Is GLM-4.6 an Open Source Alternative for Claude Sonnet 4.5?

GLM-4.6 from Zhipu AI offers a fresh option in AI models, challenging Claude Sonnet 4.5 with its open-source design. This model aims to deliver strong results at a lower price, making it attractive for developers who want more control and savings. Let's explore if it can really stand in for Claude Sonnet 4.5.

What Sets GLM-4.6 Apart

GLM-4.6 uses a 355B-parameter Mixture of Experts setup and handles up to 200K tokens. It's available for free with open weights on HuggingFace, letting users run it locally. Unlike Claude Sonnet 4.5, which depends on paid API calls, GLM-4.6 cuts costs to $0.60 per million input tokens and $2.20 for output, versus Claude's higher rates.

Performance Insights Against Claude Sonnet 4.5

In benchmarks, GLM-4.6 hits a 48.6% win rate on coding tasks from CC-Bench, nearly matching Claude Sonnet 4. Yet, it falls short against Claude Sonnet 4.5 in advanced coding. GLM-4.6 is more efficient, using 30% fewer tokens for the same jobs, which speeds up work and lowers expenses. Real tests show it works well for general coding, reasoning, and data tasks.

Improved efficiency over older GLM versions
Solid results in everyday coding
Gaps in complex software fixes, like SWE-bench where it scores 68% versus Claude's 77%

Key Advantages of GLM-4.6

GLM-4.6 stands out for its affordability. For teams handling 10 million tokens monthly, costs drop to around $22 with GLM versus $150 for Claude. It also provides full access to its code, so developers can tweak it for custom needs.

Run it on private servers to keep data secure
Avoid ties to one provider
Handle large documents easily with its 200K token limit

Where GLM-4.6 Doesn't Quite Match Up

Claude Sonnet 4.5 leads in some areas. It's better for tricky coding and offers stronger support for business use, including safety features and handling of specialized fields like finance or medicine. GLM-4.6 is great for basics but may need tweaks for high-stakes projects.

Getting GLM-4.6 Up and Running

Setting up GLM-4.6 is straightforward. It needs 2-4 GPUs for full power, but lighter versions work on one. Use tools like vLLM for easy integration.

Example command: python -m vllm.entrypoints.api_server --model /path/to/glm-4.6 --dtype float16 --quantization int4 --tensor-parallel-size 2
Fits into coding setups like VS Code extensions

Final Thoughts on the Choice

GLM-4.6 is a solid pick if you prioritize savings and flexibility. Opt for it when costs matter most or open access is key. Go with Claude Sonnet 4.5 for top coding needs or reliable enterprise tools.