Step 3.7 Flash API: toman pricing and code
stepfun/step-3.7-flash
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 11,605 toman / 1M.Pricing and top-ups
Step 3.7 Flash example: reading an image into JSON
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "stepfun/step-3.7-flash",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Return only the invoice number and the total, as JSON."},
{"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
]
}]
}'import base64, json
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
img = base64.b64encode(open("factor.jpg", "rb").read()).decode()
resp = client.chat.completions.create(
model="stepfun/step-3.7-flash",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
],
}],
response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
import { readFileSync } from "node:fs";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");
const resp = await client.chat.completions.create({
model: "stepfun/step-3.7-flash",
messages: [{
role: "user",
content: [
{ type: "text", text: "Return the invoice number, the date and the total as JSON." },
{ type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
],
}],
response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));What is Step 3.7 Flash good for?
StepFun publishes this model; we expose it under the id "stepfun/step-3.7-flash". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 230,400 tokens. On price it sits in the "cheap" band — cheaper than 150 and dearer than 256 of the other paid models in the catalogue.
Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
For a back-of-envelope figure: about 509 toman per 1,000-word exchange (58,025 in, 333,644 out, per 1M tokens). It supports cached input: repeated context is billed at 11,605 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.
The closest alternative with the same capabilities from a different provider is Qwen3.6 Flash: Step 3.7 Flash works out roughly 1× more expensive, and its context window is smaller. Both run on the same key and the same code, so trying the other one is a single string change.
To make the figure concrete: 100,000 toman of credit buys roughly 196 thousand-word requests on Step 3.7 Flash, and every 1,000 toman is about 1,965 words of round trip. A job with one million input tokens and one million output tokens comes to 391,669 toman in total. Filling this model's 262,144-token window costs 15,211 toman on the input side alone, which is the real reason to keep conversation history short.
StepFun has 2 models in our catalogue; the cheapest is Step 3.5 Flash at 151 toman per thousand words and the dearest Step 3.7 Flash at 509. Among the less common parameters it accepts logit_bias, logprobs, min_p, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
Step 3.7 Flash is StepFun's latest high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 220 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 432 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 13,636 toman |
Frequently asked
- How do I call Step 3.7 Flash from Iran?
- Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "stepfun/step-3.7-flash". Nothing else in your code changes.
- What does Step 3.7 Flash cost in toman?
- 58,025 toman per 1M input tokens and 333,644 toman per 1M output tokens; a 1,000-word request is around 509 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Step 3.7 Flash take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 230,400 tokens.
- Does Step 3.7 Flash support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.