Tutorials, deep dives and product notes — built for developers.
Hy3 (295B MoE, Apache 2.0, $0.80/1M) vs GLM 5.2 (753B MoE, MIT, $4.40/1M). GLM 5.2 wins every coding benchmark by 4-18 points. Hy3 counters with MCP Atlas #1 open-weight (79.1%), BrowseComp 84.2%, DeepSearchQA 91.0%, 47% fewer tokens, and 5.5× cheaper. Full comparison with 5 charts and a 10-point verdict.
Claude Sonnet 5 vs DeepSeek V4 Pro: Sonnet leads every coding benchmark (+7.8 Pro, +9.2 HLE tools). DeepSeek is #1 global on LiveCodeBench (93.5%), MIT open-weight, and 7.8× cheaper per task ($0.12 vs $0.90). Is 7.8 more Pro points worth 7.8× the cost?
Claude Sonnet 5 vs GLM 5.2: near-ties on every benchmark (±0.6-2.7 pts). GLM 3.4x cheaper on output, MIT open-weight, self-hostable. Sonnet has OSWorld, BrowseComp, Anthropic safety ecosystem. Proprietary premium vs open-weight value.
GLM-5.2 vs GLM-5.1: the full sibling comparison. DeepSWE +28.2 (18.0→46.2), HMMT +9.9, GPQA +5.0, Pro +3.7. 200K→1M context (5×). Single→dual thinking modes. Anthropic API native. Same MIT license, same $4.40/1M. All data from Z.ai official blog.
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.
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.
DeepSeek V4 Flash ($0.28/1M, MIT) vs Gemini 3 Flash ($3.00/1M). Flash leads Pro (+3.0), GPQA (+6.9), MCP Atlas (+7.0). Gemini leads OSWorld (65.1%), multimodal input, and Toolathlon. 10.7× price gap. Two Flash-tier models, zero overlap.