Command R+ (08-2024) API: toman pricing and code
cohere/command-r-plus-08-2024
toolsjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Command R+ (08-2024) 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="cohere/command-r-plus-08-2024",
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))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: "cohere/command-r-plus-08-2024",
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: {
name: "ticket",
strict: true,
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,
},
},
},
});
console.log(JSON.parse(resp.choices[0].message.content));curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "cohere/command-r-plus-08-2024",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Command R+ (08-2024) good for?
Command R+ (08-2024) is one of Cohere's models. Put "cohere/command-r-plus-08-2024" in the model field and the rest of your code stays as it is. Command R+ (08-2024) keeps 128,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 4,000 tokens. On price it sits in the "mid-range" band — cheaper than 322 and dearer than 84 of the other paid models in the catalogue.
What it can do beyond plain text: 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
Pricing is 725,313 toman per 1M input tokens and 2,901,250 per 1M output tokens. A 1,000-word round trip on Command R+ (08-2024) lands near 4,715 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 GPT Audio: Command R+ (08-2024) works out roughly 1× more expensive, and its context window is the same size. 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 21 thousand-word requests on Command R+ (08-2024), and every 1,000 toman is about 212 words of round trip. A job with one million input tokens and one million output tokens comes to 3,626,563 toman in total. Filling this model's 128,000-token window costs 92,840 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "cohere/command-r-08-2024", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 16.7× dearer (4,715 against 283 toman).
Cohere has 5 models in our catalogue; the cheapest is Command R7B (12-2024) at 71 toman per thousand words and the dearest Command R+ (08-2024) at 4,715. Among the less common parameters it accepts top_k — all through the standard request body. It does not support include_reasoning, reasoning, 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 Nova Micro 1.0 at 128,000 tokens.
Good fits: product chatbots and internal assistants, where cost and quality have to balance, agents that reach out to APIs and databases, extracting data against a fixed schema.
command-r-plus-08-2024 is an update of the [Command R+](/models/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint...
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 | 2,248 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 4,642 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 145,063 toman |
Frequently asked
- How do I call Command R+ (08-2024) 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 "cohere/command-r-plus-08-2024". Nothing else in your code changes.
- What does Command R+ (08-2024) cost in toman?
- 725,313 toman per 1M input tokens and 2,901,250 toman per 1M output tokens; a 1,000-word request is around 4,715 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Command R+ (08-2024) take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 4,000 tokens.
- Does Command R+ (08-2024) support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.