#GLM-5.2

Tutorials, deep dives and product notes — built for developers.

GLM-5.2 vs Qwen 3.7 Max: The Closest Open-Weight vs Proprietary Coding Fight

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.

· CodingFleet

GLM-5.2 vs DeepSeek V4 Pro: The SWE-bench Leader vs The Algorithm King

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.

· CodingFleet

GLM-5.2 vs MiniMax M3: The Text-Only Titan vs The Multimodal Maverick

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.

· CodingFleet

Claude Opus 4.8 vs GLM-5.2: 0.7 Points From the Coding King at 1/6 the Price

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.

· CodingFleet

GLM-5.2 vs GPT-5.5: The MIT Open-Weight Model That Beats OpenAI's Flagship on Pro

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.

· CodingFleet