Developer Switches from Cursor Composer 2 and Kimi 2.6 to Qwen3.6:35b-a3b for Enterprise Workloads

A developer on r/LocalLLaMA reports successfully replacing Cursor Composer 2 and Kimi 2.6 with Qwen3.6:35b-a3b for daily software development on a 500,000-700,000 line enterprise codebase (60 hours/week). The user previously tried Kimi 2.6 and DeepSeek 4 Pro/Flash but found Qwen3.6:35b-a3b to be the best fit.
Key Details
- Model: Qwen3.6:35b-a3b (the 3.6 version with 35b parameters and a 3b activated subset via MoE? — the user's notation is ambiguous; likely Qwen2.5-32B or a custom variant). The model supports image/screenshot input.
- Hosting: Run via OpenRouter at approximately $0.08 per 1M tokens averaged after caching and billing adjustments. The user lacks hardware for local inference.
- Workload: Full-time development on a large enterprise software suite. The user claims the model “actually understands” the codebase and task context, surpassing prior options.
- Missing feature: The only drawback noted is the lack of Cursor's cloud agents functionality and high throughput on Composer 2.
Cost Comparison
At ~$0.08/1M tokens, Qwen3.6:35b-a3b is described as “insanely cheap” for its capability level. No exact breakdown is given, but caching and usage discounts apply.
Who It's For
Developers working with large proprietary codebases who want a capable, low-cost model for AI-assisted coding without requiring local GPU hardware.
📖 Read the full source: r/LocalLLaMA
👀 See Also

Claude Code v2.1.187: Structured Output Fixes, Sandbox Security, and Org Model Restrictions
Claude Code v2.1.187 adds sandbox.credentials setting, org model restrictions, and fixes for structured output loops, remote MCP hangs, and subagent depth tracking.

Anthropic Launches Claude Code Channels for Messaging Integration
Anthropic has launched Claude Code Channels, allowing developers to DM Claude Code sessions from Telegram or Discord with full tool access including file edits, test runs, and git operations. The feature requires a paid Anthropic plan and supports two platforms compared to OpenClaw's 20+.

The Orchestrator: Why Intent Should Outlive the Process
Current agent stacks invert identity and surface — the orchestrated layer should sit between agents and runtimes, with identity, routing, handoff primitives, and cross-driver calls. Practical example: triaging a flaky test across Ollama, Gemini CLI, and Grok Build under one intent.

Anthropic's Natural Language Autoencoders Turn Claude's Activations into Readable English — Here's How
Anthropic releases Natural Language Autoencoders (NLAs) that convert Claude's internal activations into plain-text explanations, revealing model reasoning about rhymes, safety test awareness, and cheating detection.