Tutorials, deep dives and product notes — built for developers.
Qwen 3.7 Max (60.6% SWE-bench Pro, $7.50/1M, Anthropic API compatible) vs Kimi K2.6 (58.6%, $4.00/1M, 300 sub-agent swarms). Qwen leads all 6 shared benchmarks — but Kimi counters with open-weight, BrowseComp Agent Swarm (86.3%), and HLE w/tools (54%). Full comparison with real benchmark data.
MiniMax M3 (59.0% Pro, $1.20/1M, 1M ctx) vs GLM 5.1 (58.4%, $4.40/1M, 200K ctx). Both Huawei Ascend, both MIT, both Chinese. 0.6 pts apart on Pro. M3 leads context + multimodal. GLM leads reasoning + CyberGym #1 + pure MIT + $3/mo plan. Full comparison.
DeepSeek V4 Flash ($0.28/1M, MIT, 284B) vs Qwen 3.6 Flash ($0.90/1M, Apache 2.0, 35B/3B). V4 leads every coding benchmark (Pro +3.1, HLE +13.4, LiveCodeBench +11.2). Qwen counters with multimodal (text+image+video), speed (90-172 tok/s), and tiny 3B active params. Chinese Flash showdown.
MiniMax M3 (59.0% SWE-bench Pro) vs DeepSeek V4 Pro (93.5% LiveCodeBench). M3 wins benchmarks + multimodality. DeepSeek wins price ($0.87/1M), ecosystem (2,150× more adoption), and algorithmic dominance. The generalist vs the specialist — which open-weight Chinese model fits your stack?
Qwen 3.7 Max — Alibaba's "Agent Frontier" — challenges GPT-5.5 and Claude Opus 4.8 with 60.6% SWE-bench Pro, 91.6% LiveCodeBench, and a record-breaking 53.5% SciCode. At $7.50/1M output with Anthropic API compatibility. Full benchmark comparison, Tetris bot real-world test, and the verbosity tax explained.
32B active params vs 10B. $4.00/1M output vs $1.20. 58.6% SWE-bench Pro vs 56.22%. Kimi K2.6 wins on raw performance — but MiniMax M2.7 is the efficiency miracle: 94% of Kimi's coding score at 70% less cost, with only a fraction of the parameters. This is the battle between brute force and architectural genius.
0.2 points apart on SWE-bench Pro. Both open-weight. Both released in April 2026. But the similarities end there. Kimi K2.6 leads on coding (+11.1), agentic tasks (+7.8), and vision. GLM-5.1 counters with pure MIT license, Code Arena #3, and Claude Code compatibility. Here's the definitive comparison.
DeepSeek V4 Pro Max ($0.87/1M, MIT, 1.6T/49B) vs GLM 5.1 ($3.08/1M, MIT, 754B/40B). GLM leads SWE-bench Pro (58.4% vs 55.4%) & HLE w/tools. V4 Pro Max dominates 12/14 benchmarks. 3.5× price gap, 5× context gap. Updated June 9, 2026.