Choosing between GLM-5 and GLM-4.7 often comes down to a critical trade-off: massive-scale agentic power versus proven coding versatility. GLM-5, released by Z.ai, scales dramatically from its predecessor—jumping from 355B parameters (32B active) in GLM-4.7 to 753.9B parameters (40B active). This 2.1x parameter expansion brings substantial improvements in complex systems engineering and long-horizon agentic tasks, but GLM-4.7 remains a powerhouse for multilingual coding, terminal automation, and real-world developer workflows.
Architecture Comparison of GLM-5 and GLM-4.7
| Specification | GLM-5 | GLM-4.7 |
|---|---|---|
| Total Parameters | 753.9B | 355B |
| Active Parameters | 40B | 32B |
| Context Length | 202,752 tokens | 202,752 tokens |
| Pre-training Data | 28.5T tokens | 23T tokens |
| Precision | BF16 (FP8 available) | BF16 (FP8 available) |
| Multimodal Support | Text-only | Text-only |
| Release Date | January 2026 | December 2025 |
One of GLM-5’s most practical upgrades is its integration of DeepSeek Sparse Attention (DSA), which significantly reduces the cost of long-context attention while preserving large context windows up to 202K tokens. This makes GLM-5 far more deployable for real-world long-document reasoning, multi-turn assistants, and agent-style workflows.On the post-training side, GLM-5 benefits from slime, a new asynchronous reinforcement learning infrastructure that boosts RL training throughput and enables more frequent and fine-grained alignment iterations.
Benchmark Comparison of GLM-5 and GLM-4.7
From a benchmark perspective, GLM-5 shows a broad and consistent improvement over GLM-4.7, especially in tool-use, browsing, and agentic settings. The largest gains appear in environments that require multi-step planning, context management, and real-world execution, suggesting GLM-5 is optimized for agent-style workflows rather than isolated reasoning tasks.
GLM-4.7 benchmarks like an efficiency-optimized reasoning/coding model, still very strong in classic math-style evaluation, but less dominant in interactive tool-driven tasks.
VRAM Requirements of GLM-5 and GLM-4.7
The 2.1x parameter increase from GLM-4.7 to GLM-5 brings substantial hardware implications. Here’s the VRAM breakdown:
Recommended GPU Configuration for GLM-5
| Precision | VRAM Required | Recommended Setup | Use Case |
|---|---|---|---|
| BF16 | 1,508 GB | 19x NVIDIA H100 (80GB) | Maximum quality research |
| FP8 | About 800GB | 10x NVIDIA H100 (80GB) | Production deployment |
| INT4 | About 400GB | 5x H100 (80GB) | Cost-efficient inference |
Recommended GPU Configuration for GLM-4.7
| Precision | VRAM Required | Recommended Setup | Use Case |
|---|---|---|---|
| BF16 | 717 GB | 9x NVIDIA H100 (80GB) | Maximum quality |
| FP8 | 390 GB | 5x H100 (80GB) | Production deployment |
| INT4 | 200 GB | 3x H100 (80GB) | Cost-efficient inference |
In FP8 deployment, GLM-5 typically requires twice the GPU count compared to GLM-4.7.
For developers with limited budgets, GLM-4.7 offers a stronger performance-per-dollar profile in coding-focused workloads, achieving 73.8% on SWE-bench Verified and 84.9% on LiveCodeBench-v6.
For frontier research and agentic system development, GLM-5’s stronger tool use and long-horizon execution capabilities can justify the additional hardware investment.
Pricing & API Access of GLM-5 and GLM-4.7
| Model | Input ($ / M tokens) | Cache Read ($ / M tokens) | Output ($ / M tokens) |
|---|---|---|---|
| GLM-4.7 | $0.60 | $0.11 | $2.20 |
| GLM-5 | $1.00 | $0.20 | $3.20 |
Cache Read refers to the cost of reading tokens that were previously stored in the prompt cache. When the same prompt content is reused across requests, the model retrieves these tokens directly from the cache instead of processing them again from scratch. This reduces both inference latency and cost.
Step 1: Log In and Access the Model Library
Log in to your account and click on the Model Library button.
Step 2: Choose Your Model
Browse through the available options and select the model that suits your needs.
Step 3: Start Your Free Trial
Begin your free trial to explore the capabilities of the selected model.
Step 4: Get Your API Key
To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.
Step 5: Install the API
Install API using the package manager specific to your programming language.
After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.
from openai import OpenAI
client = OpenAI(
api_key="<Your API Key>",
base_url="https://api.novita.ai/openai"
)
response = client.chat.completions.create(
model="zai-org/glm-5 or zai-org/glm-4.7",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you?"}
],
max_tokens=131072,
temperature=0.7
)
print(response.choices[0].message.content)
Decision Framework Summary of GLM-5 and GLm-4.7
| Scenario | Recommended Model | Key Reason |
|---|---|---|
| Multi-agent systems with tool orchestration | GLM-5 | +15.8pp on MCP-Atlas, +14.2pp on Tool-Decathlon |
| Production SWE-bench workflows | GLM-4.7 | 73.8% at half the hardware cost |
| Cybersecurity & pentesting | GLM-5 | 43.2% CyberGym |
| IDE-based coding (Claude Code, Cline) | GLM-4.7 | Preserved Thinking + lower latency |
| Frontier reasoning research (HLE) | GLM-5 | 50.4% with tools (best open-source) |
| UI/frontend “vibe coding” | GLM-4.7 | Specialized training for modern web UI |
| Terminal automation (long-horizon) | GLM-5 | +28.3pp on Terminal-Bench 2.0 |
| Math competitions (AIME, HMMT) | GLM-4.7 | Matches/exceeds GLM-5 at lower cost |
| Budget-constrained startups | GLM-4.7 | Strong coding at 4x H100 vs 8x H100 |
| Research labs pushing AGI limits | GLM-5 | 28.5T token pre-training, slime RL infrastructure |
GLM-5 doesn’t obsolete GLM-4.7—it addresses different problems. If your work involves long-horizon agentic tasks requiring extensive tool use and multi-step reasoning, the 2x hardware investment in GLM-5 pays off in task completion rates. If you’re shipping coding assistants to thousands of developers or need fast iteration cycles in IDE environments, GLM-4.7’s leaner architecture and specialized training make it the better fit. Both models represent significant achievements in open-source language modeling, closing the gap with frontier proprietary models while maintaining full transparency and local deployment flexibility.
Frequently Asked Questions
GLM-5 scales from 355B to 753.9B total parameters (32B to 40B active) and integrates DeepSeek Sparse Attention (DSA) to reduce deployment costs while preserving 202K context length.
No. GLM-5 requires at least 10x H100 80GB GPUs in FP8 mode (800GB VRAM), far exceeding consumer GPU capabilities.
GLM-5 edges out GLM-4.7 with 77.8% on SWE-bench Verified (+4pp), but GLM-4.7’s 73.8% at half the hardware cost makes it more practical for production.
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing an affordable and reliable GPU cloud for building and scaling.
Recommended Reading
- How to Access Qwen3-Coder-Next: 3 Methods Compared
- Comparing Kimi K2-0905 API Providers: Why NovitaAI Stands Out
- How to Use GLM-4.6 in Cursor to Boost Productivity for Small Teams
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