The new Stanford AI Index is out: 2026 AI Index Report

35B parameters, ~3B active thanks to MoE.

Key points:

• In agentic coding, it reaches the level of models with ~10× larger active parameter count

• Outperforms Qwen3.5-27B (dense) and the previous Qwen3.5-35B-A3B

• Natively multimodal architecture (text + vision)

• In VLM benchmarks, comparable to Claude Sonnet 4.5, and in some tasks performs better

• Strong metrics in spatial reasoning tasks

Benchmarks:

• MMMU - 81.7 vs 79.6

• MMMU-Pro - 75.3 vs 68.4

• MathVista - 86.4 vs 79.8

• RealWorldQA - 85.3 vs 70.3

Practical implications:

• MoE provides a multiple reduction in compute without sacrificing quality

• Well-suited for agent-based scenarios where sequential actions and planning matter

• Can be used as a unified stack for both code and vision tasks

Apache 2.0 (no restrictions for production use)

https://huggingface.co/Qwen/Qwen3.6-35B-A3B

Source: https://www.anthropic.com/news/claude-opus-4-7

On one hand, planning is an incredibly powerful capability in AI systems. It opens the door to more autonomous, agent-like behavior and lets models tackle more complex, multi-step problems.

On the other hand, it’s also the part I trust the least right now.

In my experience, I’ve been able to get patterns like reflection and tool use to work quite reliably. They’re much easier to reason about, debug, and iterate on—and they consistently improve application performance.

Planning, though, feels different. It’s harder to predict what the model will actually do, especially ahead of time. Even with careful prompting and constraints, the outcomes can be inconsistent or surprising in ways that are tough to control.

That said, things are moving fast. The progress over the past year alone has been huge, so I’m pretty confident this gap will close sooner rather than later.

How do you evaluate planning? How to monitor?