GLM-4.7 vs Claude Sonnet 4.5: Which One Should You Choose?

In late 2025, the frontier model landscape shifted toward “Hybrid Reasoning”—models that pause to “think” before generating a response. Two major flagship models define this era: Anthropic’s Claude 4.5 Sonnet and Z.AI’s GLM-4.7. While Claude focuses on proprietary, agentic excellence with native computer control, GLM-4.7 offers a powerful open-weights alternative with unique cost efficiencies and state-of-the-art mathematical capabilities.

GLM-4.7 vs Claude 4.5 Sonnet: Basic Introduction

Both models integrate “Chain of Thought” (CoT) reasoning directly into the generation process, but they approach architecture and deployment differently.

Feature	GLM-4.7	Claude 4.5 Sonnet
Developer	Z.AI (Open Weights)	Anthropic (Closed Source)
Release Date	Dec 22, 2025	Sep 29, 2025
Architecture	358B Parameter Mixture-of-Experts (MoE)	Proprietary Hybrid Reasoning Model
Context Window	200k Input / 128k Output	200k Input / 64k Output

GLM-4.7 vs Claude 4.5 Sonnet: Benchmark

Math / Olympiad-style Reasoning

• AIME 2025: GLM-4.7 95.7 vs Claude 87.0 (+8.7 for GLM)

Coding (benchmark-style)

• LiveCodeBench-v6: GLM-4.7 84.9 vs Claude 64.0 (+20.9 for GLM)

Real-world Software Engineering

• SWE-bench Verified: GLM-4.7 73.8 vs Claude 77.2 (+3.4 for Claude)

Agentic Terminal Tasks

• Terminal Bench 2.0: GLM-4.7 41.0 vs Claude 42.8 (+1.8 for Claude; very close)

Tool Use / Interactive Tool Invocation

• τ²-Bench: GLM-4.7 87.4 vs Claude 87.2 (essentially tied)
• HLE (w/ Tools): GLM-4.7 42.8 vs Claude 32.0 (+10.8 for GLM)

Web Task / Browsing-style Evaluation

• BrowseComp: GLM-4.7 52.0 vs Claude 24.1 (+27.9 for GLM)
• BrowseComp (w/ Context Manage): GLM-4.7 67.5 (Claude not reported in that row on the same table)

💡Interpretation:

• If your priority is math reasoning and benchmark-style coding, GLM-4.7 leads strongly (AIME, LiveCodeBench).
• If your priority is real software engineering, Claude leads on SWE-bench Verified.
• For interactive tool use, the picture is mixed: τ²-Bench is a tie, but GLM-4.7 is higher on tool-augmented HLE and BrowseComp in the published table.

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GLM-4.7 vs Claude 4.5 Sonnet: Speed & Latency

🤖Net takeaway:

GLM-4.7 has a small edge in “snappy” responses, while in deep-reasoning scenarios both behave similarly—so optimizing perceived speed is mostly about controlling when the model enters long thinking rather than choosing between the two on decode speed alone.

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GLM-4.7 vs Claude 4.5 Sonnet: Cost

GLM-4.7 offers a significant cost advantage. The following table compares Novita AI’s pricing for GLM-4.7 against Anthropic’s direct API pricing.

Model	Provider	Input Price (per 1M tokens)	Output Price (per 1M tokens)
GLM-4.7	Novita AI	$0.60	$2.20
Claude 4.5 Sonnet	Anthropic	$3.00	$15.00

🎉Cost Implication:

GLM-4.7 is 5x cheaper on input and roughly 6.8x cheaper on output than Claude 4.5 Sonnet. For applications requiring heavy reasoning (which generates more output tokens), GLM-4.7 provides a massive reduction in operational expenditure.

Try GLM 4.7 Now！

How to Access GLM 4.7 on Novita Al

Novita AI provides flexible and developer-friendly access to GLM-4.7, enabling you to use this high-performance hybrid-reasoning model across research, production, and agentic AI workflows. Whether you are exploring advanced mathematics, large-scale code generation, or building cost-efficient autonomous systems, Novita AI offers the infrastructure to get started quickly.

Option 1: Use the Playground

(Available Now – No Coding Required)

• Instant Access: Create an account and start experimenting with GLM-4.7 in seconds.
• Interactive Interface: Test prompts, toggle reasoning behavior, and inspect long-context outputs in real time.
• Model Comparison: Compare GLM-4.7 with other flagship models to evaluate reasoning depth, cost efficiency, and output quality.

The Playground is ideal for prototyping, prompt experimentation, and evaluating GLM-4.7’s strengths in mathematics, tool use, and high-aesthetic code generation before integrating it into production systems.

Option 2: Integrate via API

(For Developers)

Bring GLM-4.7 into your applications using Novita AI’s OpenAI-compatible unified API.

Step 1: Log In and Access the Model Library

Log in (or sign up) to your Novita AI account and navigate to the Model Library.

Step 2: Choose GLM-4.7

Browse the available models and select GLM-4.7 based on your workload requirements.

Step 3: Start Your Free Trial

Activate your free trial to explore GLM-4.7’s reasoning, long-context, and cost-performance characteristics.

Step 4: Get Your API Key

Open the Settings page to generate and copy your API key for authentication.

Step 5: Install and Call the API (Python Example)

Below is a simple example using the Chat Completions API with Python:

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-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)

This setup allows you to control reasoning depth, token usage, and generation behavior—particularly useful when leveraging turn-level thinking to manage cost and latency.

Option 3: Multi-Agent Workflows with OpenAI Agents SDK

Build sophisticated multi-agent systems using GLM-4.7 as a reasoning or tool-specialist agent:

• Plug-and-Play Integration: Use GLM-4.7 in any OpenAI Agents workflow.
• Advanced Agent Patterns: Support for routing, handoffs, tool invocation, and mixed “Think / Non-Think” modes.
• Cost-Efficient Scaling: Ideal for large agent swarms or math-heavy reasoning tasks at scale.

This approach is well suited for combining GLM-4.7 with other models—for example, using GLM-4.7 for mathematical reasoning and structured coding, while delegating OS-level tasks to other agents.

Option 4: Connect with Third-Party Platforms

• Development Tools: Integrate GLM-4.7 with IDEs and AI coding tools such as Cursor, Trae, Qwen Code, and Cline via Novita AI’s OpenAI-compatible API.
• Orchestration Frameworks: Connect GLM-4.7 to LangChain, Dify, CrewAI, Langflow, and other orchestration platforms using official connectors.
• Hugging Face Ecosystem: Novita AI acts as an official inference provider for Hugging Face, ensuring broad compatibility across the open-source ML ecosystem.

Conclusion

Claude Sonnet 4.5 is the stronger choice when you prioritize real-world software engineering outcomes (e.g., SWE-bench Verified lead) and when your product depends on computer interaction capabilities supported through Anthropic’s Computer Use tooling.

GLM-4.7 is compelling when you want an open-weights model with strong published results on math reasoning, benchmark-style coding, and several tool/browsing-style evaluations, while offering a large pricing advantage under Novita’s rates.

Frequently Asked Questions

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