Qwen3-Next-80B-A3B API Provider: Choose Smarter for Better AI
Qwen3-Next-80B-A3B API Provider
By Andrew Zheng •
Qwen3-Next-80B-A3B API Provider


Dec 13, 2025
Andrew Zheng
Qwen3-Next-80B-A3B is a cutting-edge reasoning model built on the latest Qwen3-Next framework, including Instruct and Thinking variants. It features 80 billion total parameters while activating only 3 billion during inference, delivering high efficiency and powerful performance that competes with significantly larger dense models.
In this article, We deep-dive into Qwen3-Next-80B-A3B, the MoE model delivering flagship performance with only 3B active parameters. This guide compares Infron, Clarifai, and Hyperbolic across performance, architecture, and pricing to help you find the most efficient solution for your AI workflow.
Qwen3-Next-80B-A3B is the first installment in the Qwen3-Next series, delivering state-of-the-art performance in multiple domains.
Specification | Details |
|---|---|
Parameters | 80B in total with 3B activated |
Architecture | Mixture-of-Experts |
Number of Layers | 48 |
Number of Experts | 512 |
Training Stage | Pretraining (15T tokens) & Post-training |
Context window | 262K natively |
License | Apache 2.0 |

High performance without extreme scale, giving you near-frontier accuracy without paying for 200B+-class models.
Strong general reasoning across math, coding, and mixed benchmarks, making it a reliable default model for broad workloads.
Top performance on Arena-Hard v2, providing strong real-world alignment with human preference tasks.
Cost-efficient upgrade for teams wanting a powerful instruction model without jumping to ultra-large parameter sizes.
Well-balanced across domains, suitable for chat, code assistance, analysis, and evaluation tasks with predictable quality.

Exceptional deliberate reasoning with standout scores in math (AIME25: 87.8) and long-form logic tasks.
Better chain-of-thought efficiency, letting you achieve deeper reasoning quality while keeping token usage lower than giant models.
Strong alternative to expensive reasoning models, outperforming or matching models like Gemini 2.5 Flash Thinking at a lower parameter scale.
Ideal for decision-making, multi-step problem solving, and scientific workflows, where accuracy and depth matter more than speed.
High performance across coding and evaluation, making it valuable for engineering, research, and enterprise cognitive tasks.
Context Length (Higher is better): A larger context length lets the model read and process more text in a single run, supporting deeper summaries, longer conversations, and more complex reasoning.
Token Cost (Lower is better): A lower token cost means each piece of text processed is cheaper, making frequent queries and large scale workloads more budget friendly.
Latency (Lower is better): Lower latency means the model replies faster, creating smoother interactions that are important for assistants, chat tools, and real time systems.
Throughput (Higher is better): Higher throughput means the model can handle more requests at the same time, ensuring stable performance even during heavy usage.
Provider | Context Length | Input/Output Price | Output Speed (Tokens per sec) | Latency | Function Calling | JSON Mode |
Infron | 262K | $0.15/$1.5 per 1M Tokens | 147 | 0.89s | ✅ | ✅ |
Clarifai | 262K | $1.09/$1.08 per 1M Tokens | 175 | 0.32s | ❌ | ❌ |
Hyperbolic | 262K | $0.3/$0.3 per 1M Tokens | 323 | 0.77s | ❌ | ✅ |
Infron delivers the best overall value: the lowest prices, solid speed, and full support for function calling and JSON Mode. It offers the most cost-efficient and developer-friendly option for real production use. Clarifai offers a high token prices and lack of key features make it expensive and less practical for real-world scaling. Hyperbolic provides fast output speed but higher input cost and missing function calling limit its flexibility compared to Infron.
Infron provides a simplified API scheme where developers can call AI models right away using an easy-to-use API. By offering affordable, ready-to-use multimodal models like Qwen3-Next-80B-A3B, GLM 4.6, Kimi K2 Thinking, DeepSeek V3.2 Exp, GPT-OSS, and others, it eliminates configuration hassles and lets you begin building without delay.
Infron provides a unified API that gives you access to hundreds of AI models through a single endpoint, while automatically handling fallbacks and selecting the most cost-effective options. Get started with just a few lines of code using your preferred SDK or framework.
Log in or sign up to your account and click on the Model Marketplace button


Begin your free trial to explore the capabilities of the selected model.
To authenticate with the API, Infron provides you with a new API key. Entering the “API Keys“ page, you can copy the API key as indicated in the image.
Install API using the package manager specific to your programming language.
Once the installation is complete, import the required libraries. Then, initialize the client with your API key to access the Infron LLM. The following snippet shows how Python users can work with the Chat Completions API.
from openai import OpenAI client = OpenAI( base_url="https://llm.onerouter.pro/v1", api_key="<API_KEY>", ) completion = client.chat.completions.create( model="qwen3-next-80b-a3b-instruct", messages=[ { "role": "user", "content": "What is the meaning of life?" } ] ) print(completion.choices[0].message.content)
It is a powerful large language model built on the Qwen3-Next architecture, offering advanced reasoning, strong coding ability, and exceptional performance while keeping inference efficient.
Yes. The Thinking variant is optimized for multi-step reasoning, problem solving, math, and complex analysis tasks.
Infron consistently delivers the lowest input cost and strong performance, making it the most cost-effective option for scaling real workloads. Try [Qwen3-Next-80B-A3B] for Free Now.
Qwen3-Next-80B-A3B is a cutting-edge reasoning model built on the latest Qwen3-Next framework, including Instruct and Thinking variants. It features 80 billion total parameters while activating only 3 billion during inference, delivering high efficiency and powerful performance that competes with significantly larger dense models.
In this article, We deep-dive into Qwen3-Next-80B-A3B, the MoE model delivering flagship performance with only 3B active parameters. This guide compares Infron, Clarifai, and Hyperbolic across performance, architecture, and pricing to help you find the most efficient solution for your AI workflow.
Qwen3-Next-80B-A3B is the first installment in the Qwen3-Next series, delivering state-of-the-art performance in multiple domains.
Specification | Details |
|---|---|
Parameters | 80B in total with 3B activated |
Architecture | Mixture-of-Experts |
Number of Layers | 48 |
Number of Experts | 512 |
Training Stage | Pretraining (15T tokens) & Post-training |
Context window | 262K natively |
License | Apache 2.0 |

High performance without extreme scale, giving you near-frontier accuracy without paying for 200B+-class models.
Strong general reasoning across math, coding, and mixed benchmarks, making it a reliable default model for broad workloads.
Top performance on Arena-Hard v2, providing strong real-world alignment with human preference tasks.
Cost-efficient upgrade for teams wanting a powerful instruction model without jumping to ultra-large parameter sizes.
Well-balanced across domains, suitable for chat, code assistance, analysis, and evaluation tasks with predictable quality.

Exceptional deliberate reasoning with standout scores in math (AIME25: 87.8) and long-form logic tasks.
Better chain-of-thought efficiency, letting you achieve deeper reasoning quality while keeping token usage lower than giant models.
Strong alternative to expensive reasoning models, outperforming or matching models like Gemini 2.5 Flash Thinking at a lower parameter scale.
Ideal for decision-making, multi-step problem solving, and scientific workflows, where accuracy and depth matter more than speed.
High performance across coding and evaluation, making it valuable for engineering, research, and enterprise cognitive tasks.
Context Length (Higher is better): A larger context length lets the model read and process more text in a single run, supporting deeper summaries, longer conversations, and more complex reasoning.
Token Cost (Lower is better): A lower token cost means each piece of text processed is cheaper, making frequent queries and large scale workloads more budget friendly.
Latency (Lower is better): Lower latency means the model replies faster, creating smoother interactions that are important for assistants, chat tools, and real time systems.
Throughput (Higher is better): Higher throughput means the model can handle more requests at the same time, ensuring stable performance even during heavy usage.
Provider | Context Length | Input/Output Price | Output Speed (Tokens per sec) | Latency | Function Calling | JSON Mode |
Infron | 262K | $0.15/$1.5 per 1M Tokens | 147 | 0.89s | ✅ | ✅ |
Clarifai | 262K | $1.09/$1.08 per 1M Tokens | 175 | 0.32s | ❌ | ❌ |
Hyperbolic | 262K | $0.3/$0.3 per 1M Tokens | 323 | 0.77s | ❌ | ✅ |
Infron delivers the best overall value: the lowest prices, solid speed, and full support for function calling and JSON Mode. It offers the most cost-efficient and developer-friendly option for real production use. Clarifai offers a high token prices and lack of key features make it expensive and less practical for real-world scaling. Hyperbolic provides fast output speed but higher input cost and missing function calling limit its flexibility compared to Infron.
Infron provides a simplified API scheme where developers can call AI models right away using an easy-to-use API. By offering affordable, ready-to-use multimodal models like Qwen3-Next-80B-A3B, GLM 4.6, Kimi K2 Thinking, DeepSeek V3.2 Exp, GPT-OSS, and others, it eliminates configuration hassles and lets you begin building without delay.
Infron provides a unified API that gives you access to hundreds of AI models through a single endpoint, while automatically handling fallbacks and selecting the most cost-effective options. Get started with just a few lines of code using your preferred SDK or framework.
Log in or sign up to your account and click on the Model Marketplace button


Begin your free trial to explore the capabilities of the selected model.
To authenticate with the API, Infron provides you with a new API key. Entering the “API Keys“ page, you can copy the API key as indicated in the image.
Install API using the package manager specific to your programming language.
Once the installation is complete, import the required libraries. Then, initialize the client with your API key to access the Infron LLM. The following snippet shows how Python users can work with the Chat Completions API.
from openai import OpenAI client = OpenAI( base_url="https://llm.onerouter.pro/v1", api_key="<API_KEY>", ) completion = client.chat.completions.create( model="qwen3-next-80b-a3b-instruct", messages=[ { "role": "user", "content": "What is the meaning of life?" } ] ) print(completion.choices[0].message.content)
It is a powerful large language model built on the Qwen3-Next architecture, offering advanced reasoning, strong coding ability, and exceptional performance while keeping inference efficient.
Yes. The Thinking variant is optimized for multi-step reasoning, problem solving, math, and complex analysis tasks.
Infron consistently delivers the lowest input cost and strong performance, making it the most cost-effective option for scaling real workloads. Try [Qwen3-Next-80B-A3B] for Free Now.
Qwen3-Next-80B-A3B API Provider
By Andrew Zheng •

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Seamlessly integrate Infron with just a few lines of code and unlock unlimited AI power.

Seamlessly integrate Infron with just a few lines of code and unlock unlimited AI power.

Seamlessly integrate Infron with just a few lines of code and unlock unlimited AI power.
