Engineering task planning
Supply a bounded issue and ask for an ordered implementation plan.
USD / 1M tokens
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.iteracompute.com/v1",
api_key=os.environ["ITERACOMPUTE_API_KEY"],
)
response = client.chat.completions.create(
model="z-ai/glm-5.2",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.iteracompute.com/v1",
apiKey: process.env.ITERACOMPUTE_API_KEY,
});
const response = await client.chat.completions.create({
model: "z-ai/glm-5.2",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);curl https://api.iteracompute.com/v1/chat/completions \
-H "Authorization: Bearer $ITERACOMPUTE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "z-ai/glm-5.2",
"messages": [{"role": "user", "content": "Hello!"}]
}'Catalog snapshot: 2026-10-16. Live prices have not been verified yet.
GLM-5.2 is available through the IteraCompute OpenAI-compatible API, with 1M context, streaming and reasoning.
Supply a bounded issue and ask for an ordered implementation plan.
Compare supplied versions and cite the passages behind each change.
Task suggestions based on catalog features and price, not model quality benchmarks.
Features available through this model interface. Updated October 16, 2026.
| Capability | Model developer documentation | Route test |
|---|---|---|
| Tool calling | Supported | Supported |
| Reasoning | Supported | Supported |
| Image input | Text only | Text only |
| Streaming | Supported | Supported |
| JSON mode (json_object) | Supported | Supported |
Context: 1M · Output limit: 128K
Set thinking controls per model. Use one control style per request.
Reasoning stays enabled on this interface.
For JSON responses, validate your application schema before use. Input and requested output share the context budget.
Prices in USD per 1M tokens.
Assume 30,000 aggregate input tokens and 3,000 billable output tokens across all calls. No cache hit.
30,000 × $0.53 / 1M + 3,000 × $1.72 / 1M = $0.021060
If usage actually reports 22,500 of those input tokens as cached, replace that portion at the cached-input rate ($0.12/1M): $0.011835. Cached tokens are part of total input, never added on top.
Illustrative calculation using the 2026-10-16 catalog snapshot. Include every agent call and retry, repeated history and billable reasoning output; settle against actual usage and current Console prices.
| Parameter / protocol field | Usage |
|---|---|
model / messages | Use the exact model ID and chat message list |
stream=true / stream_options.include_usage=true | Receive SSE chunks, [DONE] and final usage |
tools / tool_calls / role=tool | Define functions in tools, execute tool_calls and return results with role=tool |
response_format.type=json_object | Request valid JSON; validate the required structure in your application |
reasoning_content | Reasoning output is separate from the final reply and counts toward output usage |
https://api.iteracompute.com/v1POST /v1/chat/completionsz-ai/glm-5.2Set base_url to https://api.iteracompute.com/v1 and model to z-ai/glm-5.2. Authenticate with your IteraCompute API key.
See the pricing section for current input, cached-input and output rates in USD per 1M tokens. Check the refresh time before use.
1M. Input and requested maximum output share the context budget.
Create your key, choose a tool and get a non-empty first reply.