GLM-4.7

With an overall score of 76.24%, GLM-4.7 presents itself as an unusually serious all-rounder: a Frontier generalist with Instruct discipline, a clear coding streak, and an optional Thinking mode — though the benchmark ran it in standard operation. The model is available as a Cloud Open-Weights offering from Zhipu AI / Z.AI and is built on a MoE architecture with 355 billion total parameters, but only 32 billion active parameters per token. That explains its character quite well: less brute-force throughput, more specialized and often highly accurate partial competence. The speed profile badge “Batch DevOps Expert” fits accordingly. At 29.16 tokens per second, GLM-4.7 is not slow, but clearly geared toward substantial workloads rather than rapid-fire chat interactions. Sovereign Risk: HIGH — Zhipu AI is based in China, and cloud processing falls under Chinese jurisdiction; for European organizations, this is not a footnote when handling sensitive data — it is a governance issue.

Header Grades: Stability and Reliability

Architecture and Character: Versatile, but Not Lightweight

The pre-assigned category General, Instruct, Coder, Thinking-Optional fits GLM-4.7 with surprising precision. As a Generalist, it must deliver across the full breadth — not just in one specialty. As an Instruct model, it should execute instructions directly and cleanly. As a Coder, a certain technical sharpness is expected. And as a Thinking-Optional model, it is worth keeping in mind that the benchmark deliberately measures standard mode — without explicitly activated extended reasoning.

That last point matters. GLM-4.7 feels, across several tasks, as though it is doing genuinely complex internal work — just without the full methodical illumination of a fully activated Thinking path. This produces a characteristic profile: frequently good to very good results, but not always with the deepest didactic clarity or the last bit of robustness under pressure. It is not a nervous short-answer model. More like one that visibly takes a breath, winds up, and then delivers. Unfortunately, sometimes so slowly that the user starts watching the clock.

The MoE architecture is more than technical folklore here. Of the 355 billion parameters, only 32 billion are active per token. This means expectations should be calibrated to active capacity, not the raw total. GLM-4.7 does not play the card of maximum brute mass — it plays specialization. The result is a model that often prioritizes intelligently, but does not bring the full force of an uncompromising top-tier solution to every domain.

Performance: Batch Over Blitz

The “Batch DevOps Expert” badge is not decorative — it is a useful shorthand. It describes a model better suited to longer, substantive tasks in technical and operational contexts than to ultra-reactive live interaction. The measured generation speed of 29.16 tokens per second should be read as a characteristic of Zhipu AI / Z.AI’s cloud infrastructure, not as a universal property of the weight model in every conceivable environment.

In practice, this means: linear output is reasonably fast, but the problematic point is reliability in the long tail of the distribution. The average looks reasonable. The outliers do not. A model that lands beyond 125.84 seconds on five percent of requests can become a bottleneck in agentic workflows. Anyone automating with it needs retry logic, timeouts, and fallbacks where necessary. GLM-4.7 is not a tool for systems that must respond within seconds. It is more suited to task batches that prioritize quality over reflexive responsiveness.

Code Quality and Security: Technically Strong, Security Judgment Not Quite Sharp Enough

GLM-4.7 shows the fingerprints of its Coder tag in code and audit tasks. The Code Quality Audit score of 70.56 is not outstanding by Frontier standards, but the qualitative logs tell the more important story: the model identifies a great deal, structures cleanly, and maintains format discipline. In the security audit, it identified 19 vulnerabilities and correctly produced the required Markdown table. That is not trivial. Many models fail not on the concept but on clean execution.

The problem runs deeper. GLM-4.7 often identifies security flaws correctly but tends to assess their severity too conservatively. In the audit, three genuinely critical vulnerabilities were rated only “High” instead of “Critical”: plaintext passwords, type juggling in API authentication, and an IDOR flaw in profile updates. This is precisely where solid technical understanding diverges from genuine security maturity. Missorting priorities does not just produce the wrong patch plan — it produces the wrong order of operations for an actual incident.

Explanatory depth also leaves room for improvement. The model correctly identifies type juggling as a problem, for instance, but omits critical nuances such as magic hash scenarios or timing aspects. On IDOR, it names the immediate vulnerability but misses the dangerous attack chain leading to admin takeover. This is not a beginner’s mistake. It is the typical weakness of a model that describes symptoms well but does not always think through operational escalation to its conclusion.

That said: for technical analysis, refactoring guidance, and initial security passes, GLM-4.7 is clearly usable. Anyone looking to use it for serious security review should have the results cross-checked by a second system or a human with a threat model. It sees the holes in the fence. It just occasionally underestimates how many people have already walked through them.

CLI, Tooling, and Hallucination Risk: Strong Terminal Instincts, Dangerous Slip

With a CLI score of 91.34, GLM-4.7 ranks among the more convincing models for terminal-adjacent tasks. This aligns with the DevOps badge and the Coder classification. Scores like this are not earned through fine words — they require reliable format discipline and technical precision. The model understands the rhythm of operational instructions, shell thinking, and sequential workflows.

That is precisely why the documented hallucination incident in tool use hits harder. In a tool execution task, GLM-4.7 fabricated content that did not originate from the actual tool output retrieved. The system consequently capped the P2 score via the hallucination cap. For research, reporting, or agentic workflows with external data sources, this is a red flag — not because the model hallucinates constantly, but because a single such instance in content-critical environments is enough to erode trust.

The ToolUse score of 42.5 confirms exactly this ambivalence. GLM-4.7 can integrate tools operationally. But when the final step from tool output to verbal synthesis arrives, the iron self-discipline sometimes falters. An agent that cleanly retrieves data and then fabricates during summarization is worse than one that fails openly. The first just looks more convincing.

Reasoning and Logic: Strong, but Not at Maximum Luminosity

In the reasoning domain, GLM-4.7 delivers a strong result with 76.87. The qualitative evaluation of the guardian paradox illustrates the model’s thinking clearly. It solves the task correctly, explains the double negation cleanly, checks both cases, and stays fully in German. That is not merely correct — it is methodically sound.

What is missing is less about logic than didactic ambition. The Judge praises the linear, clear analysis but notes the absence of visual compression, alternative formulations, and a meta-explanation of why the technique is robust. Put differently: GLM-4.7 thinks correctly, but not always with the illustrative generosity that turns a good answer into an excellent one.

Latency compounds this. In the reasoning module, the P95 response time for the individual case was 158.71 seconds, with 1 timeout in 11 cases. This is consistent with the overall picture: anyone expecting deeper reasoning without explicitly enabling Thinking mode will often get correct results from GLM-4.7 — but will pay for them in patience. The fact that the model supports extended thinking via API in principle only partially relativizes this finding. The benchmark deliberately measures standard operation. And that is precisely where the model’s behavior without special treatment becomes visible.

Content Transformation and UX Writing: Creatively Controlled, Sometimes Too Verbose

In Content Transformation, GLM-4.7 reaches 77.14 and demonstrates a notable blend of stylistic sensibility and production awareness. The qualitative log for the German 2FA video script is almost a character portrait of the model: clear time markers, usable screen annotations, spoken language rather than written prose, and an overall editor-ready structure. This is not sterile reformulation — it is genuine adaptation with an eye on medium and audience.

Particularly striking is how confidently GLM-4.7 navigates tone. The dialogue snippets sound direct, everyday-ready, and nothing like manual German. It hits the register well, integrates visual and audio cues meaningfully, and understands that a good script needs not just information but pacing. The Judge’s primary criticism is pointed: the pattern interrupt around the critical hold phase is missing. For YouTube mechanics, this is not a minor detail — it is a retention tool. The model builds hook and CTA competently. In the middle, it just does not tighten the dramatic screw quite enough.

In UX Writing, GLM-4.7 scores 75.73. That is good, but not lean. It formulates often accurately and in user-friendly terms, but tends toward longer outputs than necessary. In this module especially, a strong Instruct model should be cutting with precision. GLM-4.7 can condense — it just frequently chooses not to. It rarely writes badly. Only sometimes as though it is handing the reader the entire workbench when a clean screwdriver would have sufficed.

Documentation and Knowledge Preparation: Usable, but Not Top of the Class

The Documentation Quality score of 67.87 marks one of the model’s more visibly weaker areas. This does not mean GLM-4.7 cannot write documentation. It means its Frontier competition gets there with more discipline, didactic precision, or structural economy. Against its otherwise strong technical profile, this weakness stands out.

Characteristic of GLM-4.7 here as well: the substance is often right, but the form is not always ideally calibrated. It tends to over-explain rather than under-explain, losing sharpness in the process, and feels less elegant in documentary tasks than in code or logical analysis. For internal notes, technical explainers, and longer drafts, it works. For documentation that needs to be immediately publish-ready and maximally economical, cleaner tools exist.

Cultural Intelligence: Surprisingly Strong for a Technical Workhorse

With 81.72 in Cultural Intelligence, GLM-4.7 reveals one of the more pleasant sides of its profile. The model is not purely a technician with tunnel vision. It can read context, adjust register, and appears to handle intercultural requirements with considerably more robustness than one might expect from a model with such a clear DevOps and coding orientation.

That deserves credit — especially given that the model card itself flags potential weaknesses outside Chinese-English contexts. In the benchmark, at least, this concern does not manifest dramatically. GLM-4.7 is culturally more attentive than its matter-of-fact machine demeanor initially suggests.

API Cost Profile

GLM-4.7 is priced affordably at $0.38 per million input tokens and $1.74 per million output tokens. The catch is not the rate — it is the consumption. The model is noticeably more verbose than average across several modules.

Particularly striking is Cultural Intelligence: an average of 2,474 tokens against a fleet median of 220. That is 11.25 times the fleet average. In the CLI domain, GLM-4.7 also stands at 1,808 tokens versus a median of 287 — 6.3 times the median. In UX Writing, it produces 3,883 tokens versus 1,438, a factor of 2.7. Even where quality holds up, the model consistently generates far more text than necessary. For API users, this simply means higher costs and longer runtimes without automatically higher value.

One can read this as generosity. In accounting, it still reads as consumption.

Data Privacy and Data Sovereignty

The data privacy situation for GLM-4.7 is clear and uncomfortable. The calculated Sovereign Risk is HIGH. Rationale: the model provider and cloud operator is Zhipu AI in Beijing, China, meaning processing falls under Chinese law, including PIPL, CSL, and DSL. For German and European organizations, this means one thing above all: there is no EU adequacy decision for China, international data transfers are correspondingly fraught, and government access requirements under Chinese law are a real risk.

The listed data location is China. Data retention is indicated as -1 days — meaning it has not been transparently verified. A GDPR DPA is marked as unknown. For organizations required to operate in GDPR compliance, this is not a cosmetic flaw — it is a potential procurement obstacle. The separately flagged weights provenance risk is likewise HIGH and aligns with the deployment situation: Chinese weights, Chinese jurisdiction, Chinese cloud processing. Anyone working with this model should not pass confidential content through it without consideration.

Conclusion

GLM-4.7 is an interesting model precisely because it does not excel in just one discipline. It combines Generalist breadth, Instruct reliability, solid to strong coding, and usable reasoning into an overall package that scores 76.24% in the benchmark — firmly in serious contention. It is particularly strong in CLI-adjacent tasks, technical structured work, standard logical problems, and — perhaps surprisingly — Cultural Intelligence. It is less convincing where final prioritization sharpness, didactic elegance, or uncompromising source fidelity are required.

Its greatest flaw is not lack of intelligence but unreliability at the edges. Three timeouts, critical tail latency, and a documented hallucination incident in a tool execution task. For productive agentic systems, this is not an academic shortcoming — it is an operational risk. Those deploying GLM-4.7 should consider it for batch-oriented technical tasks, code analysis, shell assistance, drafting, and structured content adaptation. For time-critical workflows, autonomous research pipelines, and compliance-sensitive enterprise environments, caution is warranted. GLM-4.7 is not a bluffer. But it is also not a model you hand the keys to the engine room without guardrails.

This evaluation was generated automatically based on the benchmark data. Model used: GPT 4.5 by OpenAI. The raw data and full methodology are documented in the GitHub project.