Glm 4 7 251222
Context window
This model accepts 205K tokens in one request (~154K words of text).
What fits in one request
- FitsShort documentAbout 1,500 words of text
- FitsLong documentAbout 37K words of text
- FitsSmall codebaseAbout 150K words of text
- Won't fitFull novelAbout 375K words of text
Specifications
Context size, pricing, and release info in one place.
- Context window
- 204,800 tokens (205K)
- Max output tokens
- 131,072 tokens (131K)
- Speed tier
- balanced
- Provider
- Z Ai
Capabilities
See which features this model supports, such as vision, tools, and streaming.
- Tool use
- Can call external tools and APIs
- Supported
- Function calling
- Structured function call interface
- Supported
- Extended thinking
- Shows its chain-of-thought reasoning
- Supported
- Streaming
- Returns tokens as they are generated
- Supported
- Vision
- Accepts image inputs alongside text
- Not supported
- Web search
- Can browse the web during a request
- Not supported
- Batch API
- Process many requests asynchronously
- Not supported
- Prompt caching
- Reuse repeated prompt prefixes cheaply
- Not supported
Best for
Jump to a guide or ranking that matches each workload.
Compare Glm 4 7 251222
Open a side-by-side comparison with one click.
- Glm 4 7 251222 vs Amazon Titan Text Express
Glm 4 7 251222 has 387% larger context window
- Glm 4 7 251222 vs Amazon Titan Text Lite
Glm 4 7 251222 has 387% larger context window
- Glm 4 7 251222 vs Amazon Titan Text Premier
Glm 4 7 251222 has 387% larger context window
- Glm 4 7 251222 vs Claude Instant
Glm 4 7 251222 has 104% larger context window
- Glm 4 7 251222 vs Anthropic Claude
Glm 4 7 251222 has 104% larger context window
- Glm 4 7 251222 vs Codellama 34b Instruct
Glm 4 7 251222 has 4900% larger context window
Frequently asked questions
Short answers about context size and how this model behaves.
More from Z Ai
Other models by Z Ai in our catalog.
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model