DeepSeek V4 Pro Max vs GLM-5.1: Chinese Open-Weight Coding Models

Both are MIT-licensed. Both are Chinese. Both are MoE architectures targeting developers who want frontier coding without proprietary lock-in. But DeepSeek V4 Pro Max and GLM 5.1 represent two fundamentally different bets on what "open-weight value" means. V4 Pro Max (April 24, 2026): 1.6T total / 49B active, $0.87/1M output (permanent 75% discount), 93.5% LiveCodeBench — global #1. 1M context. GLM 5.1 (April 7, 2026): 754B total / 40B active MoE, $3.08/1M output, 58.4% SWE-bench Pro — briefly #1 at launch. Trained entirely on Huawei Ascend 910B chips. GLM leads the hardest coding benchmark. V4 Pro Max dominates everything else — and is 3.5× cheaper. Here's the complete data. Test both on CodingFleet.

Compare models on your own code at CodingFleet. See the SWE-bench Pro and Terminal-Bench leaderboards. Also: V4 Pro vs Qwen 3.7 Max · Pricing Calculator · GPT-5.5 vs V4 Pro.

Benchmark Comparison

MIT Open-Weight Radar

The radar reveals the asymmetry. GLM 5.1 spikes on two axes: SWE-bench Pro and HLE with tools — its signature strengths. V4 Pro Max dominates the other four — Terminal-Bench, GPQA, MMLU-Pro, and Context Window (1M vs 200K — a 5× gap). GLM's Pro lead is real but isolated; V4 Pro Max wins on breadth.

Pricing & Architecture

Where Each Model Wins

GLM 5.1 — The Pro Leader & NVIDIA-Free Pioneer

• SWE-bench Pro 58.4% — #1 at launch. The first open-weight model to top the Pro leaderboard. The 3-point lead over V4 Pro Max is genuine and independently verified from the DeepSeek model card.
• HLE with tools 50.4% — best-in-class agentic reasoning. When paired with tools, GLM 5.1 achieves the highest HLE score among open-weight models.
• 8-hour autonomous coding sessions. Purpose-built for long-horizon agentic workflows — planning, executing, and self-correcting over multi-hour sessions.
• Trained on Huawei Ascend 910B — zero NVIDIA GPUs. For organizations concerned about US chip dependency, GLM 5.1 proves frontier coding models can be built on alternative hardware.
• Faster inference: 58-128 tok/s. With 40B active parameters (vs V4's 49B), GLM achieves higher throughput. Better for interactive coding sessions.
• Code Arena Elo 1530 — highest open-source ranking. Developer preference validates the Pro benchmark. #3 overall behind only Claude Opus 4.8 and GPT-5.5.

DeepSeek V4 Pro Max — The All-Round King

• Wins 12 of 14 benchmarks. Terminal-Bench, GPQA, HLE, MMLU-Pro, HMMT, BrowseComp, MCP Atlas, SWE Multilingual, Apex Shortlist — V4 Pro Max leads every single one. GLM's Pro victory is real, but surrounded by blue.
• LiveCodeBench 93.5% — global #1. For algorithms and competitive programming, V4 Pro Max is the best model in the world — proprietary or open-weight.
• 3.5× cheaper: $0.87 vs $3.08 per 1M output. Permanent 75% discount. A 100M token/month pipeline costs $87 vs $308 — a $221 monthly difference.
• 1M native context — 5× GLM's 200K. 384K max output vs 128K. For full-codebase analysis and large-scale refactors, the context advantage is decisive.
• 1.6T total parameters — 2.1× larger. 57.9% SimpleQA-Verified vs GLM's 38.1% — a 19.8-point factual accuracy advantage for knowledge-heavy coding.

When to Use Which

Conclusion: Two MIT Philosophies, Two Different Winners

GLM 5.1 is the Pro leader for a reason. The 58.4% SWE-bench Pro score is independently verified from DeepSeek's own model card comparison table. It was the first open-weight model to top the Pro leaderboard — and the first Chinese model to do so. For real GitHub issue resolution, GLM 5.1 is the strongest open-weight option. The Huawei Ascend training story adds geopolitical significance that no other model can claim.

DeepSeek V4 Pro Max is the better model for almost everything else. Terminal-Bench, GPQA, LiveCodeBench, HMMT, MMLU-Pro, BrowseComp, MCP Atlas, SWE Multilingual, Apex Shortlist — V4 Pro Max leads every single one. The 3.5× price advantage, 5× context window, and MIT weights available today make it the default choice for cost-sensitive, large-context, and algorithm-heavy workflows. The only reasons to choose GLM over V4 Pro Max are if you specifically need the highest Pro score, NVIDIA-free infrastructure, or Claude Code integration.

Both are MIT-licensed. Both are Chinese. Both prove that open-weight models compete at the frontier. The choice: GLM 5.1 for Pro supremacy and Huawei sovereignty. DeepSeek V4 Pro Max for everything else — at 3.5× lower cost.

Sources: DeepSeek V4 Model Card (all head-to-head comparison data) | Serenities AI GLM-5.1 Review | OpenRouter | BuildFastWithAI April 2026 | Spheron Deployment Guide | SWE-bench Pro Leaderboard | Terminal-Bench Leaderboard.