Qwen3.5-35B-A3B-UD-Q6_K_XL Tested in Production Development Workflows

A developer on r/LocalLLaMA shared detailed testing results of the Qwen3.5-35B-A3B-UD-Q6_K_XL model in production development scenarios. The user conducted both benchmark testing and practical application across real client projects.
Performance Benchmarks
The model achieved benchmark scores of 1504pp2048 and 47.71 tg256. Token generation speed was solid when spread across two GPUs, and increased to 80 tokens per second (tps) when running on a single GPU.
Production Testing Methodology
The developer tested the model across five different projects using Git Worktrees to roll back to known specifications and features. Specifications for these tests were generated by Claude, with the developer using a Max Pro plan for the past year.
- Tested across JavaScript, Go, and Rust projects
- Used Git Worktrees for version control during testing
- Most "bugs" required only 5-minute tweaks or could be fixed with a second prompt
- Compared the experience to using Sonnet 4
Practical Results and Business Implications
The developer reported that Qwen3.5 "nailed them out of the park" for the work they do, particularly noting strong performance on Go and Rust projects. This has prompted serious consideration of switching from API-based models to a hybrid approach: using SOTA models via API for specification generation and reviews, while using local models for development work.
The testing has raised questions about hardware investment versus subscription costs. The developer has already spent $2,000 on Claude Pro Max since June 2025, with potential costs reaching $6,800 by 2027 if subscriptions continue. This has led to consideration of purchasing an RTX 6000 Pro as a business investment.
The developer has been using Qwen Coder for tab completion previously, but found Qwen3.5 takes local model capabilities to a new level for production use.
📖 Read the full source: r/LocalLLaMA
👀 See Also

Codesight: AI Context Engine Cuts 30K-60K Tokens from Claude Code Sessions
Codesight is an open-source tool that analyzes codebases to provide AI coding agents with structured context, reducing token waste. A developer collaborated with the maintainer to add AST parsing for Next.js and Prisma, an eval suite, token telemetry, and profiles for Claude Code and Cursor.

Alternative AI Coding Setup After Claude Price Increase
A developer shares their current AI coding setup using GPT 5.4 as the primary model, Codex as a fallback included in ChatGPT subscription, and Minimax 2.7 as a backup with coding plan pricing.

MCP Server for Semantic Search in Obsidian Vaults
A developer built an MCP server that indexes Obsidian vaults into Qdrant with local embeddings, enabling semantic search instead of keyword matching. It chunks markdown by headings, uses BAAI/bge-small-en-v1.5 embeddings, and works with Claude Code, Cursor, Windsurf, or any MCP client.
DashClaw: An Approval Layer for OpenClaw Agents
DashClaw is an open-source approval layer that intercepts destructive tool calls in OpenClaw, Claude Code, and other agents, requiring human approval via Telegram before execution.