GLM 5.2 vs LLaMA 4 Maverick: Large Open-Source MoE Models Compared

Two of the most capable open-weight language models available today — GLM 5.2 (glm-4-plus) from Zhipu AI and LLaMA 4 Maverick from Meta — share a surprising amount of common ground: both are massive Mixture-of-Experts (MoE) architectures, both support 1 million token context windows, both handle multimodal inputs, and both are freely available as open weights. Yet they diverge sharply in pricing, Chinese-language capability, self-hosting requirements, and integration maturity.

This guide cuts through the noise and gives you a direct side-by-side comparison so you can pick the right model for your production workload.

Quick Overview

GLM 5.2 (glm-4-plus) is developed by Zhipu AI, a Beijing-based research company, and released in May 2025. The model exposes 40 billion active parameters from a 753 billion total MoE architecture — one of the largest open-weight models available by total parameter count. It ships with an OpenAI-compatible API at https://open.bigmodel.cn/api/paas/v4/ and carries an MIT license on HuggingFace.

LLaMA 4 Maverick is Meta's flagship open-weight multimodal model, released in April 2025. It activates 128 billion parameters from roughly 400 billion total using a 128-expert MoE design. It is distributed under the Llama 4 Community License, which permits commercial use with conditions for deployments at scale.

Both models aim at the same general capability tier — GPT-4o class reasoning with massive context — but they approach it differently.

Side-by-Side Comparison Table

Pain Point: Picking the Wrong Model Costs You

The most common mistake developers make when choosing between large open-weight models is treating them as interchangeable and defaulting to the cheapest API price.

If your application serves Chinese-language users, this choice is not close. GLM 5.2 was built in China, trained on dense Chinese corpora, and is the default model powering Zhipu's ChatGLM products. LLaMA 4 Maverick performs well in English but its Chinese-language reasoning, fluency, and cultural alignment lags substantially. Choosing Maverick for a Chinese-facing product to save on API costs is a real correctness risk — you will trade a few dollars per million tokens for a meaningful drop in output quality for a significant portion of your users.

If you are self-hosting, the active parameter count matters more than total parameters. GLM 5.2's MoE activates only 40B parameters per forward pass; Maverick activates 128B. In practice, Maverick requires approximately four H100 80GB GPUs in FP8 precision to run at all, while GLM 5.2's lower active count allows it to fit on fewer GPUs. For teams with limited GPU budgets, GLM 5.2's architecture is more accessible.

If your budget is the primary constraint and English is your only language, Maverick's API pricing (as low as ~$0.22/M tokens on Groq or Together AI as of writing) is dramatically cheaper than GLM 5.2 at $1.40/M input and $4.40/M output. For high-volume English-only pipelines, that difference compounds quickly.

Benchmark Performance

GLM 5.2 posts publicly verified benchmark numbers. Its 89% on GPQA Diamond (a graduate-level science reasoning benchmark designed to be hard even for domain experts) places it among the top tier of open-weight models. The 62.1% SWE-bench Pro score indicates strong real-world software engineering ability — this is a particularly meaningful benchmark for agentic coding workflows because it tests actual repository-level bug fixing rather than synthetic problems.

The Artificial Analysis Quality Index of 51 contextualizes GLM 5.2 within the broader model landscape. At 158 tokens per second (Artificial Analysis benchmark), it is also genuinely fast for a model of its total size, benefiting from the efficiency inherent in MoE activation patterns.

Meta has not published a standardized GPQA Diamond or SWE-bench Pro score for LLaMA 4 Maverick at the time of writing. Independent evaluations position it as competitive with GPT-4o class models on English reasoning tasks, and it scores well on coding and instruction following. For direct comparisons on scientific reasoning or software engineering, GLM 5.2's published data is a clearer data point.

Multimodal Capabilities

Both models accept image inputs alongside text, but their multimodal implementations differ in maturity and design.

LLaMA 4 Maverick was designed as a natively multimodal model from the ground up. Meta integrated vision into the base architecture rather than bolting it on afterward, which tends to produce better cross-modal reasoning — the model can reason about text and images jointly rather than processing them sequentially.

GLM 5.2 supports vision through its messages array, accepting images as base64-encoded data or URLs. This works cleanly for document understanding, chart analysis, and image-to-text workflows. You can pass image inputs like this in the API:

import openai

client = openai.OpenAI(
    api_key="YOUR_ZHIPU_API_KEY",
    base_url="https://open.bigmodel.cn/api/paas/v4/"
)

response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/chart.png"}
                },
                {
                    "type": "text",
                    "text": "Describe the trend shown in this chart and identify any anomalies."
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

For a deeper look at GLM 5.2's full API surface — including function calling, JSON mode, streaming, and batch endpoints — see the GLM 5.2 API guide on glm5.app.

Competitive Differentiator: The Chinese Language Gap

This is where GLM 5.2 has a structural advantage that no API price cut from Maverick can close.

Zhipu AI is one of China's foremost AI research organizations, and GLM (General Language Model) was purpose-built with Chinese as a first-class language. The model understands idiomatic expressions, regional dialects, professional domain vocabulary, and cultural context in Chinese at a level that Western-trained models consistently fall short on.

This matters for:

• Customer-facing applications serving Chinese speakers who expect natural, fluent responses
• Document processing over Chinese regulatory filings, contracts, or research papers
• Bilingual pipelines that need reliable round-trip quality between English and Chinese
• Chinese code comments and technical documentation that mix both languages

LLaMA 4 Maverick, trained primarily on English-dominant corpora, will produce Chinese output that is often grammatically correct but misses nuance, uses awkward phrasing, and fails on culturally specific queries. For applications where Chinese quality is a product requirement, GLM 5.2 is the default choice.

Fine-Tuning and Customization

GLM 5.2 supports fine-tuning through the Zhipu platform. This means you can adapt the model to specific domains — legal, medical, finance, e-commerce — with your own labeled data, giving your application a quality edge on specialized tasks.

Maverick does not offer an official fine-tuning API as of writing. You can fine-tune the open weights yourself if you have the GPU infrastructure (which is substantial at 128B active parameters), but there is no managed service option comparable to Zhipu's.

For teams that need domain adaptation without building their own training infrastructure, this is a meaningful practical difference.

Zhipu Ecosystem: More Than One Model

Choosing the Zhipu API for GLM 5.2 also gives you access to a tiered family of models for different cost/performance points:

• GLM-4-Air — lightweight and extremely inexpensive ($0.0001 per 1K tokens), suitable for high-volume classification or extraction tasks
• GLM-4-Long — optimized for long document workflows ($0.001 per 1K tokens)
• embedding-2 — 1024-dimensional text embeddings for semantic search
• embedding-3 — 2048-dimensional embeddings for higher-fidelity retrieval

This means you can build a complete production pipeline — embedding, retrieval, generation, and heavy reasoning — within a single API and billing relationship, with a consistent authentication pattern.

Which Model Should You Use?

Use GLM 5.2 if:

• Your application serves Chinese-speaking users or processes Chinese content
• You need a fine-tuning API without running your own training infrastructure
• You want access to the broader Zhipu model family (embeddings, lightweight models)
• Your workload benefits from the lower active parameter count for self-hosting
• You need published, auditable benchmark numbers for procurement or compliance decisions

Use LLaMA 4 Maverick if:

• Your application is English-only and API cost is the primary constraint
• You have the GPU infrastructure to self-host 128B active parameters
• You prefer Meta's ecosystem and Llama tooling
• Native multimodal architecture (rather than vision adapters) is a design requirement
• You are building on platforms already hosting Maverick (Together AI, Groq)

Both models are genuinely capable at the GPT-4o tier. The decision comes down to language requirements, pricing tolerance, infrastructure, and ecosystem fit — not raw capability.

Getting Started with GLM 5.2

If GLM 5.2 fits your use case, glm5.app is the fastest way to explore the model's capabilities, test prompts interactively, and get your API integration started. The platform supports the full glm-4-plus feature set including vision, function calling, and long-context workloads.

GLM 5.2's API is OpenAI-compatible, so migration from an existing OpenAI integration typically requires only changing the base URL and API key — the SDK, request format, and response structure remain the same.

Sources

• Zhipu AI GLM-4-Plus model page: https://open.bigmodel.cn/
• GLM-4-Plus on HuggingFace: https://huggingface.co/THUDM/glm-4-plus
• Artificial Analysis GLM-4-Plus benchmark: https://artificialanalysis.ai/models/glm-4-plus
• Meta LLaMA 4 announcement: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
• LLaMA 4 Maverick on HuggingFace: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
• Zhipu API documentation: https://open.bigmodel.cn/dev/api