GLM-4.5
Zhipu's Open Frontier Family
Zhipu's GLM-4.5 is a complete open-weight family — frontier MoE, a smaller "Air" tier, and a vision variant — released July 2025 under the MIT license. By the time GLM-4.6 shipped in late 2025 (trained on Chinese Cambricon chips, no Nvidia), the family was at parity with closed Western coding models on WebDev Arena.
Intelligence
Below avg
Speed
Slow
49 tok/s output
Cost
Moderate
$0.60 in / $2.20 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/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
GLM-4.7 (Dec 2025) became the first non-Western model to top WebDev Arena. The family's combination of MIT licensing, Chinese hardware, and frontier coding scores marks a turning point in the geopolitical model landscape.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Multimodal
Combines text, vision, and audio in one model.
Generative
Produces images, video, audio, or other media.
Agent Workflows
Built for tool use and autonomous tasks.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
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
6.0
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
- Vision-language model from Zhipu — see the linked sources below for benchmark and review coverage
- Tool-use and agent loops are the typical fit per the published model card
- Vision and multimodal tasks are the typical fit per the published model card
Best use cases
- Agent / tool-use workflows that match the model's published benchmarks
- Vision tasks (charts, documents, images) per the model card
- 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