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Step 3.7 Flash API: toman pricing and code

stepfun/step-3.7-flash

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
69,244 toman
Output · per 1M tokens
398,152 toman
One 1,000-word request ≈
608 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 13,849 toman per 1M tokens.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 242 and dearer than 156 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 608 toman per 1,000-word exchange (69,244 in, 398,152 out, per 1M tokens). It supports cached input: repeated context is billed at 13,849 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, and Step 3.7 Flash costs about the same, 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 164 thousand-word requests on Step 3.7 Flash, and every 1,000 toman is about 1,645 words of round trip. A job with one million input tokens and one million output tokens comes to 467,396 toman in total. Filling this model's 262,144-token window costs 18,152 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "stepfun/step-5-preview", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.7× cheaper (608 against 1,665 toman). The context windows differ too: 262,144 against 1,048,576 tokens. Maximum answer length differs as well: 230,400 against 64,000 tokens. On parameters the other takes min_p.

StepFun has 3 models in our catalogue; the cheapest is Step 3.5 Flash at 180 toman per thousand words and the dearest Step 5 Preview at 1,665. Among the less common parameters it accepts logprobs, presence_penalty, reasoning_effort, repetition_penalty, top_k, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 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.

Provider's own description

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.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out263 toman
Summarising a ten-page document4,000 in + 600 out516 toman
Classifying a thousand short rows120,000 in + 20,000 out16,272 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?
69,244 toman per 1M input tokens and 398,152 toman per 1M output tokens; a 1,000-word request is around 608 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.