qwen3-32b

completions

On:deepinfraparasailgroq

qwen3-32b

completions

Published by: qwenProviders:

deepinfra parasail groq

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.

Provider	Input	Output
deepinfra	$0.1 / 1M tokens	$0.3 / 1M tokens
parasail	$0.1 / 1M tokens	$0.5 / 1M tokens
groq	$0.29 / 1M tokens	$0.59 / 1M tokens

Released

Apr 29, 2025

Knowledge

Oct 31, 2024

Context

40960

Input

$0.1 / 1M tokens

Output

$0.3 / 1M tokens

Capabilities: tools

Accepts: text

Returns: text

Providers	Context	Input Price	Output Price	Input Formats	Output Formats	License
deepinfra	40960 tokens	$0.1 / 1M tokens	$0.3 / 1M tokens	text	text	Apache-2.0
parasail	40960 tokens	$0.1 / 1M tokens	$0.5 / 1M tokens	text	text	Apache-2.0
groq	131072 tokens	$0.29 / 1M tokens	$0.59 / 1M tokens	text	text	Apache-2.0

Released Apr 29, 2025Knowledge Cutoff: Oct 31, 2024

Access qwen3-32b through LangDB AI Gateway

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Integrate with qwen's qwen3-32b and 250+ other models through a unified API. Monitor usage, control costs, and enhance security.

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Available from 3 providers

Provider:

Benchmark Results for qwen3-32b

Category Performance Scores:

Academia: Score 52.75 (Top 30% - Rank #101)
Finance: Score 55.85 (Top 22% - Rank #74)
Marketing: Score 38.05 (Top 67% - Rank #225)
Maths: Score 73.00 (Top 22% - Rank #74)
Programming: Score 30.90 (Top 46% - Rank #155)
Science: Score 65.36 (Top 33% - Rank #111)
Writing: Score 39.88 (Top 65% - Rank #218)

Overall Performance: 50.82579365079365 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
AIME	80.70	Top 10%	Mathematics
AA Coding Index	30.90	Top 46%	Programming
AAII	38.70	Top 32%	General
AA Math Index	73.00	Top 22%	Mathematics
GPQA	66.80	Top 34%	STEM (Physics, Chemistry, Biology)
HLE	8.30	Top 17%	General Knowledge
LiveCodeBench	54.60	Top 32%	Programming
MATH-500	96.10	Top 17%	Mathematics
MMLU-Pro	79.80	Top 30%	General Knowledge
SciCode	35.40	Top 27%	Scientific

GPQA Score: 66.80 - Graduate-level reasoning benchmark

Model Comparison:

Provider: deepinfra

Model Type: completions

Context Size: 40960 tokens

Comparing against 335 models in the database

Category Scores

Benchmark Tests

View Other Benchmarks

AIME

80.7

Mathematics

AA Coding Index

30.9

Programming

AAII

38.7

General

AA Math Index

73.0

Mathematics

GPQA

66.8

STEM (Physics, Chemistry, Biology)

HLE

8.3

General Knowledge

LiveCodeBench

54.6

Programming

MATH-500

96.1

Mathematics

MMLU-Pro

79.8

General Knowledge

SciCode

35.4

Scientific

Metric	AIME	AA Coding Index	AAII	AA Math Index	GPQA	HLE	LiveCodeBench	MATH-500	MMLU-Pro	SciCode
Score	80.7	30.9	38.7	73.0	66.8	8.3	54.6	96.1	79.8	35.4

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Code Examples

Integration samples and API usage

Code Samples for qwen3-32b

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="qwen3-32b",
    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: "qwen3-32b",
    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": "qwen3-32b",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: qwen3-32b

Provider: deepinfra

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

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qwen3-32b by deepinfra - AI Model Details, Pricing, and Performance Metrics

qwen3-32b

qwen3-32b

Access qwen3-32b through LangDB AI Gateway

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

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