GLM 5.2 vs Opus 4.8 vs GPT 5.5: The Real Test
GLM 5.2 vs Opus 4.8 vs GPT 5.5: The Real Test
GLM 5.2 is the strongest open-source model we've ever seen, but in my head-to-head tests it finished a clear step below Opus 4.8 and GPT 5.5 on real coding tasks — and it burned roughly 10x more tokens to get there. I gave all three models the same prompts, in plan mode, and graded the output myself. The hype says open-source finally caught the frontier. The reality is more complicated, and if you're on a subsidized plan the math doesn't work the way you think.
Here's exactly what I found, benchmark by benchmark and task by task.
Is GLM 5.2 Actually Better Than Opus 4.8 and GPT 5.5?
The short answer: no, not when you actually use it. The longer answer is where it gets interesting.
GLM 5.2 dropped this week and the internet immediately went nuts — open-source model, beats the giants on some benchmarks, must be cheaper and just as good, right? That's the story getting passed around. But "better on some benchmarks" is doing a lot of heavy lifting in that sentence, and most people aren't reading the fine print.
The three models I tested: GPT 5.5 running in Codex on extra high, GLM 5.2 running in OpenCode on extra high (routed through OpenRouter), and Opus 4.8 running in Claude Code on high. I picked those effort settings because that's how people actually use these tools day-to-day. Nobody serious is running these on medium.
What Does the Deep Sweep Benchmark Actually Say?
Before the hands-on tests, look at the benchmark everyone's citing. The one worth paying attention to is Deep Sweep — a newer benchmark meant to improve on Terminal Bench and Terminal Bench Pro. It runs 113 long-running agentic tasks across TypeScript, Go, Python, JavaScript, and Rust, each in isolated environments with program-based verifiers.
The chart plots score (percent correct) against average cost per task. You want to be up and to the right — highest score, lowest cost. Here's where the three actually land at their top effort settings:
- GLM 5.2 Max: 44% at $3.92 per task
- Opus 4.8 max: 59% at ~$13 per task
- GPT 5.5 extra high: 67% at $7.23 per task
Yes, GLM is cheaper at the top. But watch what happens when you drop the frontier models to medium effort. Opus 4.8 at medium scores 49% at $3.44 — higher AND cheaper than GLM. GPT 5.5 at medium hits 54% at $2.75, also higher and cheaper. So the "GLM is the efficient choice" story falls apart the moment you compare like-for-like.
Taken at face value, on this benchmark, 4.8 and 5.5 are simply a step above GLM 5.2. Which shouldn't surprise anyone. These are the best closed frontier models on the planet. On long-horizon tasks they pull away.
Is GLM 5.2 Really Cheaper Than the Frontier Models?
This is where the hype breaks down hardest. On a raw per-token basis, GLM is genuinely cheap: $1.40 per million input tokens, $4.40 per million output. Opus 4.8 is about 5.7x more expensive per token, and GPT 5.5 is about 6.8x more expensive.
But per-token cost is the wrong thing to measure. You care about the cost to finish a task, not the price of a token. Opus and GPT 5.5 use dramatically fewer tokens to do the same job — they're more efficient — which is exactly why they come out cheaper per task even at higher token prices.
And the benchmark numbers above are based on raw API pricing. If you're on Anthropic's Max plan, Opus is roughly 10x cheaper than that API cost. Same story on OpenAI's $100 or $200 monthly plans. Once you factor in those subsidized plans — which is how most real users pay — GLM's cost advantage basically evaporates.
One more thing people get wrong: "open source" here does not mean you can run it on your PC. GLM 5.2 is open in the sense that you can see the code and the weights. It is not something you download onto your laptop and fire up in Ollama. This is nearly a trillion-parameter model — it needs a ton of hardware to run. If you're open-source-maxing because you think you'll self-host it for free, that's not the model you're getting.
Test 1: Build a Playable 3D Racing Game in the Browser
Enough benchmarks. For the first real test I gave all three the same deliberately vague prompt: build a playable 3D racing game that runs in the browser, full freedom to pick whatever stack and libraries you want. Vague on purpose — I wanted to see how each model thinks when it isn't handed a roadmap.
Opus 4.8 finished first, in 13 minutes. Low-poly, some sound, smooth movement, even drift mechanics. The grass messed with the physics a bit, and the track was basic and kind of boring — but it worked and it felt clean. It used about 100,000 tokens.
GLM 5.2 took about 5 minutes longer and burned over a million tokens — total spend of $1.21 for 1.35 million tokens on that single run. The result was jumpy: controls way too fast relative to the track, no real differentiation between the track and the field, and in spots I could clip straight through the course. A timer existed, gameplay was janky.
GPT 5.5 was slowest (no surprise, it tends to be) and produced "the Foundry Circuit." Points for flavor, but the wheels spun sideways, the noises were grating, and it was randomly dark for no reason. It used about the same token count as Opus — roughly 7% of its 5-hour window, almost nothing.
First pass ranking: Opus 4.8 clearly ahead, GLM and 5.5 both janky.
The Second Pass: Make It Triple-A
I sent all three back with the same note — do another pass, and make the graphics look like a triple-A game, not low-poly.
Opus 4.8 delivered a big jump. Way better car, real lighting with sun reflecting off the ground, smoother everything, shadowed trees. About 50,000 tokens and 10 minutes for the upgrade.
GLM 5.2 spent another ~1.2 million tokens (total now $1.83) and arguably went backwards — the car looked better but the lighting was so glary and distracting it was a downgrade, and the jumpy controls never got fixed.
GPT 5.5 fixed the sideways wheels, but otherwise served up basically the same thing with a slightly nicer car. It did not hit the triple-A brief.
Game verdict: GLM and 5.5 a clear step below Opus.
Test 2: Build an Award-Style Landing Page for Smart Glasses
Second test: build a fake landing page for AI-powered smart glasses — think Meta Ray-Bans. Full freedom on stack and design, go find real images and product shots instead of hand-rolling HTML, look up landing-page best practices. The key instruction: make it look like an award site, not AI slop — real visual hierarchy, intentional typography, motion where it makes sense.
Opus 4.8 produced a dark page with hand-built glasses, some cut-off text, and scroll-in loading animations. Fine, but it didn't go find real product imagery and it was nowhere near award-level.
GLM 5.2 was a genuine disaster — barely loaded, looked half-thrown-together, no clear intent. About a million tokens for something that looked unfinished. Actually terrible.
GPT 5.5 was the best of the three — a moving banner, a multi-colored cursor, deliberate dead space you could read as a design choice. Still HTML assets rather than real product shots, but the strongest result. Used ~100,000 tokens like Opus.
Landing-page verdict: 5.5 best, Opus okay, GLM a complete failure.
The Second Pass: Rebuild It as an Immersive 3D Experience
I told all three to rebuild the page as an immersive 3D experience using Three.js — an actual interactive 3D scene, full freedom on execution.
Opus 4.8 added moving glasses via Three.js but kept the original problems: cut-off text, overwritten sections, and an obviously-AI feel overall.
GLM 5.2 actually pulled it together this time — a coherent site, a scroll-stopping banner, a layout I'd give the edge over Opus, even if the glasses themselves looked strange and the hero section was weaker than Opus's.
GPT 5.5 won this round. Better overall design, Three.js motion that made sense in context, glasses living naturally in the white space up top. Still recognizably AI-made, but the best top-to-bottom.
Token costs on the second runs were roughly equal to the first runs across all three.
So Which Model Should You Actually Use?
Pulling the benchmark and the hands-on tests together, this landed about where I expected. GLM 5.2 was never grossly worse — but it was never better, and it was always near the bottom while using infinitely more tokens. In test one Opus was best; in test two GPT 5.5 was best; GLM sat at the bottom of both.
That tracks perfectly with the Deep Sweep chart: GLM near the bottom on score and less efficient than 5.5 and 4.8 on cost.
Is GLM 5.2 a great open-source model? Absolutely. It's the strongest open model we've seen. But it runs into the same wall open-source models always hit — they aren't as powerful as the closed frontier — and it's big enough that you're not self-hosting it anyway.
Here's the part that gets lost in the debate: GLM's cost story is already shaky on raw API pricing, and it collapses the moment you remember the enormous subsidy baked into the Anthropic Max plan and the OpenAI Max plan. For the average single user paying for a subsidized plan instead of straight API costs, I don't see an argument for GLM 5.2.
Maybe — maybe — if you're doing lower-level tasks and comparing purely on API prices, it's worth a look. But even then, there's real value in just sticking with one model instead of hopping to whatever benched highest this week. And Sonnet 5 is out next week anyway.
Frequently Asked Questions
Is GLM 5.2 better than Opus 4.8 and GPT 5.5?
Not in practice. GLM 5.2 tops some benchmarks at max effort, but drop Opus 4.8 or GPT 5.5 to medium effort and they score higher for less money. In head-to-head coding tasks — a 3D racing game and an award-style landing page — GLM finished at the bottom both times.
Can I run GLM 5.2 on my own computer?
No. GLM 5.2 is open source in that you can see its code and weights, but it's a nearly trillion-parameter model that needs serious hardware to run. You can't just pull it into Ollama and run it on a personal PC.
Is GLM 5.2 cheaper than the frontier models?
On a per-token basis, yes — about 5.7x cheaper than Opus 4.8 and 6.8x cheaper than GPT 5.5 for output. But it uses far more tokens per task, so the per-task cost advantage mostly disappears. Factor in Anthropic and OpenAI's subsidized Max plans and there's little cost case left for GLM.
What is the Deep Sweep benchmark?
Deep Sweep is a newer agentic-coding benchmark meant to improve on Terminal Bench and Terminal Bench Pro. It runs 113 long-running tasks across TypeScript, Go, Python, JavaScript, and Rust in isolated environments with program-based verifiers, and plots each model's score against its average cost per task.
Which model won the coding tests?
It split. Opus 4.8 won the 3D racing game outright with the smoothest gameplay and biggest quality jump on the second pass. GPT 5.5 won the smart-glasses landing page, especially once Three.js was added. GLM 5.2 was near the bottom in both.
If you want to go deeper into comparing AI coding models and getting the most out of Claude Code, join the free Chase AI community for templates, prompts, and live breakdowns. And if you're serious about building with AI, check out the paid community, Chase AI+, for hands-on guidance on how to make money with AI.
