I Tested GLM5 Pony Alpha — It's Not What I Expected

I Tested GLM5 Pony Alpha — It's Not What I Expected

Three nights ago, I stumbled onto a model called Pony Alpha on Open Router. No announcement. No hype thread on X. No official blog post. Just a mysterious entry sitting quietly on the programming leaderboard — ranked number ten — with a 200K context window and performance numbers that made me stop scrolling.

I pulled it up, ran my usual battery of coding tests, and within forty-five minutes I was staring at something that shouldn't exist yet. An animated SVG butterfly with photorealistic wing motion. A fully interactive landing page with smooth transitions and dynamic elements. A browser-based operating system with working apps — a paint program, a minesweeper game, a weather widget, all generated from a single prompt.

This wasn't some proof-of-concept demo model. This was something serious hiding behind a silly name.

After spending the better part of three days putting Pony Alpha through its paces — coding challenges, frontend generation, game development, complex simulations — I'm convinced this is actually GLM5, the next foundation model from Zhipu AI. And if I'm right, the open-source AI landscape just got a lot more interesting. But there's a catch that most coverage is glossing over, and I'll get to that in a bit.

Why a "Stealth Model" Matters More Than You Think

Here's the thing about stealth releases in AI — they're not accidents. When a model surfaces on platforms like Open Router, Arena, and Hilo without any marketing push, without even a proper name, that's a deliberate strategy. The team behind it wants real-world performance data from developers who don't know what they're testing. No placebo effect. No hype bias. Just honest benchmarks from people like you and me who throw actual problems at it and see what sticks.

I've been tracking Chinese AI models since the original ChatGLM launch, and Zhipu AI has pulled this move before. Their GLM4 appeared under a different alias on several benchmark platforms weeks before the official announcement. The pattern is unmistakable.

What made me pay attention this time? The numbers didn't add up — in a good way. The model's responses were faster than they should be for something this capable. The code quality rivaled what I get from Claude Opus 4.5 on certain tasks. And that 200K context window wasn't just a marketing number — I actually fed it a 150K-token codebase and it maintained coherent understanding throughout.

If you've been dismissing Chinese AI models as perpetual runners-up, this one might force you to reconsider. The gap is closing faster than most Western developers realize. And the fact that it's free to test right now? That's a window that won't stay open forever.

But before I show you the results, you need to understand what's under the hood — because the architecture explains why this model punches so far above its weight class.

745 Billion Parameters and the Trick That Makes It Practical

Let me throw some numbers at you. Pony Alpha — or GLM5, as I'll call it from here — is estimated at approximately 745 billion total parameters. That's massive. Bigger than DeepSeek V3. Potentially the largest Chinese Mixture of Experts model ever built.

But here's the part that actually matters: only 44 billion parameters are active at any given time.

If you're not familiar with Mixture of Experts (MoE) architecture, think of it like this. Imagine you're running a hospital. You could hire one doctor who knows a little about everything — a general practitioner with broad but shallow knowledge. Or you could hire fifty specialists and route each patient to the two or three experts who are most relevant to their condition. The hospital has fifty doctors on payroll, but any individual patient only sees a few of them.

That's MoE. GLM5 has this enormous pool of specialized "expert" networks, but for any given input, it activates only the subset that's most relevant. The result is a model that has the knowledge breadth of a 745B-parameter giant but the inference speed of something much smaller.

The specific mechanism GLM5 uses is called DC sparse attention — a technique designed specifically for handling extremely long input sequences efficiently. Most transformer models struggle when context windows get large because attention computation scales quadratically. DC sparse attention sidesteps that bottleneck by being selective about which tokens attend to which other tokens. The model learns which connections matter and ignores the rest.

This is why GLM5 can offer a genuine 200K context window without the response times becoming unusable. I tested it with progressively larger inputs — 50K tokens, 100K, 150K — and while latency did increase, it stayed within a range I'd consider practical for development work. Compared to feeding the same long context to some other large models I've used, the difference was noticeable.

The comparison to GLM4.5 is striking — roughly double the total parameters with significantly improved long-context handling. That's not an incremental upgrade. That's a generational leap.

Now, the architecture is impressive on paper. The question is whether it translates to real output quality. So I ran my standard gauntlet of tests — the same ones I use whenever a new model claims to be competitive. What came out the other end surprised me more than once.

The SVG Test — Where Art Meets Code

Whenever I evaluate a coding model, my first test is always SVG generation. Why? Because SVGs sit at the intersection of visual creativity and precise code syntax. The model needs to understand geometry, animation timing, color theory, and XML structure simultaneously. Most models can produce a basic SVG circle. Very few can produce something you'd actually want to look at.

My prompt was simple: "Create an animated butterfly with photorealistic coloring and natural wing motion."

GLM5 produced a butterfly that genuinely caught me off guard. The wing geometry was complex — multiple layered paths creating depth and translucency effects. The animation used CSS keyframes with easing curves that mimicked the slightly irregular rhythm of real butterfly flight. The coloring involved gradient fills with multiple color stops that created a convincing monarch butterfly pattern.

Was it perfect? No. When I compared it side-by-side with what Opus 4.6 generates for the same prompt, the Opus version had slightly more refined path definitions and smoother gradient transitions. But the gap was smaller than I expected. Maybe 85% of the way there — and for a model that's presumably still an early checkpoint, that's remarkable.

I pushed further. A cityscape at sunset. An animated ocean wave. A mechanical watch face with moving gears. GLM5 handled all of them with competent, sometimes impressive results. The watch face in particular showed strong understanding of rotational mechanics — each gear meshed properly with its neighbors and rotated at the correct relative speeds.

The pattern I noticed: GLM5 excels at structural accuracy — the code compiles, the animations work, the proportions make sense. Where it falls slightly short compared to top-tier models is in the artistic refinement — the subtle touches that make an SVG look polished rather than merely correct.

That's a solvable problem with fine-tuning. The foundation is solid.

Here's where it gets really interesting, though. SVGs are a party trick. What I actually care about is whether a model can build things real users would interact with.

Frontend Generation That Made Me Double-Check the Source

My second test is always a full landing page. I gave GLM5 this prompt: "Build a modern, fully interactive landing page for a fictional AI startup called NeuralFlow. Include a hero section with animated background, feature cards with hover effects, a pricing table with toggle between monthly and annual, testimonials carousel, and a contact form with validation."

What came back was — and I'm measuring my words carefully here — production-quality code. The layout was clean, responsive, and well-structured. The animated hero background used a subtle particle system that didn't murder the browser's performance. Feature cards had smooth scale-and-shadow hover transitions. The pricing toggle actually worked, updating all three tier prices with a nice cross-fade animation.

The testimonials carousel auto-rotated with pause-on-hover behavior. The contact form validated email format and required fields with inline error messages that appeared with a gentle slide-down animation.

I opened the developer tools expecting to find a mess of inline styles and spaghetti JavaScript. Instead, I found reasonably organized CSS custom properties, semantic HTML, and JavaScript that used modern patterns. Not flawless — there were a few redundant event listeners and one z-index issue that caused the mobile nav to render behind the hero section. But these are the kinds of bugs you'd find in code written by a competent junior developer, not the kind of structural issues that indicate a model doesn't understand web development.

My next test cranked up the difficulty. "Build a celebrity portfolio landing page — choose any real public figure and create a page that feels like their personal brand." GLM5 chose autonomously, styled everything from scratch, added multiple sections with different visual treatments, included smooth scroll behavior, and wired up interactive elements. The design choices were cohesive and opinionated in a way that felt intentional rather than random.

I've spent enough time reviewing AI-generated frontends to know what to look for. The telltale signs of a model that's memorized component patterns versus one that actually understands layout principles. GLM5 showed genuine understanding. When I asked it to move the testimonials above the pricing section, it didn't just cut-and-paste — it adjusted the visual flow, updated the scroll-based animations to trigger at the new positions, and modified the color transition between sections to maintain visual coherence.

That level of contextual awareness during modification is what separates a good coding model from a great one. And this is supposed to be the pre-release version.

But I still had my biggest test ahead — one that breaks most models.

Building an Entire OS in the Browser — From a Single Prompt

Here's the prompt that usually separates contenders from pretenders: "Build a browser-based operating system with a working desktop, taskbar, and at least five functional applications. Include a web browser, weather app, game, paint application, and system monitor. Make it look like a blend of macOS and Windows."

I call this my "stress test from hell." A model needs to coordinate windowing systems, state management across independent applications, UI rendering for completely different app types, and maintain visual consistency — all in a single generation pass.

GLM5 produced what it called "Pony OS." And honestly? I was impressed.

The desktop rendered with a clean wallpaper, dock-style taskbar at the bottom, and a top menu bar with a clock. Clicking app icons opened draggable, resizable windows with proper z-index management — clicking a background window brought it forward. The minimize and close buttons worked.

Let me walk through each app:

The Web Browser — had an address bar, navigation buttons, and rendered a default homepage. It couldn't actually fetch real web content (that would require server-side proxying), but the UI was complete and the navigation state management was correct.

Weather App — displayed a fictional five-day forecast with temperature graphs and weather icons. The data was hardcoded, but the UI was polished with proper layout and responsive card design.

Minesweeper — fully playable. Right-click to flag, left-click to reveal, proper flood-fill algorithm for empty cells, mine counter, and a timer. I actually played three rounds. Won two.

Paint App — canvas-based drawing with color picker, brush size slider, eraser tool, and a clear button. The drawing was smooth with proper mouse event handling. Not Photoshop, but genuinely functional.

System Monitor — displayed animated CPU and memory usage graphs with randomized data. The graphs updated in real-time with smooth line rendering.

Did everything work flawlessly? No. The dark mode toggle in the settings panel didn't actually change the theme — it toggled a class that wasn't connected to the CSS variables. Some windows could be dragged outside the viewport with no boundary detection. The paint app's eraser left artifacts at the edges of strokes.

But take a step back and consider what actually happened here. A single model, in a single generation pass, produced a coordinated multi-application desktop environment with working games, drawing tools, and system utilities. The HTML, CSS, and JavaScript came out as one coherent package that ran in the browser without modification.

Most models I've tested either refuse this prompt entirely, produce something that looks like a desktop but where nothing actually functions, or generate apps that work independently but can't coexist in the same windowing system. GLM5 nailed the hard part — the coordination — while leaving some polish on the table.

If you've made it this far, you already have a good sense of GLM5's coding strengths. But I saved the most ambitious test for last — and it's the one that revealed both the model's ceiling and its most interesting limitation.

A Minecraft Clone and a Galaxy — Pushing the 3D Boundary

Two more tests. First: "Build a Minecraft-like voxel game using Three.js with block breaking, block placing, and procedural terrain generation with height-based biome coloring."

GLM5's first attempt produced a working 3D world. You could move with WASD, look around with the mouse, and the terrain had visible chunk boundaries with different height levels. Block breaking worked — click a block, it disappears. Block placing worked — right-click to place a block adjacent to the one you're looking at.

The terrain generation used Perlin noise for height mapping, and the biome coloring created a gradient from sand-colored lowlands to green midlands to grey-white peaks. The chunk-loading system rendered terrain around the player and culled distant chunks for performance.

Issues? The first version had z-fighting on some block faces where adjacent chunks met. The lighting was flat — no ambient occlusion or shadow effects. And the block-placing raycast occasionally attached blocks to the wrong face when clicking at steep angles.

I asked for a version 2. GLM5 improved the visuals noticeably — better texture-like coloring on block faces, fixed the chunk boundary artifacts, and added a basic sky gradient. The raycast issue persisted, and it introduced a new problem where occasionally two blocks would place simultaneously. Solid improvement, but not a complete fix.

My honest assessment: the 3D output is where GLM5 shows its age compared to top-tier models. The code is architecturally sound — the chunk system, the raycasting, the noise-based generation are all implemented correctly. But the edge cases and visual polish that make a 3D application feel finished aren't there yet. For prototyping and proof-of-concept work, it's excellent. For shipping, you'd need meaningful human refinement.

The solar system simulation, on the other hand, was a genuine bright spot. The prompt was straightforward — "simulate the solar system with accurate relative sizes, orbital periods, and visual styling" — and GLM5 produced a beautiful Three.js scene with all eight planets orbiting at correct relative speeds, textured spheres, orbital trail lines, and a glowing sun with a point light that cast illumination on the planet surfaces. The Saturn rings were a nice touch I didn't explicitly request.

Fast, accurate, and visually compelling. This is where the model's strength in combining mathematical precision with visual output really shines.

Alright — I've shown you the wins and the stumbles. Now for the part most reviewers skip.

The Honest Conversation Nobody's Having About GLM5

I need to address something that keeps coming up whenever Chinese AI models surface, because ignoring it would be dishonest.

There are persistent rumors — and at this point, more than rumors — that models like GLM5 are trained on synthetic data generated by American AI companies. Outputs from Anthropic's Claude, Google's Gemini, and OpenAI's GPT models reportedly form part of the training mixture. This is a known practice across the Chinese AI industry, and Zhipu AI is not the only company doing it.

What does this mean practically? For most developers evaluating whether to use GLM5 for their projects, probably not much. You care about output quality, speed, cost, and reliability. The training data provenance is an ethical and legal question that's above any individual developer's pay grade.

But I think it's worth being transparent about. When I say GLM5's code output quality "rivals Opus 4.5," there's a reason for that. The model has likely seen — and learned from — enormous volumes of high-quality outputs from exactly those models. That's not necessarily a criticism. All models learn from existing text. But the specific dynamic of Chinese labs training on American model outputs creates a complicated landscape that the industry hasn't fully reckoned with.

My personal take? I evaluate models on their output, not their origin story. If GLM5 generates better code for my use case than the alternative, I'll use it. But I go in with eyes open about what the model is and how it got there.

The other thing I want to be honest about: the "Pony Alpha" name and stealth release suggest this is still an early checkpoint, not the final model. Some of the rough edges I encountered — the dark mode toggle that doesn't work, the raycast bugs in the Minecraft clone, occasional verbosity in code comments — these might get cleaned up before the official GLM5 launch. Or they might not. Betting on future improvements is a game I've lost before.

One more thought that might be unpopular. There's another stealth model floating around — Aurora Alpha — that performs less impressively and appears to mimic GPT-style outputs. If the leaks are accurate and this is connected to OpenAI's ecosystem in some way, then we're looking at an entire shadow ecosystem of AI models being tested anonymously on public platforms. That's either exciting or concerning depending on your perspective. For me, it's mostly fascinating.

What I care about most is what you can actually do with this model today. So here's how to get your hands on it.

Getting Started with GLM5 Pony Alpha — A Practical Guide

Right now, you have multiple paths to try GLM5 for free. Here's the quickest way to go from zero to running prompts:

1. Open Router (Easiest Path)

Head to Open Router and search for "Pony Alpha" in the model list. You can use it directly through the Open Router playground without any setup. The model appears under its stealth name — don't look for "GLM5" as it's not listed under that identifier yet.

# If you're using the Open Router API:
curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "pony-alpha",
    "messages": [{"role": "user", "content": "Your prompt here"}],
    "max_tokens": 4096
  }'

Pro tip: Set max_tokens higher for coding tasks — 8192 or even 16384 if your prompt requires generating complete applications. GLM5 tends to produce thorough, complete implementations rather than abbreviated snippets, so giving it room to work pays off.

2. Arena (For Blind Comparison)

LMSys Arena lets you test GLM5 in blind A/B comparisons against other models. This is actually how I first noticed it — I kept picking the same "mystery model" as the winner in coding comparisons, and when I checked the reveal, it was Pony Alpha every time.

3. API Access via Kilo

For programmatic access with higher rate limits, Kilo provides API endpoints. The setup process is similar to any OpenAI-compatible API — swap the base URL and API key, keep the same message format.

4. Testing Recommendations

Based on my experience, here's where to start:

• Frontend generation — this is GLM5's sweet spot. Give it complex UI descriptions and be specific about interactions and animations. The results will surprise you.
• Code refactoring — feed it a messy function and ask for a clean rewrite. The 200K context window means you can include entire files for context.
• SVG and visual code — great for generating illustrations, diagrams, and animated graphics programmatically.
• Long-context analysis — throw your entire codebase at it and ask questions. The DC sparse attention mechanism handles this genuinely well.

What to avoid for now: Tasks requiring extremely precise numerical computation, real-time data access (it's an LLM, not a search engine), and anything requiring multi-step agentic behavior with tool use — the model is strong at single-pass generation but hasn't shown the same level of polish in multi-turn agentic workflows.

5. Common Pitfalls

One thing I noticed: GLM5 responds better to detailed, specific prompts than to vague ones. "Build a landing page" gets you something generic. "Build a landing page for an AI startup with a particle animation hero, three feature cards with hover lift effects, a pricing table that toggles between monthly and annual with animated price changes, and a dark color scheme using navy and electric blue" gets you something worth shipping.

Also — and this tripped me up initially — the model sometimes generates code with Chinese variable names or comments when it detects ambiguity in the prompt language. Adding "use English for all code, comments, and variable names" to your system prompt eliminates this entirely.

What This Means for the Open-Source AI Race

Step back from the individual test results for a moment and look at the bigger picture.

A year ago, the gap between the best proprietary models and the best open-source alternatives was enormous. You'd use GPT-4 or Claude for serious work and open-source models for experimentation and cost optimization. That calculus is shifting underneath us.

GLM5 — if the parameter estimates are accurate — represents a new class of open-source model. Seven hundred forty-five billion parameters. Not a fine-tuned version of someone else's foundation. Not a small model punching above its weight through clever training. A genuinely massive model with architectural innovations that make it practical to run and a context window that competes with the best proprietary offerings.

Here's what I expect to happen. The official GLM5 release — likely this month given the leaks and GitHub sightings — will come with proper documentation, fine-tuned variants, and quantized versions optimized for consumer hardware. The community will immediately start building on top of it. Within weeks, we'll see GLM5-based coding assistants, chatbots, and specialized tools proliferating across GitHub.

The model's MoE architecture with only 44B active parameters means it might actually be runnable on high-end consumer GPUs with aggressive quantization. That's the real story here. Not that a Chinese lab made a big model — that's been happening for years. The story is that a 745B-parameter model might be accessible to individual developers and small teams.

Three metrics to watch after the official launch: inference speed on consumer hardware, community fine-tune quality at the 30-day mark, and whether the 200K context window holds up under diverse real-world workloads (not just the coding tasks I tested).

I got into this rabbit hole three days ago expecting to spend twenty minutes testing another forgettable model with a weird name. I'm writing four thousand words about it instead, which tells you everything about how those twenty minutes actually went.

GLM5 Pony Alpha isn't the best coding model available right now — Opus 4.6 still holds that crown in my testing, and the latest Claude models have a sophistication in code architecture that GLM5 hasn't matched yet. But it might be the best open-source coding model I've ever used, and the fact that it's a pre-release checkpoint makes the trajectory genuinely exciting.

The question I keep coming back to: if this is the early version, what does the polished release look like?

Here's my challenge to you. Go to Open Router tonight. Pull up Pony Alpha. Give it the hardest single-prompt coding challenge you can think of — the one you've been using as your personal litmus test for AI models. Run it. Look at the output. Then come back and tell me what you found.

Because the best evaluations don't come from reviewers like me running standardized tests. They come from thousands of developers throwing their actual problems at a model and seeing what breaks. GLM5 is free. The window is open. The only cost is your curiosity.

And honestly? That butterfly animation alone was worth the three days.

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