The AI community has grown accustomed to splashy model launches choreographed like Apple keynotes. So when a 764-billion-parameter language model materializes through GitHub commit logs and cryptic API endpoints, people notice. Zhipu AI’s GLM-5 isn’t just another incremental upgrade, it’s a stealth deployment that rewrites the rules of how AI labs announce their presence.
The Pull Request That Launched a Thousand Speculations
On February 9, 2026, HuggingFace maintainer Cyril Vallez merged a seemingly routine PR into the transformers repository. The commit message was almost insultingly terse: “Add GlmMoeDsa.” Those four words triggered a cascade of discovery across the AI development community.
The PR introduced GlmMoeDsaForCausalLM, a new architecture class that inherits from DeepSeek V3.2’s implementation. Within hours, developers connected the dots: DSA stands for DeepSeek Attention, the MoE (Mixture of Experts) routing mechanism that made DeepSeek’s models both powerful and efficient. The model configuration files told a story of staggering scale, 764 billion total parameters with only 44 billion active per forward pass.
This isn’t a typo. We’re looking at a model roughly 2.5x the size of GPT-4, using a sparse activation pattern that echoes the efficiency-first approach that made GLM-4.7-Flash’s real-world agentic workflow capabilities so compelling for developers running hardware locally.
The Pony Alpha Smoking Gun
The most compelling evidence isn’t in the code, it’s in the API. A model mysteriously labeled “Pony Alpha” appeared on OpenRouter around the same time the GitHub PRs materialized. Independent testers ran it through Sam Peach’s EQ Bench for creative writing, and the results stopped developers mid-scroll: performance comparable to Claude Sonnet 4.5, Anthropic’s gold standard for creative tasks.
One developer noted the model “feels way larger than GLM 4.5” and “uses much of Deepseek V3.2’s architecture.” Another calculated the parameter counts from API responses, confirming the 764B/44B split. The technical consensus formed quickly: Pony Alpha is GLM-5 wearing a fake mustache.
This stealth deployment strategy is brilliant. While Western labs pre-announce models months in advance, Zhipu AI is running live A/B tests in production, gathering real-world data without the hype cycle’s distortion. It’s the difference between a controlled lab environment and battlefield testing.
China’s Calculated AI Arms Race
The timing isn’t accidental. As the South China Morning Post reported, China’s AI sector is “bracing for a monumental week” with multiple flagship releases timed before Lunar New Year. Alibaba’s Qwen-3.5 and Zhipu’s GLM-5 are firing simultaneously, creating a domestic competition shockwave that mirrors the US OpenAI-Anthropic rivalry.
But GLM-5’s stealth approach reveals a deeper strategy. In a race where US sanctions restrict access to advanced chips, Chinese labs can’t afford wasteful compute. Every training cycle must count. By quietly deploying and iterating, Zhipu AI validates its architecture in production without burning political capital or tipping off competitors.
The model’s DeepSeek lineage is politically significant. DeepSeek’s open-weights approach has made it the Linux kernel of Chinese AI, a shared foundation that domestic labs can build upon without reinventing the wheel. GLM-5 inherits this DNA, suggesting Zhipu AI is betting on collaborative infrastructure rather than proprietary silos.
The 764 Billion Parameter Problem
Here’s where the controversy ignites. The AI community has been flirting with smaller, more efficient models, Devstral Small’s intelligence-per-token advantage over GLM 4.7 Flash demonstrated that bigger isn’t always better. Local developers celebrated when GLM-4.7-Flash’s transparent reasoning made 30B-parameter models genuinely useful on consumer hardware.
GLM-5 laughs at those optimizations.
764 billion parameters means even with 4-bit quantization, you’re looking at ~382GB of VRAM just to load the model weights. That’s not a consumer GPU, that’s a data center installation. The 44B active parameters offer some relief, but the MoE routing infrastructure still demands the full model reside in memory.
Developer forums are already splitting into two camps:
The Pragmatists: “I can barely run GLM 4.5 at Q2 quantization. This is a cloud-only model that abandons the local AI movement.”
The Infrastructure Optimists: “This forces the ecosystem to solve large-model serving. We’ll get better parallelism, smarter offloading, and eventually, democratized access.”
The truth is messier. Zhipu AI isn’t abandoning local deployment, they’re segmenting the market. GLM-5 targets API providers and enterprise installations, while GLM 4.7’s benchmark performance versus real-world limitations suggests the smaller models aren’t going anywhere.
The DeepSeek Architecture Gambit
GLM-5’s technical blueprint reveals Zhipu AI’s pragmatic engineering culture. Rather than chasing novel attention mechanisms, they’re adopting battle-tested innovations:
- DeepSeek Attention (DSA): Reduces memory bandwidth requirements for long contexts
- MoE Routing: 44B active parameters out of 764B total (5.8% activation rate)
- Rope Interleaving: Enhanced positional encoding for extended sequences
This architecture addresses GLM-4’s Achilles’ heel: context length collapse. One developer complained that GLM-4 “falls off a cliff around 60k tokens.” DSA is specifically designed to maintain coherence across 128k+ token windows, crucial for codebases and long-document analysis.
The vLLM integration PR shows another strategic move. By ensuring GLM-5 runs efficiently on the most popular open-source inference engine, Zhipu AI guarantees immediate adoption. The commit adds GlmMoeDsaForCausalLM to vLLM’s model registry with a one-line architecture mapping that inherits DeepSeek V3.2’s optimized kernels.
The API Pricing Time Bomb
Here’s the unspoken question: how much will GLM-5 cost? The model’s scale suggests eye-watering inference costs, but Zhipu AI has a history of aggressive pricing. GLM 4.7’s API pricing lie exposed how per-token costs hide real-world inefficiencies, and the company undercut competitors by 4-7x on launch.
If GLM-5 launches at DeepSeek-level pricing, say, $0.50 per million tokens, it would be a nuclear option in the API wars. Western labs relying on subscription models and enterprise contracts would face margin pressure from a model that matches Claude’s quality at a fraction of the cost.
But the 764B parameter count tells a different story. Serving this model requires serious infrastructure, and someone has to pay for those GPUs. The most likely scenario: a tiered launch with premium pricing for full capability, followed by distilled smaller versions months later.
The Multimodal Question
One detail remains conspicuously absent: multimodal support. The current PRs focus exclusively on text-only implementations. When asked directly, developers responded with mixed opinions.
The pragmatists argue pure-text models maintain higher reasoning quality: “multimodality seems to hurt intelligence a fair bit more than its worth.” The integration optimists counter that native multimodal training enhances general intelligence even for text tasks.
Zhipu AI’s silence suggests they’re either:
1. Holding back a separate multimodal variant for a staged reveal
2. Focusing on text mastery before adding vision capabilities
3. Waiting for the political climate to settle around Chinese AI exports
Given the Lunar New Year timing, expect clarity within days, not weeks.
What This Means for AI Development
GLM-5’s shadow launch signals a maturation of the AI ecosystem. We’re moving from research projects to production systems, from announcement hype to deployment reality. For developers, this brings three immediate consequences:
1. Infrastructure Fragmentation: The gap between “local” and “cloud” models becomes a chasm. Your 4090 won’t run GLM-5, but it remains perfect for fine-tuning and prototyping with smaller models.
2. API Dependency Deepens: If you want frontier capabilities, you’re increasingly locked into API providers who can afford the infrastructure. This centralizes power among well-funded labs, contradicting the open-source ethos.
3. Benchmarks Become Performance Art: When models launch without official papers, the community becomes the evaluation framework. Reddit threads and Discord benchmarks replace peer review, creating a parallel knowledge ecosystem.
The stealth deployment model also raises ethical flags. Without official documentation, alignment testing, or safety disclosures, we’re trusting Zhipu AI’s internal protocols. For a model approaching GPT-4 scale, that’s a gamble, one the community is making without explicit consent.
The Bottom Line
GLM-5 isn’t just a new model, it’s a new playbook. Zhipu AI is weaponizing open-source infrastructure, leveraging DeepSeek’s architecture, and testing in production while competitors hold press conferences. The 764 billion parameters are a statement: China isn’t just catching up, it’s redefining the rules of engagement.
For now, the evidence remains circumstantial but overwhelming. GitHub PRs, API artifacts, and benchmark results all point to an imminent launch. The only missing piece is Zhipu AI’s official confirmation, which will likely arrive as a quiet blog post dropped at 2 AM Beijing time, just in time for the Lunar New Year fireworks.
The AI arms race has entered its shadow phase. Keep your eyes on the commit logs.
