Step 3.5 Flash API: toman pricing and code
stepfun/step-3.5-flash
toolsreasoningYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Step 3.5 Flash example: a multi-step problem with reasoning
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="stepfun/step-3.5-flash",
messages=[{"role": "user", "content": (
"A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
"If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
"Work through it step by step and give just the two numbers at the end."
)}],
reasoning_effort="medium", # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "stepfun/step-3.5-flash",
"messages": [{"role": "user", "content": "120 and 85 daily orders split across two warehouses; work through it step by step."}],
"reasoning": {"effort": "medium"}
}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "stepfun/step-3.5-flash",
messages: [{ role: "user", content: "120 and 85 daily orders are split between two warehouses; work through it step by step and give just the two numbers at the end." }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.completion_tokens_details);What is Step 3.5 Flash good for?
Step 3.5 Flash is one of StepFun's models. Put "stepfun/step-3.5-flash" in the model field and the rest of your code stays as it is. Step 3.5 Flash keeps 262,144 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 65,536 tokens. On price it sits in the "very cheap" band — cheaper than 61 and dearer than 345 of the other paid models in the catalogue.
What you get on top of text in, text out: it supports tool calling, so it can invoke your own functions with valid arguments; 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.
Input runs at 29,013 toman per 1M tokens and output at 87,038 — output costs 3× input, so trimming the answer saves more than trimming the prompt. 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 Voxtral Small 24B 2507: Step 3.5 Flash works out roughly 1× more expensive, and its context window is larger. 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 662 thousand-word requests on Step 3.5 Flash, and every 1,000 toman is about 6,623 words of round trip. A job with one million input tokens and one million output tokens comes to 116,050 toman in total. Filling this model's 262,144-token window costs 7,605 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 top_k — all through the standard request body. It does not support response_format, seed, structured_outputs, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, analysing a long document or codebase in one request.
Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....
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 | 78 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 168 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 5,222 toman |
Frequently asked
- How do I call Step 3.5 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-3.5-flash". Nothing else in your code changes.
- What does Step 3.5 Flash cost in toman?
- 29,013 toman per 1M input tokens and 87,038 toman per 1M output tokens; a 1,000-word request is around 151 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Step 3.5 Flash take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 65,536 tokens.
- Does Step 3.5 Flash support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape.