glm-4-6v
Visual understanding, information extraction, and tool calling.
Pricing: Input 72 credits / 1M tokens (≈ $0.36), Output 216 credits / 1M tokens (≈ $1.08).
Prices updated
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This demo page does not include the full memory features of the client. For the complete experience, please use the client or the CLI.
GLM-4.6V API
Visual understanding, information extraction, and tool calling.
Parameters
| Parameter | Description |
|---|---|
| model | glm-4-6v |
| messages | user / assistant / system; text / image_url |
| context | 128K tokens; text and image input, text output. |
| max_tokens | integer |
| stream | true / false (SSE) |
| thinking.type | enabled / disabled |
| tools / tool_choice | function / auto |
Pricing
Current retail rate per 1M tokens: input 72 credits, cached input 12 credits, output 216 credits. Requests reserve credits before execution and settle once from actual token usage, rounded up to whole credits. Failed requests are refunded.
Input image
Real API examples 1
Generated by this exact model on 2026-11-11. Prompt: Count blue and orange squares; return JSON counts. Response: {"blue": 3, "orange": 2}
Real API examples 2
Generated by this exact model on 2026-11-11. Prompt: Extract JSON with item and quantity: 3 blue notebooks. Output JSON only. Response: ```json { "item": "blue notebooks", "quantity": 3 } ```
API examples · OpenAI
curl https://modelfold.ai/v1/chat/completions \ -H "Authorization: Bearer $MODELFOLD_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"glm-4-6v","messages":[{"role":"user","content":"Hello"}],"max_tokens":1024,"stream":true}'
API examples · Anthropic
from anthropic import Anthropic client = Anthropic(api_key="YOUR_MODELFOLD_KEY", base_url="https://modelfold.ai/anthropic") message = client.messages.create(model="glm-4-6v", max_tokens=1024, messages=[{"role":"user","content":"Hello"}]) print(message.content)
FAQ
How do I enable streaming and tools?⌄
Use stream: true for SSE. Pass tools and tool_choice for function calling. Tool execution remains the responsibility of your application.
Is conversation history retained?⌄
Send previous user and assistant messages in messages. API calls do not keep a conversation automatically.
How does caching work?⌄
The first request is generally uncached. Reuse an identical prefix for later cache hits; cache usage is reported by the API and is not guaranteed.
Can I use the Anthropic protocol?⌄
Yes. Use /anthropic as the base URL with the Anthropic SDK, the same ModelFold key, and this model ID. Responses API is not supported.
Can this model read images?⌄
Yes. Supply image_url parts in OpenAI messages, or image blocks in Anthropic messages.
| Input | 72 credits 1M tokens≈ $0.36 |
| Cached input | 12 credits 1M tokens≈ $0.06 |
| Output | 216 credits 1M tokens≈ $1.08 |
Billed per token: input and output are priced separately, and thinking tokens count as output. The Playground charges exactly the same way.
Read API docs →AI Agents · Claude Code / Cursor / Trae →curl https://modelfold.ai/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"glm-4-6v","messages":[{"role":"user","content":"Hello"}]}'