GLM-4.7
Z.ai's open MoE update of the GLM-4 line
Z.ai (Zhipu) GLM-4.7 — open-weights successor to GLM-4.5 with reasoning post-training. 357B total / 32B active per token MoE, 200K context, MIT licensed. Costs $0.60 input / $2.20 output on Z.ai's API; you can also self-host the weights from Hugging Face.
Intelligence
Good
Speed
Slow
89 tok/s output
Cost
Moderate
$0.60 in / $2.20 out
Context
200K
Up to 200,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/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
Tightens the open-weights frontier against closed competitors. GLM-4.7 → 4.6 → 4.5 cadence matches DeepSeek's V3 line, suggesting Z.ai is committed to fast iteration in the open tier.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Reasoning
Solves complex math, logic, and planning tasks.
Context Window
200k tokens
≈ 154+ pages
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
200k
Availability
API
Available
Product / App
Not available
Open Source
Released
Enterprise
Contact sales
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
6.7
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
- Reasoning-style step-by-step traces in the DeepSeek V3 / GLM-4.5 lineage
- Frontier-tier on knowledge benchmarks (GPQA 85.9%, MMLU-Pro), gap to GPT-5 / Opus 4.7 still meaningful
Best use cases
- Self-host scenarios where you want Chinese-strong reasoning without API dependency
- Price-sensitive serving at $0.60/$2.20 — competitive with DeepSeek V3.2
- Long documents — 200K context
Tools to try
Not ideal for
- Multimodal / vision tasks — text-only release
- Latency-sensitive UX — reasoning traces add output time
Model Evolution
GLM is Zhipu's language model family.