Mistral Medium 3.5 (batch) API: toman pricing and code
mistralai/mistral-medium-3-5:batch
visiontoolsreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Mistral Medium 3.5 (batch) 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": "mistralai/mistral-medium-3-5:batch",
"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="mistralai/mistral-medium-3-5:batch",
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: "mistralai/mistral-medium-3-5:batch",
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 Mistral Medium 3.5 (batch) good for?
The id for Mistral Medium 3.5 (batch) in our API is "mistralai/mistral-medium-3-5:batch", served from Mistral. Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 209,715 tokens. On price it sits in the "mid-range" band — cheaper than 257 and dearer than 149 of the other paid models in the catalogue.
What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input; 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.
Pricing is 217,594 toman per 1M input tokens and 1,087,969 per 1M output tokens. A 1,000-word round trip on Mistral Medium 3.5 (batch) lands near 1,697 toman. 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 Gemini 3.6 Flash: Mistral Medium 3.5 (batch) 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 59 thousand-word requests on Mistral Medium 3.5 (batch), and every 1,000 toman is about 589 words of round trip. A job with one million input tokens and one million output tokens comes to 1,305,563 toman in total. Filling this model's 262,144-token window costs 57,041 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "mistralai/mistral-medium-3-5", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 2× cheaper (1,697 against 3,394 toman).
Mistral has 20 models in our catalogue; the cheapest is Mistral Nemo at 18 toman per thousand words and the dearest Mistral Medium 3.5 at 3,394. Among the less common parameters it accepts reasoning_effort — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.
Good fits: product chatbots and internal assistants, where cost and quality have to balance, 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.
Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...
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 | 762 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,523 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 47,871 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | mistralai/mistral-medium-3-5 | 2,175,938 | 262,144 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call Mistral Medium 3.5 (batch) 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 "mistralai/mistral-medium-3-5:batch". Nothing else in your code changes.
- What does Mistral Medium 3.5 (batch) cost in toman?
- 217,594 toman per 1M input tokens and 1,087,969 toman per 1M output tokens; a 1,000-word request is around 1,697 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Mistral Medium 3.5 (batch) take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 209,715 tokens.
- Does Mistral Medium 3.5 (batch) 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.