<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeepSWE on Lynx Tech Blog</title><link>https://blog.lynxflow.co/en/tags/deepswe/</link><description>Recent content in DeepSWE on Lynx Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Thu, 13 Aug 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://blog.lynxflow.co/en/tags/deepswe/index.xml" rel="self" type="application/rss+xml"/><item><title>Choosing a Primary Model for Your Everyday Coding Agent: Stop Looking Only at Who Ranks First on SWE-bench</title><link>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-benchmark-selection/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-benchmark-selection/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-13-coding-agent-benchmark-selection.png" alt="Featured image of post Choosing a Primary Model for Your Everyday Coding Agent: Stop Looking Only at Who Ranks First on SWE-bench" /&gt; Bottom line first: if you use Claude Code / Codex for everyday coding, you should no longer treat “#1 on SWE-bench” as the only yardstick for choosing your main model. In 2026, the more reliable public stack is Terminal-Bench + SWE-rebench/DeepSWE + a cost dashboard; the final verdict still has to come from tasks in your own repositories.
What you’re evaluating is not “can it write a function,” but “can it act like an engineer over time” If your workflow looks like this:</description></item><item><title>Coding Agent Candidate Scorecard: Grok 4.6 / Qwen3.8-Max / DeepSeek-V4 Pro / GLM-5.2</title><link>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-candidate-models-scorecard/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-candidate-models-scorecard/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-13-coding-agent-candidate-models-scorecard.png" alt="Featured image of post Coding Agent Candidate Scorecard: Grok 4.6 / Qwen3.8-Max / DeepSeek-V4 Pro / GLM-5.2" /&gt; TL;DR: This is the targeted follow-up to “How to pick your daily Coding Agent model — stop worshipping SWE-bench #1” — we put four candidates (grok-4.6, qwen3.8-max, deepseek-v4-pro, glm-5.2) on official leaderboards and pinned down each score. None of the four beats the public frontier (claude-opus-5 74% / gpt-5.6-sol 73% / fable-5 70%); but on “cheap + good enough” there is a clear route: GLM-5.2 for 80% daily work, Grok 4.6 for hard tasks, DeepSeek-V4 Pro as the cheap sub-agent. The key red</description></item></channel></rss>