Tutorials, deep dives and product notes — built for developers.
GLM-5.2 (62.1% Pro, MIT, $4.40) vs Qwen 3.7 Max (60.6%, proprietary, $7.50). Near-ties everywhere: Pro +1.5, MCP +0.6, HLE -0.9. Qwen dominates math (GPQA 92.4%) and is the Agent Frontier (35hr autonomous). GLM is MIT open-weight. Full comparison.
GLM-5.2 (62.1% Pro, $4.40/1M) vs DeepSeek V4 Pro (55.4%, $0.87/1M). GLM leads all shared benchmarks (+6.7 Pro, +6.5 HLE, +3.4 MCP). But DeepSeek dominates competitive coding: LiveCodeBench 93.5% (#1 global), Codeforces 3206, GPQA 90.1%. Both MIT, both 1M context. Full comparison.
GLM-5.2 (62.1% Pro, MIT, $4.40/1M) vs MiniMax M3 (59.0%, open-weight, $1.20/1M). GLM leads all shared benchmarks (+3.1 Pro, +15.0 TB 2.1, +2.8 MCP Atlas). But M3 is 3.7× cheaper, multimodal (video+image+desktop), and leads BrowseComp (83.5%). Text-only powerhouse vs the Swiss Army knife. Full comparison.
Claude Opus 4.8 leads every benchmark — but GLM-5.2 is within 0.7 pts on FrontierSWE and 0.8 pts on MCP Atlas. At $4.40 vs $25 per 1M (5.7× cheaper) with MIT open weights, GLM-5.2 is the first open-weight model that makes Opus look expensive. Full 8-benchmark comparison from Z.AI & LLM Stats data.
GLM-5.2 (62.1% Pro, MIT open-weight, $4.40/1M) beats GPT-5.5 (58.6%, $30/1M) on SWE-bench Pro by 3.5 points at 1/7 the cost. Also leads HLE w/tools (+2.5), FrontierSWE (+1.8), MCP Atlas (+1.7). GPT-5.5 counters with DeepSWE (+23.8), TB 2.1 (+3.0). Full comparison with 12 shared benchmarks from Z.AI/VentureBeat 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.
17 budget AI coding models ranked by output price ($0.28–$5.00/1M), SWE-bench Pro scores, and real-world CodingFleet speed. DeepSeek V4 Flash cheapest ($0.28). MiniMax M3 best open-weight (59.0% Pro). GPT-5.4 Mini fastest (439.8 char/s). Complete value-per-dollar analysis.
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.