How much does Llama 3.2 1B Instruct API cost on Routeway?

Llama 3.2 1B Instruct costs $0.15/1M input tokens on Routeway, offering competitive pricing compared to meta's direct rates.

Is Llama 3.2 1B Instruct compatible with OpenAI's API format?

Yes, Llama 3.2 1B Instruct on Routeway uses the same API format as OpenAI, making it easy to switch with zero code changes. Simply update your API endpoint and key.

What is the context window for Llama 3.2 1B Instruct?

Llama 3.2 1B Instruct supports a context window of 16,384 tokens, allowing for extensive conversations and document processing.

How do I get started with Llama 3.2 1B Instruct on Routeway?

Getting started is easy: 1) Create a Routeway account, 2) Generate your API key, 3) Use our OpenAI-compatible endpoint with Llama 3.2 1B Instruct as the model parameter.

Back to Models

Llama 3.2 1B Instruct

Available

Description

Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate efficiently in low-resource environments while maintaining strong task performance. Supporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models. Click here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md). Usage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).

At a Glance

Key pricing and model details available for this model.

Input price

$0.15

per 1M tokens

Output price

$0.07

per 1M tokens

Context window

16K

tokens

Hallucination rate

48.4%

Token Pricing

Token pricing normalized to per-million-token rates.

Input / 1M tokens

$0.15

Output / 1M tokens

$0.07

Cache Read / 1M tokens

Free

Token Pricing Details

Rates are shown per 1M tokens for easier comparison.

Input / 1M tokens	$0.15
Input unit	1M tokens
Output / 1M tokens	$0.07
Output unit	1M tokens
Cache Read / 1M tokens	Free
Cache Read unit	1M tokens

Feature Availability

Capabilities explicitly listed in the current payload.

LLM

Available

Yes

Vision

Not listed

Function calling

Not listed

Reasoning

Not listed

Supported Parameters

frequency_penalty

logit_bias

max_completion_tokens

presence_penalty

reasoning_effort

response_format

stop

temperature

tool_choice

tools

top_p

Artificial Analysis

Index scores currently reported for this model.

Intelligence Index

11.8

Coding Index

4.9

Math Index

4.3

Category Radar

Aggregated from the benchmark values present for reasoning, code, math, and accuracy.

Benchmark Breakdown

Detailed benchmark results drawn from the current payload.

Intelligence Index

index

Overall 'how smart' score for an AI, combining reasoning, math, coding, and knowledge.

11.8

Reported score

Coding Index

index

How well the model handles real programming tasks.

4.9

Reported score

Math Index

index

Composite score measuring mathematical reasoning and problem-solving.

4.3

Reported score

MMLU-Pro

reasoning

A broad and difficult knowledge-and-reasoning benchmark across many subjects.

47.6%

Reported score

GPQA

reasoning

Graduate-level science questions designed to be difficult to shortcut.

25.9%

Reported score

HLE

reasoning

A very hard expert-level exam across a wide range of subjects.

5.1%

Reported score

LiveCodeBench

code

Fresh programming tasks meant to test current coding ability.

11.6%

Reported score

SciCode

code

Coding tasks drawn from real scientific workflows.

13.2%

Reported score

MATH-500

math

A set of difficult competition-style math problems.

51.9%

Reported score

AIME

math

Advanced math competition questions.

7.7%

Reported score

AIME 2025

math

The 2025 AIME benchmark used to reduce data leakage concerns.

4.3%

Reported score

IFBench

accuracy

Measures how precisely the model follows detailed instructions.

28.6%

Reported score

LCR

accuracy

Tests long-context reasoning over large documents and conversations.

15.7%

Reported score

TerminalBench Hard

code

A harder coding-agent benchmark for complex multi-step terminal tasks.

0.8%

Reported score

Tau2

accuracy

Evaluates realistic agent behavior in tool-using support workflows.

16.4%

Reported score

Code Samples

Quick start with the Routeway API

import OpenAI from 'openai';

const openai = new OpenAI({
  baseURL: "https://api.routeway.ai/v1",
  apiKey: "<YOUR_API_KEY>",
});

async function main() {
  const completion = await openai.chat.completions.create({
    model: "llama-3.2-1b-instruct",
    messages: [
      {
        role: "user",
        content: "Explain quantum computing in simple terms"
      }
    ]
  });

  console.log(completion.choices[0].message);
}

main();