Leaderboard / Z.ai (GLM)

GLM-4.6V-Flash

Z.ai (GLM) · GLM-4.6V · released 2025-12-08 · data checked 2026-11-01 Needs verification

GLM-4.6V-Flash is a 9B-parameter dense model from Z.ai (GLM) (China), released 2025-12-08 under the MIT license. It doesn't have enough reported benchmarks for an index score yet. It needs about 6.5 GB of memory at 4-bit with an 8K-token context.

Small LM Index
—
Needs 2 core benchmarks (has 0)
Parameters
9B
Dense
Price per 1M tokens
—
No hosted API on OpenRouter; self-host
Context window
128K
tokens

Benchmarks

Each bar shows this model's score. The tick marks the best small model on that benchmark.

No benchmark results for this release yet. Know of one? Send it.

Specifications

Lab
Z.ai (GLM) (CN · China)
Architecture
Dense transformer
Total parameters
9B
Active parameters
All (dense)
Size class
Small
Context window
128K tokens
Input modalities
text, image
Reasoning
Hybrid (thinking on/off)
License
MIT
Openness
Open weights
Memory to run
~6.5 GB at 4-bit, 8K context (more sizes)
Hugging Face
43.3K downloads (30 days) · 633 likes

Compare with

Sources

Something wrong here? Report it.

Run it locally

Uses ggml-org/GLM-4.6V-Flash-GGUF (Q4_K_M). Swap the tag for another size: Q4_K_M, Q8_0.

Ollama
ollama run hf.co/ggml-org/GLM-4.6V-Flash-GGUF:Q4_K_M
llama.cpp
llama-server -hf ggml-org/GLM-4.6V-Flash-GGUF:Q4_K_M
Then open http://localhost:8080 for a chat window.
MLX (Mac)
pip install -U mlx-lm
mlx_lm.chat --model lmstudio-community/GLM-4.6V-Flash-MLX-4bit
Text only; for images use mlx-vlm.
vLLM (GPU server)
vllm serve zai-org/GLM-4.6V-Flash
Full-precision weights, for a server GPU.

Open in LM Studio

Memory needed: weights, context cache and runtime buffers.

Quantization4K context32K context128K context
16-bit F1619 GB20 GB24 GB
8-bit Q8_010 GB12 GB15 GB
4-bit Q4_K_M6.3 GB7.5 GB11 GB

Check it against your GPU, Mac or phone →