GLM 5.2 Benchmarks: How It Really Performs

I used to scroll right past benchmark charts. Every model launch ships with a wall of bar graphs where the new model conveniently wins, so I'd learned to ignore them. Then GLM 5.2 landed, my whole feed started shouting that it was "beating GPT-5.5," and I had to know whether that was real or just launch-day noise. So I pulled the actual numbers—from Z.AI's official release, third-party leaderboards, and independent reviews—and lined them up side by side.

Here's the honest read: GLM 5.2 is the strongest open-weight model on coding benchmarks right now. It beats GPT-5.5 on most of them, lands within a point or two of Claude Opus 4.8 on long-horizon coding, and does it at roughly a fraction of the price. Below is what each benchmark actually measures, where GLM 5.2 wins, where it still loses, and what that means for the work you'd actually hand it.

GLM 5.2 Benchmarks at a Glance

If you read one table, read this one. These are the agentic-coding suites everyone is watching, with the headline scores reported at launch:

Numbers reflect the official Z.AI release and third-party leaderboards as of June 2026. Benchmarks move weekly and methodologies differ—verify the current figures on each vendor's page before you quote them.

The pattern jumps out immediately: GLM 5.2 sits above GPT-5.5 and just below Opus 4.8 on the suites that matter for real coding—while being open-weight and far cheaper. Now let's break down what's behind each row.

Coding Benchmarks: Beating GPT-5.5, Chasing Opus

The two scores people quote most are SWE-bench Pro (can the model resolve real GitHub issues?) and Terminal-Bench 2.1 (can it work a real shell to get a job done?).

• SWE-bench Pro: 62.1. That edges past GPT-5.5 (58.6) and, more tellingly, jumps its own predecessor GLM 5.1 (58.4). Opus 4.8 still leads the raw number here, but GLM 5.2 is the first open model in striking range.
• Terminal-Bench 2.1: 81.0. This is the result that actually surprised me. GLM 5.1 scored around 62 on the same test; 5.2 leaps to 81.0, landing a few points behind Opus 4.8 (85.0) and GPT-5.5 (84.0). A near-20-point generational jump on agentic terminal work is the single biggest story in these numbers.

The takeaway for everyday coding: on the kind of "resolve this issue, run the tests, fix the shell command" work that fills a real sprint, GLM 5.2 is now playing in the same league as the closed frontier models.

Long-Horizon Benchmarks: Where the Gap Shows

This is the honest part most launch posts gloss over. The longer and harder the task, the more the closed frontier still pulls ahead—and the GLM 5.2 numbers show it.

On FrontierSWE, realistic long-horizon coding, GLM 5.2 (74.4) finishes in a near-tie with Opus 4.8 (75.1) and clears GPT-5.5—genuinely impressive. On PostTrainBench it holds second, again ahead of GPT-5.5. But look at SWE-Marathon, the multi-hour engineering grind: Opus 4.8 (26.0) is roughly double GLM 5.2 (13.0). That's the "hard 10%" showing up in the data—when a task stretches across hours and dozens of steps, the premium closed reasoner still has a real edge.

I saw the same thing when I tested both models head-to-head on 40 real pull requests: GLM 5.2 matched Opus on the everyday work and only fell behind on the gnarliest, longest problems. If you want that breakdown, I wrote it up here: GLM 5.2 vs Claude Opus 4.8: Coding, Compared.

Tool Use, Agents & Reasoning

Coding isn't the whole story—agent workflows live or die on tool calling, and some tasks need raw reasoning.

• MCP-Atlas: 76.8. On this tool-usage eval GLM 5.2 outscores GPT-5.5 (75.3) and sits a hair under Opus 4.8 (77.8). For agent loops, reliable function calling matters more than a leaderboard point, and this is close enough to call even.
• Tool-Decathlon: 48.2. Here's the other honest miss. On this harder, broader tool benchmark, Opus 4.8 (59.9) and GPT-5.5 (55.6) both pull clearly ahead. Complex multi-tool orchestration is still a weak spot.
• Reasoning: On AIME 2026 (competition math) GLM 5.2 posts 99.2, nudging past GPT-5.5 (98.3). On GPQA-Diamond (graduate-level science) it scores 91.2, trailing the 93.6 that both Opus and GPT-5.5 hit. Translation: it's excellent at structured math, a step behind on the very hardest knowledge questions.

The Open-Weight Crown

Zoom out from individual tests and one fact stands: GLM 5.2 is the leading open-weight model on the independent Artificial Analysis Intelligence Index (51 on v4.1), ahead of other open models like MiniMax-M3, DeepSeek V4 Pro, and Kimi K2.6. It also took the top spot in Design Arena's code categories and ranks among the top handful of all models—open or closed—on aggregate leaderboards like BenchLM.

No other model you can download and self-host is this close to the closed frontier. That's the headline these benchmarks are really telling.

The Cost Angle: Same League, A Fraction of the Price

Benchmarks don't run on a budget, but your team does—and this is where GLM 5.2 stops being interesting and starts being a decision. Its API input price lands around $1.40 per million tokens versus roughly $5.00 for both Opus 4.8 and GPT-5.5. VentureBeat pegged the all-in gap, blending input and output, at about one-sixth the cost of GPT-5.5.

Put that next to the scores: you're getting performance within a point or two of the frontier on most coding work, for somewhere between a third and a sixth of the price. That's the ratio that makes the open-weight crown more than a trophy.

What These Benchmarks Actually Mean for Your Work

Scores are abstractions. Here's how I'd translate them into a routing decision:

1. Everyday coding (the 90%) — issues, refactors, tests, glue code, terminal work. GLM 5.2's SWE-bench Pro, Terminal-Bench, and FrontierSWE numbers say it'll keep pace with the frontier. Default to it.
2. Marathon tasks (the hard 10%) — multi-hour, many-step engineering where SWE-Marathon and Tool-Decathlon expose the gap. Keep a premium closed model on standby for these.
3. Cost-sensitive or high-volume pipelines — the price ratio makes GLM 5.2 the obvious default, escalating only the rare hard case.

The one thing no benchmark captures is how a model feels on your code. A score is an average over someone else's test set; your repo, your prompts, and your edge cases are what you actually ship.

The Fastest Way to Test GLM 5.2 Yourself

Reading a leaderboard is one thing—watching a model handle your own task is another. The catch with an open-weight model is that the "proper" way to run it usually means downloading weights or wiring up an API key, and most people stall right there.

You can skip all of it. glm5.app lets you chat with GLM 5.2 straight in your browser—no install, no key, no setup. Paste a real ticket from your backlog, watch how it codes and plans, and judge the everyday-coding quality for yourself instead of trusting a chart.

If you want to feel where GLM 5.2 lands relative to the frontier, that's the fastest path: try GLM 5.2 free at glm5.app and let your own task decide.

Frequently Asked Questions

Is GLM 5.2 better than GPT-5.5 on benchmarks? On most coding and long-horizon suites, yes—it leads GPT-5.5 on SWE-bench Pro (62.1 vs 58.6), FrontierSWE (74.4 vs 72.6), and MCP-Atlas, while costing far less.

Is GLM 5.2 better than Claude Opus 4.8? Not on raw scores. Opus 4.8 still edges ahead on most benchmarks and pulls clearly away on the hardest multi-hour tasks (SWE-Marathon, Tool-Decathlon). GLM 5.2 is close enough that for everyday coding the gap rarely shows—at a fraction of the price.

What's GLM 5.2's SWE-bench Pro score? 62.1, the first open-weight model to genuinely close in on the closed frontier, and a clear jump over GLM 5.1's 58.4.

Are these GLM 5.2 benchmark numbers reliable? They come from Z.AI's official release and independent leaderboards, but benchmarks move fast and methods vary. Treat them as a snapshot and verify current figures on each vendor's page.

Where does GLM 5.2 rank among open models? First. It tops the Artificial Analysis Intelligence Index for open-weight models and leads Design Arena's code categories.

How can I test GLM 5.2 without any setup? Chat with it free in your browser at glm5.app—no API key, no install, nothing to download.

The Bottom Line

So how does GLM 5.2 really perform? It's the open-weight model that finally closed the gap: ahead of GPT-5.5 on most coding benchmarks, within a point or two of Claude Opus 4.8 on long-horizon work, and only clearly behind on the very hardest multi-hour tasks—all at a fraction of the cost. For the work that fills most developers' days, the scores say it's a frontier-class default you can also run your own way.

But a benchmark is an average over someone else's tasks. The only score that matters is how it handles yours—so run your own prompt through it, no keys, no setup, right here: try GLM 5.2 free on glm5.app.