mistral-small-3.2-24b-instruct

completions

On:deepinfraparasail

mistral-small-3.2-24b-instruct

completions

Published by: mistralaiProviders:

deepinfra parasail

Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on WildBench and Arena Hard, reduces infinite generations, and delivers gains in tool use and structured output tasks. It supports image and text inputs with structured outputs, function/tool calling, and strong performance across coding (HumanEval+, MBPP), STEM (MMLU, MATH, GPQA), and vision benchmarks (ChartQA, DocVQA).

Provider	Input	Output
deepinfra	$0.05 / 1M tokens	$0.1 / 1M tokens
parasail	$0.09 / 1M tokens	$0.3 / 1M tokens

Released

Sep 17, 2024

Knowledge

Mar 21, 2024

Context

128K

Input

$0.05 / 1M tokens

Output

$0.1 / 1M tokens

Accepts: text, image

Returns: text

Providers	Context	Input Price	Output Price	Input Formats	Output Formats
deepinfra	128K tokens	$0.05 / 1M tokens	$0.1 / 1M tokens	textimage	text
parasail	131072 tokens	$0.09 / 1M tokens	$0.3 / 1M tokens	textimage	text

Released Sep 17, 2024Knowledge Cutoff: Mar 21, 2024

Access mistral-small-3.2-24b-instruct through LangDB AI Gateway

Recommended

Integrate with mistralai's mistral-small-3.2-24b-instruct and 250+ other models through a unified API. Monitor usage, control costs, and enhance security.

Unified API

Cost Optimization

Enterprise Security

Get Started Now

Free tier available • No credit card required

Instant Setup

99.9% Uptime

10,000+Monthly Requests

Available from 2 providers

Provider:

Benchmark Results for mistral-small-3.2-24b-instruct

Category Performance Scores:

Academia: Score 25.55 (Top 75% - Rank #259)
Marketing: Score 41.08 (Top 49% - Rank #170)
Programming: Score 14.80 (Top 79% - Rank #273)
Science: Score 40.10 (Top 77% - Rank #266)
Writing: Score 38.74 (Top 61% - Rank #211)

Overall Performance: 32.053000000000004 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
AIME	6.30	Top 72%	Mathematics
AA Coding Index	14.80	Top 79%	Programming
AAII	13.00	Top 79%	General
GPQA	38.10	Top 74%	STEM (Physics, Chemistry, Biology)
HLE	4.30	Top 60%	General Knowledge
LiveCodeBench	14.10	Top 78%	Programming
MATH-500	56.30	Top 75%	Mathematics
MMLU-Pro	52.90	Top 76%	General Knowledge
SciCode	15.60	Top 76%	Scientific

GPQA Score: 38.10 - Graduate-level reasoning benchmark

Model Comparison:

Provider: deepinfra

Model Type: completions

Context Size: 128000 tokens

Comparing against 345 models in the database

Category Scores

Benchmark Tests

View Other Benchmarks

AIME

6.3

Mathematics

AA Coding Index

14.8

Programming

AAII

13.0

General

GPQA

38.1

STEM (Physics, Chemistry, Biology)

HLE

4.3

General Knowledge

LiveCodeBench

14.1

Programming

MATH-500

56.3

Mathematics

MMLU-Pro

52.9

General Knowledge

SciCode

15.6

Scientific

Metric	AIME	AA Coding Index	AAII	GPQA	HLE	LiveCodeBench	MATH-500	MMLU-Pro	SciCode
Score	6.3	14.8	13.0	38.1	4.3	14.1	56.3	52.9	15.6

Compare with Similar Models

gemini-2.5-pro-preview

grok-4

Code Examples

Integration samples and API usage

Code Samples for mistral-small-3.2-24b-instruct

Python SDK Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai/projects/<your_project_id>",
    api_key="<your_api_key>"
)

response = client.chat.completions.create(
    model="mistral-small-3.2-24b-instruct",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

TypeScript SDK Example:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: "https://api.us-east-1.langdb.ai/projects/<your_project_id>",
    apiKey: "<your_api_key>"
});

const response = await client.chat.completions.create({
    model: "mistral-small-3.2-24b-instruct",
    messages: [
        { role: "user", content: "Hello, how are you?" }
    ]
});

console.log(response.choices[0].message.content);

cURL Example:

curl -X POST "https://api.us-east-1.langdb.ai/projects/<your_project_id>/v1/chat/completions" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-small-3.2-24b-instruct",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: mistral-small-3.2-24b-instruct

Provider: deepinfra

API Endpoint: $https://api.us-east-1.langdb.ai

Create API Key

Related Models

Similar models from deepinfra

mistral-small-3.2-24b-instruct

mistral-small-3.2-24b-instruct

Access mistral-small-3.2-24b-instruct through LangDB AI Gateway

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

Related Models

deepseek-chat-v3-0324

deepseek-chat-v3.1

deepseek-prover-v2

DeepSeek-R1

deepseek-r1-0528

DeepSeek-R1-Distill-Llama-70B

mistral-small-3.2-24b-instruct by deepinfra - AI Model Details, Pricing, and Performance Metrics

mistral-small-3.2-24b-instruct

mistral-small-3.2-24b-instruct

Access mistral-small-3.2-24b-instruct through LangDB AI Gateway

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

Related Models

deepseek-chat-v3-0324

deepseek-chat-v3.1

deepseek-prover-v2

DeepSeek-R1

deepseek-r1-0528

DeepSeek-R1-Distill-Llama-70B