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WizardLM-2 8x22B API: toman pricing and code

microsoft/wizardlm-2-8x22b

json
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Input · per 1M tokens
179,878 toman
Output · per 1M tokens
179,878 toman
One 1,000-word request ≈
468 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

WizardLM-2 8x22B example: JSON output against a schema

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="microsoft/wizardlm-2-8x22b",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))

What is WizardLM-2 8x22B good for?

WizardLM-2 8x22B comes from Microsoft; in uttapen you reach it with the model id "microsoft/wizardlm-2-8x22b". It accepts up to 65,535 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 8,000 tokens. On price it sits in the "cheap" band — cheaper than 106 and dearer than 300 of the other paid models in the catalogue.

What it can do beyond plain text: it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Pricing is 179,878 toman per 1M input tokens and 179,878 per 1M output tokens. A 1,000-word round trip on WizardLM-2 8x22B lands near 468 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 Gemma 2 27B: WizardLM-2 8x22B works out roughly 1× cheaper, 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 214 thousand-word requests on WizardLM-2 8x22B, and every 1,000 toman is about 2,137 words of round trip. A job with one million input tokens and one million output tokens comes to 359,755 toman in total. Filling this model's 65,535-token window costs 11,788 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "microsoft/phi-4", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 5.9× dearer (468 against 79 toman). The context windows differ too: 65,535 against 16,384 tokens. Maximum answer length differs as well: 8,000 against 14,745 tokens. On parameters the other takes logit_bias, min_p, structured_outputs.

Microsoft has 2 models in our catalogue; the cheapest is Phi 4 at 79 toman per thousand words and the dearest WizardLM-2 8x22B at 468. Among the less common parameters it accepts repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, tool_choice, tools, 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 Nano Banana Pro (Gemini 3 Pro Image Preview) at 65,536 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema.

Provider's own description

WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models. It is...

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 out342 toman
Summarising a ten-page document4,000 in + 600 out827 toman
Classifying a thousand short rows120,000 in + 20,000 out25,183 toman

Frequently asked

How do I call WizardLM-2 8x22B 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 "microsoft/wizardlm-2-8x22b". Nothing else in your code changes.
What does WizardLM-2 8x22B cost in toman?
179,878 toman per 1M input tokens and 179,878 toman per 1M output tokens; a 1,000-word request is around 468 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does WizardLM-2 8x22B take?
Up to 65,535 tokens per request, roughly 49k words. A single answer can reach 8,000 tokens.
Can I stream WizardLM-2 8x22B's output?
Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.