Comparison of GLM-5.2 and GPT-5.5 performance on SWE-bench Pro, showing GLM-5.2 achieving 62.1 vs 58.6 with 1/6th the cost, h

Editorial illustration for GLM-5.2 beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6) for 1/6 cost

GLM-5.2 beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6)...

GLM-5.2 beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6) for 1/6 cost

By AI Daily Post Edited by Brian Petersen, Editor-in-Chief

June 16, 2026 • 3 min read

Why does this matter? Because an open‑weights model is now posting scores that challenge the big proprietary players. Z.ai’s GLM‑5.2 hit 62.1 on SWE‑bench Pro, edging out OpenAI’s GPT‑5.5, which logged 58.6, while costing roughly one‑sixth as much.

The headline numbers are striking, but the broader picture is equally worth noting. On a suite of industry‑standard third‑party benchmarks, GLM‑5.2 sits above most open‑source flagships and even nudges past DeepSeek v4, a model that has drawn attention for its performance‑to‑cost ratio. It also lands near or above closed‑weights rivals such as GPT‑5.5 and Anthropic’s Claude Opus 4.8.

While the tech is impressive, Z.ai’s “thinking modes” add a practical twist: the “Max” setting pushes the model to its peak, consuming about 85 k output tokens per task; the “High” mode trims performance by only a few points yet slashes token usage by roughly half, a useful lever for latency‑sensitive deployments. The data suggest that open‑weight alternatives are narrowing the gap with commercial giants, especially when cost and efficiency are factored in.

The model particularly shines in agentic tool use and long-horizon software engineering tasks:
SWE-bench Pro: GLM-5.2 scored 62.1, decisively beating GPT-5.5 (58.6) and its own predecessor, GLM-5.1 (58.4).
FrontierSWE (Dominance): Designed to test long-horizon task completion, GLM-5.2 hit 74.4%, surpassing GPT-5.5 (72.6%) and finishing in a near-tie with Claude Opus 4.8 (75.1%).
MCP-Atlas: On this tool-usage evaluation, GLM-5.2 achieved a 77.0, outscoring GPT-5.5 (75.3) and performing just shy of Claude Opus 4.8 (77.8).
Humanity's Last Exam (w/ Tools): When equipped with external tools, GLM-5.2 reached a score of 54.7, coming out ahead of GPT-5.5 (52.2) and tracking closely behind Claude Opus 4.8 (57.9).
PostTrainBench & SWE-Marathon: In extended, multi-hour engineering workloads, GLM-5.2 consistently topped GPT-5.5, scoring 34.3% against GPT-5.5's 25.0% on PostTrainBench, and 13.0% against GPT-5.5's 12.0% on SWE-Marathon.
While GLM-5.2 trails Claude Opus 4.8 and GPT-5.5 slightly on raw Terminal-Bench 2.1 scores (81.0 versus 85.0 and 84.0, respectively), it significantly outscores Google's Gemini 3.1 Pro (74.0).
Beyond traditional coding metrics, GLM-5.2 took an impressive first place on the crowdsourced design task benchmark Design Arena, beating out even the aforementioned state-of-the-art Claude Fable 5 with an ELO score of 1360.

Z.ai’s open-weights GLM-5.2 beats GPT-5.5 on multiple long-horizon coding benchmarks for 1/6th the cost - VentureBeat AI

Why this matters

GLM-5.2’s 62.1 score on SWE‑bench Pro shows an open‑weights model can outpace GPT‑5.5 on a demanding coding benchmark while costing roughly one‑sixth as much. That gap matters for teams watching compute budgets tighten. Yet the result rests on a single third‑party test; we have yet to see how the model behaves across the broader spectrum of real‑world development pipelines.

Its strong showing in agentic tool use and long‑horizon software engineering tasks suggests a step toward more autonomous coding assistants, but the article provides no detail on failure modes or consistency. Compared with DeepSeek v4 and other closed‑weight rivals, GLM‑5.2 appears competitive, though the claim of “near or above” performance leaves room for interpretation. For developers, the lower cost could lower entry barriers, but founders should verify whether the reported gains translate into production stability.

Researchers may find the open‑weights nature useful for reproducibility, yet it remains unclear how the model scales beyond the benchmarks highlighted. We’ll watch how the community validates these numbers before drawing firm conclusions.