GLM 5.2 Benchmark: Every Score Explained and What It Means for Your Workflow

GLM 5.2 is the strongest open-weight coding model released in 2026 so far, and its benchmark numbers are a large part of why. Scores like 62.1 on SWE-bench Pro, 81.0 on Terminal-Bench 2.1, and 74.4 on FrontierSWE put it within striking distance of closed frontier models from Anthropic and OpenAI, at roughly one-sixth the API cost. But raw numbers on a leaderboard can mislead without context. Each benchmark measures a different capability, carries its own caveats around contamination and harness setup, and says something different about what the model can and cannot do in practice.

This guide breaks down every major GLM 5.2 benchmark score, explains what each test actually measures, compares it head-to-head with Claude Opus 4.8 and GPT-5.5, and identifies the specific workflows where the model excels or falls short.

What is GLM 5.2?

GLM 5.2 is Z.ai's flagship language model, built for long-horizon coding and agentic tasks. Z.ai (the international brand of Zhipu AI, a 2019 Tsinghua University spinoff) released it on June 13, 2026 via its GLM Coding Plan, with open weights following on June 16 under a pure MIT license on Hugging Face.

A few technical details that matter for understanding the benchmarks:

• Parameters: Approximately 753 billion (some sources report 744B), Mixture-of-Experts (MoE) architecture
• Context window: 1,000,000 tokens (5x the 200K limit of its predecessor GLM-5.1)
• Output limit: 131,072 tokens (128K) per response
• Thinking effort levels: High and Max, letting users balance performance against latency
• Architecture innovation: IndexShare, which reuses a lightweight indexer across every four sparse-attention layers, cutting per-token FLOPs by 2.9x at 1M context

The company went public on the Hong Kong Stock Exchange on January 8, 2026 (stock code 2513.HK), raising approximately HK$4.3 billion (about $558 million) in what it called the world's first IPO by a large language model company, as reported by CNBC. Z.ai has raised roughly $1.5 billion since founding, backed by Alibaba, Tencent, Ant Group, Meituan, and Saudi Aramco's Prosperity7 Ventures.

GLM 5.2 benchmark scores at a glance

The table below consolidates GLM 5.2's benchmark scores alongside its closest competitors. All scores are vendor-reported from Z.ai's official benchmark table published on June 17, 2026, except where noted. Independent verification status varies by benchmark.

GLM 5.2 benchmark comparison with frontier models. All scores are vendor-reported. Asterisked HLE scores are from the full exam set; GLM 5.2 HLE scores are text-only subset. Pricing as of July 2026.

Important caveat: Scores marked with an asterisk (*) are from the full HLE exam set, while Z.ai reports GLM 5.2's HLE scores on the text-only subset. This difference in evaluation scope means HLE comparisons between GLM 5.2 and the asterisked models are not strictly apples-to-apples. All other cross-vendor benchmark comparisons carry inherent variability because of differences in harness setup, prompt formatting, and evaluation conditions. Independent benchmarking organizations like Artificial Analysis, Scale AI, and BenchLM provide third-party validation, though even their methodologies differ.

1. Coding benchmarks: where GLM 5.2 shines

Coding is GLM 5.2's primary selling point, and the numbers back it up. Three benchmarks tell the core story.

SWE-bench Pro: 62.1

SWE-bench Pro is Scale AI's contamination-resistant coding benchmark. It draws 1,865 real-world software tasks from 41 professional GitHub repositories across Python, Go, TypeScript, and JavaScript, scored on a Pass@1 basis. Pro is significantly harder than SWE-bench Verified: frontier models that clear 80 to 95% on Verified typically solve only around 59% of Pro tasks under standardized scaffolding.

GLM 5.2 scores 62.1, up from 58.4 for GLM-5.1. That 3.7-point gain is significant on a benchmark where even top closed models rarely move more than a few points between generations. It outperforms GPT-5.5 (58.6) and trails Claude Opus 4.8 (69.2 on the llm-stats vendor aggregate) by seven points.

What it means practically: GLM 5.2 can resolve real software bugs and implement features across production codebases. The seven-point gap to Opus 4.8 matters if your workflow depends on first-attempt success rates, but for most iterative development, the difference is less pronounced.

Terminal-Bench 2.1: 81.0

Terminal-Bench measures autonomous terminal-based coding, where the model writes, executes, and debugs code in a real shell environment. GLM 5.2's 81.0 represents a 17.5-point jump from GLM-5.1's 63.5, one of the largest single-generation gains seen on this benchmark.

It lands within four points of Claude Opus 4.8 (85.0) and three points behind GPT-5.5 (84.0), while pulling significantly ahead of Gemini 3.1 Pro (74.0).

What it means practically: If you're using AI coding assistants that work in a terminal environment (Claude Code, Cline, or similar tools), GLM 5.2 is competitive with closed frontier models at a fraction of the cost.

FrontierSWE: 74.4

FrontierSWE tests long-horizon task completion at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. This is the benchmark class that matters most for autonomous agent workflows.

GLM 5.2 hits 74.4, trailing Opus 4.8 (75.1) by just 0.7 percentage points and beating GPT-5.5 (72.6) by 1.8 points. Per Z.ai's official benchmarks, this makes GLM 5.2 the highest-ranked open-weight model on FrontierSWE.

What it means practically: For extended engineering sessions where the model needs to maintain context across hours of work, GLM 5.2 performs at near-frontier levels. All three leading frontier models (Opus 4.8, GPT-5.5, and GLM 5.2) now offer 1M-token context windows, so context length alone is no longer a differentiator. The advantage GLM 5.2 holds at this context scale is price: running long-context coding sessions costs a fraction of what the closed alternatives charge.

2. Long-horizon agentic benchmarks

These benchmarks test the model's ability to sustain quality over extended, multi-step engineering tasks, which is exactly the use case GLM 5.2 was built for.

PostTrainBench: 34.3

PostTrainBench gives each agent an H100 GPU and evaluates how much it can improve a smaller model through post-training techniques like fine-tuning, RLHF, and DPO. GLM 5.2 scores 34.3, outperforming GPT-5.5 (28.4) and trailing Claude Opus 4.8 (37.2).

SWE-Marathon: 13.0

SWE-Marathon covers ultra-long-horizon engineering tasks: building compilers, optimizing kernels, developing production-grade services. At 13.0, GLM 5.2 outperforms GPT-5.5 (12.0) but trails Claude Opus 4.8 (26.0) by a wide margin. This is the benchmark where the gap between the open-weight leader and the closed-source frontier is clearest.

MCP-Atlas: 76.8

MCP-Atlas (Public Set) evaluates tool and API usage accuracy. GLM 5.2 scores 76.8, within a point of Opus 4.8 (77.8) and ahead of GPT-5.5 (75.3). For workflows that require calling external tools reliably, GLM 5.2 holds its own against the best.

Where it falls short: Tool-Decathlon, a harder multi-tool benchmark, exposes a real gap. GLM 5.2 falls well behind both Opus 4.8 and GPT-5.5 on this test, as noted by BitsMinds. If your workload involves long, tool-heavy agent chains with diverse API integrations, the closed-source leaders still have a measurable edge.

3. Reasoning and math benchmarks

GLM 5.2 is not just a coding model. Its reasoning scores confirm it competes across the full spectrum.

AIME 2026: 99.2

AIME is competition-level math for strong high schoolers. A 99.2 is effectively a ceiling score. It tells you the model has no weaknesses in mathematical reasoning, but the test no longer meaningfully separates frontier models. Claude Opus 4.8 scores 95.7, GPT-5.5 scores 98.3, and GLM 5.2 sits at the top.

GPQA-Diamond: 91.2

GPQA-Diamond is the hardest slice of a graduate-level science Q&A set, filtered so that non-experts cannot brute-force answers even with web access. GLM 5.2 scores 91.2, trailing both Claude Opus 4.8 and GPT-5.5 (each at 93.6) by 2.4 points but still firmly in frontier territory on technical reasoning.

Humanity's Last Exam (with tools): 54.7

HLE is a deliberately difficult exam spanning expert-level questions across many fields. The "with tools" setting lets the model search and compute rather than answer cold. GLM 5.2's 54.7 edges out GPT-5.5 (52.2) and tracks behind Opus 4.8 (57.9). On a benchmark this difficult, anything in the 50s is a serious result.

GLM 5.2 reasoning benchmark comparison. All scores vendor-reported. HLE scores for Opus/GPT are full set; GLM 5.2 is text-only subset.

The takeaway: GLM 5.2 is not a narrow coding specialist. It competes on reasoning and science benchmarks that have nothing to do with code, which matters for agentic workflows where the model needs to reason about domain problems before writing solutions.

4. Independent validation: arena rankings and third-party indexes

Vendor-reported benchmarks only tell part of the story. Several independent rankings and third-party evaluations add useful context.

Design Arena Code Categories: #1 overall

According to Design Arena's Code Categories leaderboard, which uses Elo-style human preference comparisons rather than synthetic scoring, GLM 5.2 ranks #1 overall, sitting 10 Elo points ahead of Claude Fable 5, as reported by Medium. This is notable because arena rankings are significantly harder to game than automated pass-rate benchmarks. Users prefer GLM 5.2's coding solutions in head-to-head comparisons more frequently than any other model.

Artificial Analysis Intelligence Index: 51

On the Artificial Analysis Intelligence Index, GLM 5.2 scores 51, the highest of any open-weight model. For context, Claude Opus 4.8 scores 56, GPT-5.5 scores 53, and Gemini 3.5 scores 50. The open-weight leader is inside the closed-frontier pack, not a tier below it.

GDPval-AA v2: 1524

On Artificial Analysis's GDPval benchmark, GLM 5.2 scores 1524, ahead of MiniMax-M3 (1418) and DeepSeek V4 Pro (1328), and in line with GPT-5.5 at its highest reasoning effort level.

Semgrep IDOR detection: 39% F1 (independent)

Semgrep, a cybersecurity company, ran GLM 5.2 against its proprietary IDOR vulnerability detection benchmark using the same prompt it uses to evaluate frontier coding agents. GLM 5.2 scored a 39% F1, beating Claude Code (32%) at roughly $0.17 per vulnerability found. This result carries weight because Semgrep had no commercial relationship with Z.ai and ran the test independently on its own infrastructure.

5. GLM 5.2 benchmark vs. GLM 5.1: what improved

The generational leap from GLM 5.1 to GLM 5.2 is one of the largest in recent open-weight model history.

GLM 5.2 vs. GLM 5.1 benchmark improvement

The FrontierSWE leap is the most dramatic: from 30.5 to 74.4, a 43.9-point gain that largely reflects the 1M context window enabling long-horizon tasks GLM-5.1 could not sustain. Terminal-Bench tells a tighter story about raw coding improvement: a 17.5-point jump from 63.5 to 81.0 on the same benchmark format, suggesting Z.ai's agentic RL training paid off in measurable ways.

Three technical changes drove these improvements. IndexShare reduced per-token computation costs by 2.9x at 1M context, making it practical to sustain quality across very long sessions. The improved MTP layer for speculative decoding increased acceptance length by up to 20%, speeding up inference. And the 5x context window expansion (200K to 1M tokens) gave the model structural room to handle entire codebases in a single pass.

6. How to read GLM 5.2 benchmarks critically

Benchmark numbers are useful, but they need context. Here is what to watch for when evaluating GLM 5.2 (or any model) based on published scores.

Vendor-reported vs. independently verified

Z.ai published its benchmark blog on June 17, 2026, four days after the model's initial release, with the full benchmark suite on GitHub following by June 19. The delay is actually a positive signal, since scores published on launch day carry higher cherry-picking risk. Still, these are vendor-reported numbers. Independent organizations like Scale AI, Artificial Analysis, and BenchLM use their own harness configurations, and scores can shift depending on prompt formatting, temperature settings, and evaluation conditions.

Groundy's analysis of the GLM 5.2 benchmarks highlights that each score on the benchmark card measures something distinct, with different contamination exposure and different relevance to real work. The SWE-bench Pro evaluation, for example, was run using OpenHands with specific temperature and context settings that other evaluators may not replicate exactly.

Ceiling effects

Some benchmarks have effectively saturated at the frontier level. AIME 2026 (99.2) tells you GLM 5.2 has no math weaknesses, but it does not differentiate it from other top models. Multiple frontier models score above 95 on AIME, which means the test no longer separates them meaningfully.

Benchmark-to-production gaps

LayerLens demonstrated with GLM-5 (the predecessor family) that re-running benchmarks 24 days apart on the same model produced shifts in both directions, including a 12-point regression on one test and a nearly seven-point improvement on another. The takeaway: benchmark scores are point-in-time snapshots, not guarantees. The model you approve based on initial numbers may perform differently under your specific conditions.

What benchmarks cannot tell you

No benchmark measures prompt fidelity at 1M context, reliability of output formatting over 50+ tool calls in a single session, or how well a model recovers from cascading errors in a multi-hour engineering workflow. These are the characteristics that matter most for production use, and they require hands-on testing with your actual workload.

7. GLM 5.2 pricing and access

GLM 5.2 is available through Z.ai's official API, multiple third-party inference providers, and self-hosted deployment.

API pricing (as of July 2026)

Per Z.ai's official pricing page:

GLM 5.2 API pricing. Pricing as of July 2026.

Third-party providers like DeepInfra, OpenRouter, and Together.ai offer slightly different pricing. DeepInfra lists GLM 5.2 at approximately $0.95 per million input tokens and $3.00 per million output tokens.

Cost comparison with closed models

GLM 5.2's pricing advantage is substantial. Input tokens are roughly 3.6x cheaper than Claude Opus 4.8 and GPT-5.5. Output tokens are approximately 5.7x cheaper than Opus 4.8 and 6.8x cheaper than GPT-5.5, according to CryptoBriefing (which estimates 80 to 90% savings overall) and third-party pricing analysis.

For a team processing 10 million tokens per month with a 50/50 input-output split, GLM 5.2 costs about $29 per month via the Z.ai API. The same workload on GPT-5.5 runs approximately $175 per month.

GLM Coding Plan subscriptions

Z.ai also offers subscription-based access through the GLM Coding Plan, which meters usage in prompts per cycle rather than tokens:

• Lite: approximately $3 to $6 per month (launch/promo range)
• Pro: approximately $15 (promo) to $72 (standard monthly)
• Max: approximately $30 (promo) to $160 (standard monthly)
• Team: custom pricing for organizations

Note: GLM 5.2 consumes quota roughly 3x faster than older models on the plan during peak hours, so effective capacity depends on which model you're running.

Self-hosting

Because GLM 5.2 is released under the MIT license, you can download the full weights from Hugging Face (zai-org/GLM-5.2) and run the model entirely on your own infrastructure. The practical barrier is hardware: the approximately 753B MoE parameters weigh roughly 1.5TB at BF16 precision. Production self-hosting typically uses FP8 quantization (~744GB), which fits on an eight-GPU H200 node with 1.13TB aggregate VRAM. Consumer-grade setups (a Mac Studio with 256GB+ unified memory running 2-bit quantized weights) can run the model locally at reduced speed, around three to nine tokens per second. For most teams, the API or third-party inference providers are more practical than self-hosting.

8. Key considerations and limitations

Data sovereignty and regulatory factors

Z.ai is headquartered in Beijing. Its hosted API routes data through Chinese infrastructure, subject to China's National Intelligence Law and content regulations. For teams handling sensitive data, this is a meaningful consideration, as TechTimes reported alongside the launch.

The self-hosting option under MIT license mitigates this concern entirely, since running the model on your own hardware means no data leaves your environment.

US Entity List status

The US Commerce Department added Zhipu AI to its Entity List in January 2025, restricting its access to US technology. The listing primarily targets export controls on technology flowing to Zhipu, not end-user access to the open-weight model. Consult legal counsel if your organization operates under specific compliance requirements.

Where closed models still lead

Claude Opus 4.8 maintains clear advantages on SWE-Marathon (26.0 vs. 13.0), Tool-Decathlon, and several of the hardest agentic reasoning tests. For workloads that require peak reliability on complex multi-tool chains, the closed frontier is still the safer choice. GLM 5.2's advantage is in cost-efficient coding at scale, not in replacing the best closed model on every task.

Beyond the benchmarks: from model scores to shipped products

Benchmarks tell you what a model can do in controlled conditions. They do not tell you how to turn that capability into a working product your customers can use.

If you are evaluating GLM 5.2 as a backend model for an AI-powered application, you still need a frontend, a database, authentication, deployment infrastructure, and all the non-model engineering that turns a capable LLM into actual software. Emergent handles that entire stack. Describe the app you want to build, and Emergent's multi-agent architecture produces full-stack, production-ready software with a real backend, real integrations, and real code you own. Emergent's Universal LLM Key gives you access to Claude, OpenAI, and Google AI models through a single credential and unified billing.

Whether you choose to build with GLM 5.2 via your own API setup or with one of the models available through Emergent, the question that matters most is not which model scores highest on a leaderboard. It is whether you can ship something real to actual users.

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