Qwen3-Next-80B-A3B-Instruct via Nvidia
Specifications
Context Window
262,144 tokens
Release Date
2024-12-01
Capabilities
Tool callingTemperature
Availability
Proprietary API
Model Overview
NVIDIA provides AI inference through their NIM (NVIDIA Inference Microservices) platform, offering optimized access to both NVIDIA-developed and popular open-source models on their GPU infrastructure.
Qwen3-Next-80B-A3B-Instruct is a qwen-family model by Nvidia with a 262k token context window and up to 16k output tokens. It is priced at $0.00/1M input tokens and $0.00/1M output tokens.
Key capabilities include: tool calling, temperature. It can call external tools and functions for agentic workflows.
Details
ProviderNvidia
Model IDqwen/qwen3-next-80b-a3b-instruct
Familyqwen
Release Date2024-12-01
Last Updated2025-09-05
Knowledge Cutoff2024-12
Context Window262,144 tokens
Max Output16,384 tokens
Input Cost / 1M$0.00
Output Cost / 1M$0.00
More models from Nvidia
Frequently Asked Questions
How much does Qwen3-Next-80B-A3B-Instruct cost to use?
Qwen3-Next-80B-A3B-Instruct is priced at $0.00/1M input tokens and $0.00/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.
What is a token and how does it relate to pricing?
A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.
Why are input and output tokens priced differently?
LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.
What is the context window of Qwen3-Next-80B-A3B-Instruct?
Qwen3-Next-80B-A3B-Instruct supports a context window of 262,144 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.
How accurate is this cost estimation?
This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.
How does Qwen3-Next-80B-A3B-Instruct pricing compare to other models?
You can compare Qwen3-Next-80B-A3B-Instruct with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.
What factors affect my total API cost?
Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.
How can my team use Qwen3-Next-80B-A3B-Instruct via API?
You can connect your own Nvidia API key and give your entire team access to Qwen3-Next-80B-A3B-Instruct through TypingMind Teams. It lets you build a unified AI workspace where team members can use Qwen3-Next-80B-A3B-Instruct and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.
