> ## Documentation Index > Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt > Use this file to discover all available pages before exploring further. # Classify API > Score prompts you format yourself with the Qwen3 reranker `POST https://api.pinference.ai/api/v1/classify` runs [Qwen3 Reranker 0.6B](https://docs.primeintellect.ai/inference/models/qwen3-reranker) on prompts you format yourself. For each prompt it returns the model's probability of answering "yes" over "no", and the logit behind it. The [Rerank API](https://docs.primeintellect.ai/api-reference/inference-rerank.md) builds the same prompts from one query and a list of documents. Use classify to score pairs with different queries in one request, to write your own question, or to work with logits. ## Prompt format Qwen publishes this prompt for the model. `{instruction}`, `{query}` and `{document}` are your text: ```text theme={null} <|im_start|>system Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|> <|im_start|>user : {instruction} : {query} : {document}<|im_end|> <|im_start|>assistant ``` The prompt ends with two newlines after ``. The Rerank API uses exactly this template, with the default instruction below. This function builds the same string: ```python theme={null} DEFAULT_INSTRUCTION = "Given a web search query, retrieve relevant passages that answer the query" def format_pair(query: str, document: str, instruction: str = DEFAULT_INSTRUCTION) -> str: return ( "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query " 'and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n' "<|im_start|>user\n" f": {instruction}\n: {query}\n: {document}" "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" ) ``` The API sends each prompt to the model unchanged and reads the model's preference for "yes" over "no" at the prompt's last token. It doesn't check the format. A prompt that asks a yes-or-no question and ends with the exact assistant suffix, `<|im_start|>assistant\n\n\n\n\n`, gets a meaningful score. Before the suffix you can change the system message, the field labels and the instruction. A prompt without the suffix still returns a score, but that score has no defined meaning. ## Classify prompts Send one prompt as a string, or up to 1,000 as an array. This example uses `format_pair` from above: ```python theme={null} import os import requests prompts = [ format_pair("Which planet is known as the Red Planet?", "Mars is often referred to as the Red Planet."), format_pair("Which planet is known as the Red Planet?", "Venus is often called Earth's twin because of its similar size."), ] response = requests.post( "https://api.pinference.ai/api/v1/classify", headers={"Authorization": f"Bearer {os.environ['PRIME_API_KEY']}"}, json={"model": "qwen/qwen3-reranker-0.6b", "input": prompts}, timeout=120, ) response.raise_for_status() for item in response.json()["data"]: print(item["index"], item["label"], round(item["probs"][0], 3)) ``` The request returns: ```json theme={null} { "id": "req_...", "object": "list", "created": 1794009600, "model": "qwen/qwen3-reranker-0.6b", "data": [ {"index": 0, "label": "yes", "probs": [0.997], "num_classes": 1, "logit": 5.73}, {"index": 1, "label": "no", "probs": [0.025], "num_classes": 1, "logit": -3.65} ], "usage": {"prompt_tokens": 183, "total_tokens": 183, "completion_tokens": 0} } ``` With curl, the prompt is a JSON string with its newlines and quotes escaped: ```bash theme={null} export PRIME_API_KEY="your-api-key" curl https://api.pinference.ai/api/v1/classify \ -H "Authorization: Bearer $PRIME_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen/qwen3-reranker-0.6b", "input": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n: Given a web search query, retrieve relevant passages that answer the query\n: Which planet is known as the Red Planet?\n: Mars is often referred to as the Red Planet.<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" }' ``` To charge a team, add `-H "X-Prime-Team-ID: your-team-id"` to each request. See [team billing](https://docs.primeintellect.ai/inference/overview.md#team-billing). ## Request body | Field | Type | Description | | - | - | - | | `model` | string | `qwen/qwen3-reranker-0.6b`. | | `input` | string or array of strings | 1 to 1,000 prompts. Each must be a non-empty string of at most 32,768 tokens. A longer prompt is refused with a 400, and nothing is truncated. | The whole request body can be at most 16 MiB. ## Response | Field | Description | | - | - | | `id` | The request's ID, the same as the `X-Inference-Id` response header. Share it with support to trace a request. | | `object` | Always `list`. | | `created` | When the response was made, in Unix seconds. | | `model` | The model ID from the request. | | `data` | One entry per prompt, in request order. | | `data[].index` | The prompt's position in `input`, counting from 0. | | `data[].probs` | A one-element array with the probability, from 0 to 1, of "yes" over "no". | | `data[].label` | `yes` when that probability is at least 0.5, otherwise `no`. | | `data[].logit` | The model's raw score. The probability is its sigmoid. | | `data[].num_classes` | Always 1. The model has one output. | | `usage.prompt_tokens` | The tokens the model read, summed over all prompts. | | `usage.total_tokens` | The same as `prompt_tokens`. | | `usage.completion_tokens` | Always 0. Nothing is generated. | Pick your own threshold for decisions. At a probability of 0.5 the model is undecided between "yes" and "no". When two probabilities both round to 1.0, compare their logits to order them. ## Usage and billing Classification is billed on input tokens, `usage.prompt_tokens`, at \$0.20 per million. There is no output charge. ## Errors Most errors return an `error` object with `message`, `type` and `code`, as the [Chat Completions API](https://docs.primeintellect.ai/api-reference/inference-chat-completions.md) does. A 401, a 404, or a 503 while authentication is unavailable returns a `detail` message instead. | Status | Cause | | - | - | | 400 | `Invalid classify request (input).` means `input` is missing or empty, holds an empty string or a non-string, or holds a prompt longer than 32,768 tokens. `Invalid classify request (too_many_inputs).` means more than 1,000 prompts. | | 401 | The API key is missing or invalid. | | 402 | The billed account has insufficient funds. See [troubleshooting](https://docs.primeintellect.ai/inference/troubleshooting.md). | | 404 | The request didn't name a reranker, or its body isn't JSON. A misspelled model ID, a missing `model` and a non-JSON body all return a `detail` of `Not Found`. This check comes before the API key check, so a wrong model with a bad key returns 404. | | 413 | The request body is larger than 16 MiB. | | 429 | The model has no free capacity. Retry after the number of seconds in the `Retry-After` header. | | 502 or 503 | The model failed to answer. Retry the request. | ## vLLM compatibility The request and response follow vLLM's classification API, with `logit` added. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.