AI tool comparison
DeepSeek V4 vs GLM-5.1
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Open Source Models
DeepSeek V4
1.6T open-source MoE that nearly matches frontier — MIT, 1M token context
75%
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Community
Paid
Entry
DeepSeek V4 dropped April 24, 2026 as two production-ready Mixture-of-Experts models: V4-Pro (1.6T parameters, 49B activated) and V4-Flash (284B parameters, 13B activated). Both support 1 million token context and ship under the MIT license — the most permissive option in AI. The architecture innovation is the hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), which slashes long-context inference costs dramatically. At 1M tokens, V4-Pro requires only 27% of the FLOPs and 10% of the KV cache compared to DeepSeek V3.2 — a meaningful efficiency gain that makes million-token context economically viable. Performance-wise, DeepSeek V4-Pro beats all rival open models on math and coding benchmarks, trailing only Google's Gemini 3.1-Pro (closed) on world knowledge. One year after V2 upended the industry, DeepSeek has done it again — a model approaching frontier performance that anyone can run, modify, and ship commercially with zero licensing friction.
AI Models
GLM-5.1
Zhipu AI's 744B MIT-licensed model that beats Claude and GPT on SWE-Bench
50%
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Community
Paid
Entry
GLM-5.1 is Zhipu AI's latest open-weights language model — a 744B parameter mixture-of-experts (MoE) architecture that activates 40B parameters per forward pass. Released under the MIT license with a 200,000-token context window, it has quietly topped the SWE-Bench Pro leaderboard, surpassing both Claude Opus 4.6 and GPT-5.4 on expert-level software engineering tasks. The MoE architecture means GLM-5.1 is significantly cheaper to run per token than a dense 744B model, with inference costs approaching dense 40B models for most workloads. Zhipu AI (a Tsinghua University spin-out) has steadily iterated on the GLM family to produce a text-focused reasoning model that holds its own against proprietary frontier models — now, for the first time, reportedly exceeding them on coding benchmarks. The MIT license is the headline for enterprise and research users: full commercial use, no usage restrictions, no API dependency. This puts GLM-5.1 in direct competition with Qwen3.5 for the "best open-weights model you can actually use for anything" crown, with a differentiating edge in software engineering tasks specifically.
Reviewer scorecard
“MIT license on a 1M context model that beats GPT-5 on coding evals is wild. V4-Flash at 13B active params is particularly practical — you get near-frontier coding performance with inference costs that don't require a mortgage. Ship immediately.”
“SWE-Bench Pro beating Claude and GPT-5.4 is the real signal here. For coding automation workflows, having an MIT-licensed 200K context model at that quality tier changes the build-vs-buy calculus significantly. Deploying this on dedicated hardware is now a serious option for engineering teams.”
“Running 1.6T parameters requires infrastructure most companies don't have, and DeepSeek's API has had reliability issues before. The 'MIT license' is less useful when you're dependent on their API anyway. Wait for quantized local versions to stabilize.”
“744B total parameters still requires serious infrastructure — you're looking at 8x H100s at minimum for comfortable inference. The 40B active parameters help with cost but not with deployment complexity. This is 'open source' for well-funded teams, not indie builders.”
“The efficiency breakthrough is the story. If 1M-token context now costs 73% less to serve, that changes the economics of an entire class of applications. DeepSeek is compressing the frontier timeline faster than anyone predicted a year ago.”
“The open-weights ecosystem has now fully caught up to proprietary models on the most demanding software engineering benchmarks. This is the moment the 'open vs closed' debate definitively changes — the argument that proprietary models are categorically better no longer holds.”
“A million-token context means I can feed an entire brand style guide, all past campaign materials, and a full brief into one call. V4-Flash is fast enough for real-time creative iteration. This is now my go-to for long-context creative workflows.”
“Unless you're a creative tech team with serious infrastructure, this isn't practical for most creative workflows. The quality is undeniably impressive but the deployment story doesn't fit solo creators or small studios.”
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