qwen3-14b

completions

On:deepinfraparasail

qwen3-14b

completions

Published by: qwenProviders:

deepinfra parasail

Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, programming, and logical inference, and a "non-thinking" mode for general-purpose conversation. The model is fine-tuned for 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	$0.06 / 1M tokens	$0.24 / 1M tokens
parasail	$0.06 / 1M tokens	$0.25 / 1M tokens

Released

Apr 28, 2025

Knowledge

Oct 30, 2024

Context

40960

Input

$0.06 / 1M tokens

Output

$0.24 / 1M tokens

Capabilities: tools

Accepts: text

Returns: text

Providers	Context	Input Price	Output Price	Input Formats	Output Formats
deepinfra	40960 tokens	$0.06 / 1M tokens	$0.24 / 1M tokens	text	text
parasail	40960 tokens	$0.06 / 1M tokens	$0.25 / 1M tokens	text	text

Released Apr 28, 2025Knowledge Cutoff: Oct 30, 2024

Access qwen3-14b through LangDB AI Gateway

Recommended

Integrate with qwen's qwen3-14b 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 qwen3-14b

Category Performance Scores:

Academia: Score 48.20 (Top 31% - Rank #107)
Finance: Score 45.85 (Top 36% - Rank #125)
Marketing: Score 38.12 (Top 61% - Rank #211)
Maths: Score 55.70 (Top 30% - Rank #104)
Programming: Score 41.90 (Top 25% - Rank #87)
Science: Score 60.78 (Top 41% - Rank #142)
Writing: Score 38.97 (Top 60% - Rank #207)

Overall Performance: 47.07325396825398 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
AIME	76.30	Top 12%	Mathematics
AA Coding Index	41.90	Top 25%	Programming
AAII	36.00	Top 29%	General
AA Math Index	55.70	Top 30%	Mathematics
GPQA	60.40	Top 41%	STEM (Physics, Chemistry, Biology)
HLE	4.30	Top 60%	General Knowledge
LiveCodeBench	52.30	Top 27%	Programming
MATH-500	96.10	Top 12%	Mathematics
MMLU-Pro	77.40	Top 31%	General Knowledge
SciCode	31.60	Top 35%	Scientific

GPQA Score: 60.40 - Graduate-level reasoning benchmark

Model Comparison:

Provider: deepinfra

Model Type: completions

Context Size: 40960 tokens

Comparing against 345 models in the database

Category Scores

Benchmark Tests

View Other Benchmarks

AIME

76.3

Mathematics

AA Coding Index

41.9

Programming

AAII

36.0

General

AA Math Index

55.7

Mathematics

GPQA

60.4

STEM (Physics, Chemistry, Biology)

HLE

4.3

General Knowledge

LiveCodeBench

52.3

Programming

MATH-500

96.1

Mathematics

MMLU-Pro

77.4

General Knowledge

SciCode

31.6

Scientific

Metric	AIME	AA Coding Index	AAII	AA Math Index	GPQA	HLE	LiveCodeBench	MATH-500	MMLU-Pro	SciCode
Score	76.3	41.9	36.0	55.7	60.4	4.3	52.3	96.1	77.4	31.6

Compare with Similar Models

gemini-2.5-pro-preview

grok-4

Code Examples

Integration samples and API usage

Code Samples for qwen3-14b

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="qwen3-14b",
    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: "qwen3-14b",
    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": "qwen3-14b",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: qwen3-14b

Provider: deepinfra

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

Create API Key

Related Models

Similar models from deepinfra

qwen3-14b

qwen3-14b

Access qwen3-14b 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

qwen3-14b by deepinfra - AI Model Details, Pricing, and Performance Metrics

qwen3-14b

qwen3-14b

Access qwen3-14b 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