Qwen3 14B

by Alibaba

Qwen3 14B is Alibaba's April 2025 dense model with switchable hybrid reasoning: a thinking mode that exposes step-by-step logic for math, coding, and logical problems, and a non-thinking mode for fast direct responses. Trained on a large multilingual corpus across many languages, it combines reasoning strength with agentic tool use. A 40K context window supports extended reasoning and conversation history, and the model improves on the Qwen2.5 line for mathematics, code generation, and commonsense reasoning while staying lighter than the 235B MoE variant. Released under Apache 2.0, it fits workloads wanting optional visible reasoning chains and cost-efficient agentic systems with tool integration.

Call Qwen3 14B on Opper with the OpenAI SDK. Sign up without a credit card. Get started

Key info

Input
Output
Features
Context window
41K
Max output
41K
Input price
$0.12 /1M
Output price
$0.24 /1M
  • EU residency available
  • US residency available
  • Zero data retention on pay-as-you-go
  • No training by default
  • GDPR DPA available

Available routes

Qwen3 14B runs on 2 different routes through the Opper gateway. Compare residency, zero data retention and training posture at a glance, with full data-handling detail per route below.

ProviderRegionZero data retentionTrainingInputOutputCache read
USNo$0.12$0.24Input price
EUNo$0.10$0.22Input price

Uptime and availability

One of Qwen3 14B's 2 routes is on the monitoring board, so the figure below is that provider's availability rather than the model's.

100%route uptime, last 30 days
Its other routes aren't measured here yet.

Name a second model in the same request and the gateway tries it on retriable errors, so a busy hour never has to reach your users. Set up a fallback chain.

Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. 1 further route is not on the board yet. See uptime for every provider Opper monitors.

Data handling per route

Each route hosting Qwen3 14B has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

DeepInfra United States🇺🇸

Zero data retention: nothing is logged, held for abuse monitoring or used for training. No training on customer data. US; unknown; DPA available.

Zero data retention
Yes. Nothing is logged, held for abuse monitoring or used for training.
Training
No training on customer data.
Logging
None
Abuse monitoring
No classifier
GDPR DPA
DPA available
Transfer mechanism
unknown

Nextbit Spain🇪🇸

Content is retained: provider logging, 90-day retention. No training on customer data. EU; SCCs; DPA available.

Zero data retention
Retained: provider logging, 90-day retention.
Training
No training on customer data.
Logging
Other (90-day retention)
Abuse monitoring
On by default, holds flagged content
Caching
Content cached for replay
GDPR DPA
DPA available
Transfer mechanism
SCCs

Benchmarks

Independent benchmark scores — composite indices for reasoning, coding, and math, plus individual eval scores where available.

Intelligence Index8.2
Coding Index13.8
Math Index55.7
Reasoning & knowledge
MMLU-Pro
77%
GPQA Diamond
60%
Humanity's Last Exam
5%
Long-context reasoning
0%
Coding
LiveCodeBench
52%
SciCode
31%
Agentic & tool use
Terminal-Bench Hard
4%
τ²-Bench Telecom
35%
Math & instruction following
AIME 2025
56%
IFBench
41%

Get started

Call Qwen3 14B through the Opper gateway with one API key. Opper is drop-in compatible with the OpenAI, Anthropic and Google AI SDKs.

Qwen3 14B is a premium model on Opper. Sign up needs no credit card: you get an API key straight away and the free models work in the playground and the API. Add a card to use premium models, pay-as-you-go with no minimum.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.OPPER_API_KEY,
baseURL: "https://api.opper.ai/v3/compat",
});
const completion = await client.chat.completions.create({
model: "qwen3-14b",
messages: [{ role: "user", content: "Hello" }],
});
console.log(completion.choices[0].message.content);

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