ChatGLM-6B
Tsinghua's Bilingual Open Chat Model
A 6-billion-parameter open-weight chat model from Tsinghua University, released the same week as GPT-4. The first major open model specifically tuned for Chinese — its release on Hugging Face within days drew >1M downloads and seeded the Chinese open-source LLM ecosystem (later joined by Qwen, Yi, DeepSeek, Baichuan).
Cost
Free
Open weights — self-host
Context
2K
Up to 2,048 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
ChatGLM is the moment Chinese AI labs stopped just translating Western models and started shipping models tuned natively for Chinese language and culture. The current Chinese AI ecosystem — Qwen, DeepSeek, Yi, Kimi — exists because ChatGLM proved a market existed.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Context Window
2k tokens
short prompt
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
2k
Availability
API
Not available
Product / App
Not available
Open Source
Released
Enterprise
—
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
0.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Language model from Zhipu — see the linked sources below for benchmark and review coverage
Best use cases
- General-purpose tasks within Zhipu's deployment footprint
- See the model spec and sources block for benchmarked use cases
Tools to try
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher