Short text extraction
Extract named fields from a supplied document and validate the schema.
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="qwen/qwen3.8-flash",
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: "qwen/qwen3.8-flash",
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": "qwen/qwen3.8-flash",
"messages": [{"role": "user", "content": "Hello!"}]
}'Catalog snapshot: 2026-10-16. Live prices have not been verified yet.
Qwen3.8-Flash is available through the IteraCompute OpenAI-compatible API, with 1M context, streaming and reasoning.
Extract named fields from a supplied document and validate the schema.
Try one small code change with a failing test and review the result.
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 | Supported | Supported |
| 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.
For JSON responses, validate your application schema before use. Input and requested output share the context budget.
Prices in USD per 1M tokens.
Assume 12,000 aggregate input tokens and 1,000 billable output tokens across all calls. No cache hit.
12,000 × $0.13 / 1M + 1,000 × $0.42 / 1M = $0.001980
If usage actually reports 9,000 of those input tokens as cached, replace that portion at the cached-input rate ($0.02/1M): $0.000990. 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 |
{"enable_thinking":false} | Supported; use one thinking-control style per request |
{"enable_thinking":true} | Supported; use one thinking-control style per request |
https://api.iteracompute.com/v1POST /v1/chat/completionsqwen/qwen3.8-flashSet base_url to https://api.iteracompute.com/v1 and model to qwen/qwen3.8-flash. 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.