## GLM 5.2 vs DeepSeek V4 Pro — Full Comparison Both are **MIT-licensed, open-weight MoE models** with **1M token context windows**, released in 2026 by Chinese AI labs. But they target very different strengths. --- ### At a Glance | Attribute | GLM 5.2 (Z.ai) | DeepSeek V4 Pro | |---|---|---| | **Released** | June 13, 2026 | April 24, 2026 | | **Params** | 753B total, ~40B active MoE | 1.6T total, ~49B active MoE | | **Context** | 1M tokens | 1M tokens | | **Max Output** | 131K tokens | 384K tokens | | **License** | MIT | MIT | | **Modalities** | Text only | Text + Vision | | **API** | Anthropic native | Dual-mode OpenAI + Anthropic | | **Code Execution** | ✅ Yes | ❌ No | --- ### 💰 Pricing — DeepSeek is 3–5× Cheaper | | GLM 5.2 | DeepSeek V4 Pro | Advantage | |---|---|---|---| | **Input / 1M tokens** | $1.40 | **$0.435** | 3.2× cheaper | | **Output / 1M tokens** | $4.40 | **$0.87** | **5.1× cheaper** | | **Est. monthly cost** | ~$2,220 | ~$566 | Saves ~$1,655/mo | --- ### 📊 Shared Benchmarks — GLM 5.2 Wins All 4 | Benchmark | GLM 5.2 | DeepSeek V4 Pro | Margin | |---|---|---|---| | **SWE-bench Pro** ★ | **62.1%** | 55.4% | GLM **+6.7** | | **MCP Atlas** | **77.0%** | 73.6% | GLM **+3.4** | | **HLE (with tools)** | **54.7%** | 48.2% | GLM **+6.5** | | **HLE (no tools)** | **40.5%** | 37.7% | GLM **+2.8** | | **Google-Proof Q&A** | **91.2%** | 90.1% | GLM **+1.1** | | **BenchLM Overall** | **90/100 (#9)** | **68/100 (#31)** | GLM dominates | --- ### 🏆 DeepSeek-Only Benchmarks — Uncontested Dominance | Benchmark | DeepSeek V4 Pro | Significance | |---|---|---| | **LiveCodeBench** | **93.5%** | #1 globally — ANY model (open or closed) | | **Codeforces Rating** | **3,206** | Highest open-weight by a wide margin | | **SWE-bench Verified** | **80.6%** | Highest open-weight (tied w/ Gemini 3.1 Pro) | | **GPQA Diamond** | **90.1%** | Frontier reasoning | | **HMMT 2026** | **95.2%** | Harvard-MIT Math Tournament | | **MMLU-Pro** | **87.5%** | Broad knowledge | *(GLM 5.2 hasn't published on any of these benchmarks.)* --- ### ⚡ High-Effort Mode (DeepSeek V4 Pro High → narrows the gap) | Category | GLM 5.2 | DS V4 Pro (High) | Winner | |---|---|---|---| | **Agentic** | **81** | 70 | GLM +11 | | **Coding** | 62.1 | **73.8** | DeepSeek **+11.7** | | **Knowledge** | **67.2** | 62.6 | GLM +4.6 | | **BenchLM Overall** | **90** | 82 | GLM | --- ### 🎯 Verdict — Choose Based on Your Use Case **Pick GLM 5.2 if you need:** - ✅ Best open-weight real-world software engineering (SWE-bench Pro leader at 62.1%) - ✅ Agentic AI / tool orchestration (MCP 77%, agentic score 81) - ✅ Anthropic API compatibility (Claude Code drop-in) - ✅ Code execution support **Pick DeepSeek V4 Pro if you need:** - ✅ **Competitive programming & algorithms** — it's the #1 model globally on LiveCodeBench (93.5%) with a 3206 Codeforces rating - ✅ **Advanced math reasoning** — 95.2% on HMMT, 90.1% GPQA - ✅ **Budget/value** — 5× cheaper output pricing, massive savings at scale - ✅ **Vision/multimodal** (GLM is text-only) - ✅ **Longer max outputs** (384K vs 131K tokens) - ✅ **Multilingual coding** (76.2% SWE Multilingual) **Bottom line:** - **GLM 5.2** wins on **shared benchmarks** (SWE-bench Pro, agentic, tool use) and is the stronger all-around open-weight coding model for real-world software engineering. - **DeepSeek V4 Pro** dominates **contested algorithm/math strongholds** that GLM doesn't even compete on, and does so at a **fraction of the cost** — making it the algorithmic reasoning value king.