Benchmark on Lynx Tech Blog

The Complete Guide to LLMs for Novel Writing, September 2026: A Data-Driven Comparison Across Five Dimensions (with Real Benchmarks for DeepSeek V4.1 / GLM-5.3 / Kimi K3 / Qwen3.8)

Fri, 11 Sep 2026 00:00:00 +0800

Picking an AI model for novel writing, the internet is full of claims — “Claude has the best prose,” “DeepSeek has the densest foreshadowing,” “Kimi is in a league of its own for ultra-long-context continuation.” Which of these are backed by actual testing, and which are marketing? This article pulls together all publicly available raw benchmark data as of September 2026, evaluates models across the five dimensions that actually matter for novel writing, and g

Choosing a Primary Model for Your Everyday Coding Agent: Stop Looking Only at Who Ranks First on SWE-bench

Thu, 13 Aug 2026 00:00:00 +0800

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:

Coding Agent Candidate Scorecard: Grok 4.6 / Qwen3.8-Max / DeepSeek-V4 Pro / GLM-5.2

Thu, 13 Aug 2026 00:00:00 +0800

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