compute.

- **Coding results:** On Terminal-Bench 4.0, K3.1 Pro scores 65.7%, ahead of GPT-6 Astra (58.2%) and just behind Claude Opus 5.5 (66.4%), and Moonshot’s charts put it first on SWE Marathon (51.7) and Agents’ Last Exam (43.2). It also builds full projects in game engines such as Unreal, Unity, and Godot.
- **3D and CAD work:** K3.1 Pro writes and runs scripts for Blender and CAD software to build and edit assets. On BenchCAD, which reconstructs 3D objects from multi-view renderings through CAD code, it scores 92.7% with tools, against 96.2% for Claude Opus 5.5 and 95.9% for GPT-6 Astra.
- **Finance and factual accuracy:** It sets a state-of-the-art 64.4 on OfficeQA Pro v2 and improves on K3 by 7.2 points in financial modeling and 11.6 points in financial research on internal benchmarks. On Moonshot’s Kimi Hallu Bench Hard stress test, the claim-level hallucination rate fell from 23.4% for K3 to 8.3%.
- **Cyber controls:** For the open-weight release, Moonshot removed cyber-related data from mid-training and the RL environment mix. A checkpoint without these controls solves 90.5% of its xdev-bench-hard exploit tasks at pass@3, while the final model reaches 81.0%.
- **Availability:** K3.1 Pro and K3.1 Pro Highspeed, which runs 2 to 3x faster, are live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.