GLM 4.5 is the best open-sourced LLM, beats Kimi-K2
It has been a crazy week where we saw some big releases. Be it Wan2.2 or Hunyuan-World 1.0 or GLM 4.5, the best open-sourced LLM.
But the story didn’t end their, Zhipu, GLM’s owner dropped not just one but two bombs, GLM 4.5 and GLM 4.5 Air.
https://medium.com/media/7fb1c4e60dc8f0c576abc16a10db908c/href
This blogs clearly defines the difference between the two and when to use what
Architecture
Both models use a Mixture-of-Experts (MoE) design. That means during any single inference step, only a subset of the full model is active. In this case, GLM‑4.5 uses about 32B active parameters, while Air uses around 12B.
https://medium.com/media/157e55d86c749b0220bf769c84f8bcb7/href
Both support 128k context using grouped-query attention (GQA), similar to how Claude or Gemini handles long prompts.
Inference requirement
GLM‑4.5 needs multi-GPU setups (A100 or H100 class) for real-time inference. Not ideal for local setups. GLM‑4.5 Air runs on a single 3090 or 4090, even on lower VRAM using quantized INT4 versions. People have run it on Colab with no major issues.
Air is roughly 2x faster than full GLM‑4.5 in generation speed due to fewer active parameters.
Training Data and Capabilities
Both models were trained on ~23T tokens:
15T general corpus (web, books, conversations)
5–8T code, reasoning, math, and documents
Uses RLHF with SLIME: a training strategy mixing supervised fine-tuning with feedback over multiple trajectories.
They both support:
Multi-turn reasoning (chain-of-thought)
Code generation (Python, C++, JavaScript)
Tool use (JSON-based API calling)
Function calling (OpenAI style)
Retrieval-augmented generation (RAG compatibility)
Bnechmarks
Both beat models like DeepSeek-V2, Yi-1.5, and Claude Sonnet on several reasoning and coding benchmarks. Air performs ~2–3% lower across the board but still better than most models in its weight class.
GLM 4.5 is the best open-sourced model, by some margin
API Costs
This is significantly cheaper than OpenAI, DeepSeek, or even Mistral’s hosted endpoints. You can also run both models locally under Apache 2.0.
Usecase
Model Files and Deployment
Both the models are open-sourced and available for free as well
- Available on HuggingFace, ModelScope, and Zhipu’s own infra.
- Supports INT8, INT4 quantization for fast CPU/GPU inference.
- Works with vLLM, FastChat, and Exllama2 backends.
- Has OpenAI-style compatible APIs for seamless integration into tools like LangChain, AutoGen, Haystack.
When to use what?
Use GLM‑4.5 when you’re working with heavy reasoning tasks, multi-step tool use, or large context workflows that demand precision and robustness think agents that plan, simulate, or write complex code across long documents. It’s built for high-stakes domains where even small gains in performance are worth the compute cost. You’ll need powerful hardware (A100s or H100s, multi-GPU setups), but you get better benchmark results and more reliability for complex tasks like legal analysis, mathematical reasoning, or long-chain question answering.
Use GLM‑4.5 Air when you need speed, affordability, and lower hardware requirements without losing much performance. It runs smoothly on single 3090s or even laptop-grade GPUs with quantization, generates faster, and still handles most real-world tasks like summarization, code completion, and chatbots just fine. If you’re building products or running models at scale where latency, throughput, and cost actually matter, Air is almost always the smarter choice.
Final Notes
If you’re running on consumer-grade GPUs and want a good balance of speed and capability, go with GLM‑4.5 Air. It’s competitive with many larger models while being cheap to deploy. If you’re building an AI agent that needs top performance on reasoning, coding, and multi-step planning GLM‑4.5 full is worth the compute, assuming you can afford it.
Don’t pick based on size alone. Pick based on what you actually need.
GLM 4.5 vs GLM 4.5 Air was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
