R1 API: toman pricing and code

What is R1 good for?

R1 comes from DeepSeek; in uttapen you reach it with the model id "deepseek/deepseek-r1". It accepts up to 64,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,000 tokens. On price it sits in the "mid-range" band — cheaper than 159 and dearer than 224 of the other paid models in the catalogue.

Capabilities available on this id: 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 1,440 toman per 1,000-word exchange (242,354 in, 865,548 out, per 1M tokens). 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 Kimi K2 0905, and R1 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 69 thousand-word requests on R1, and every 1,000 toman is about 694 words of round trip. A job with one million input tokens and one million output tokens comes to 1,107,902 toman in total. Filling this model's 64,000-token window costs 15,511 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "deepseek/deepseek-r1-0528", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 20% more expensive (1,440 against 1,193 toman). The context windows differ too: 64,000 against 163,840 tokens. Maximum answer length differs as well: 16,000 against 32,768 tokens. On parameters the other takes logit_bias, logprobs, min_p, structured_outputs.

DeepSeek has 18 models in our catalogue; the cheapest is DeepSeek V4.1 Flash (batch) at 90 toman per thousand words and the dearest DeepSeek Pro Latest at 3,647. Among the less common parameters it accepts presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support 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 Nano Banana Pro (Gemini 3 Pro Image Preview) at 65,536 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema.