Direct
Direct upstream connection — best when you need native behavior and the full context window.
| Input | Output | Cache read |
|---|---|---|
| 0.29/M | 0.88/M | 0.06/M |

GLM-4.5VGLM-4.5V series is a flagship visual understanding model based on the MoE architecture. With 106B total parameters and 12B activated parameters, it is a comprehensive upgrade from GLM-4.1V-Thinking, reaching SOTA level among open-source multimodal models. Combined with the innovative RLCS reinforcement learning technology, it performs excellently on tasks such as video understanding, image question answering, OCR, and document parsing, and achieves significant improvements in complex scenarios including front-end web Coding, Grounding, and spatial reasoning. It supports flexible switching between thinking / non-thinking modes, balancing reasoning depth and efficiency.
The same model is available through multiple service channels — choose based on latency, reliability and cost.
Prices in $ / 1M tokensprovider field to the request body, for example "provider": { "channel": "direct" }. Valid values are direct / stable / economical; omit it to use the default channel.Direct upstream connection — best when you need native behavior and the full context window.
| Input | Output | Cache read |
|---|---|---|
| 0.29/M | 0.88/M | 0.06/M |
GLM-4.5Vhttps://api.atalk-ai.com/v2/"provider": { "channel": "direct" }SeaWhale AI is compatible with the OpenAI API protocol, so you can call it with the OpenAI SDK or plain HTTP requests. Streaming is enabled by default.
About the provider parameter (optional, a SeaWhale AI extension): most models are served over several channels that differ slightly in price and reliability. Add a provider field to the request body to pick one; omit it and the system selects the default channel — normal calls are unaffected.
provideris not part of the official OpenAI protocol — it is a SeaWhale AI extension that only takes effect on this platform. The OpenAI SDK allows custom fields like this to pass through; see the examples below.
| Value | Channel | Best for |
|---|---|---|
direct | Direct | The official upstream link, for native behavior and the full context window |
stable | Preferred | Balanced availability and speed — a good default for production traffic |
economical | Economy | Cost first, well suited to batch processing and price-sensitive workloads |
Available channels and their prices are listed under "Pricing" above (channels vary by model). Additional notes:
"provider": { "channel": "direct" }.extra_body; in Node.js put it directly on the request object and it passes through. In TypeScript projects, add a // @ts-expect-error line to skip the type check.curl https://api.atalk-ai.com/v2/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <API_KEY>" \
-d '{
"model": "GLM-4.5V",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
],
"provider": { "channel": "direct" },
"stream": true
}'
# provider is optional — remove this line to use the default channelfrom openai import OpenAI
client = OpenAI(
base_url="https://api.atalk-ai.com/v2",
api_key="<API_KEY>",
)
stream = client.chat.completions.create(
model="GLM-4.5V",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"},
],
stream=True,
# Optional: pick a service channel; omit to use the default
extra_body={"provider": {"channel": "direct"}},
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)import OpenAI from 'openai'
const client = new OpenAI({
baseURL: 'https://api.atalk-ai.com/v2',
apiKey: '<API_KEY>',
})
const stream = await client.chat.completions.create({
model: 'GLM-4.5V',
messages: [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: 'Hello!' },
],
stream: true,
// Optional: pick a service channel; omit to use the default
// @ts-expect-error provider is a SeaWhale AI extension, not in the OpenAI SDK types
provider: { channel: 'direct' },
})
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content ?? '')
}