Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B Instruct is an 80-billion-parameter hybrid Transformer-Mamba model that activates only 3B parameters per token, delivering high inference throughput over dense alternatives at a native context window of 262.1K tokens.
View API reference- Input and output price
- Prices from: Input $0.09, Output $1.10, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'alibaba/qwen3-next-80b-a3b-instruct', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out Qwen3 Next 80B A3B Instruct by Alibaba Cloud. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B Instruct
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Getting started
Call Qwen3 Next 80B A3B Instruct through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.
Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-next-80b-a3b-instruct', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same Qwen3 Next 80B A3B Instruct request in each API format AI Gateway supports.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-next-80b-a3b-instruct', system: 'You are a concise technical assistant.', prompt: 'Summarize the tradeoffs between static generation and SSR.', maxOutputTokens: 1024, temperature: 0.5, });
console.log(result.text);}
main().catch(console.error);Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | Yes | Model ID in the form creator/model, e.g. alibaba/qwen3-next-80b-a3b-instruct. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. Qwen3 Next 80B A3B Instruct supports up to 262,114 output tokens. |
providerOptions | Record<string, JSONValue> | No | AI Gateway routing options under gateway, plus any provider-native options under the provider’s own namespace — see the table below. |
Input limits
| Input | Formats | Sources | Max count | Max size | Limits |
|---|---|---|---|---|---|
| Text | — | — | — | — | Prompt and response share the 262K-token context window |
Provider options
Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.
Learn more in the AI SDK alibaba provider docs.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-next-80b-a3b-instruct', prompt: 'Why is the sky blue?', providerOptions: { gateway: { only: ['alibaba', 'novita'], }, }, });
console.log(result.text);}
main().catch(console.error);These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.
| Parameter | Type | Required | Description |
|---|---|---|---|
providerOptions.gateway.only | string[] | No | Restrict routing to these provider slugs. Requests fail over only within the listed providers. |
providerOptions.gateway.order | string[] | No | Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks. |
providerOptions.gateway.sort | 'cost' | 'ttft' | 'tps' | No | Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order. |
providerOptions.gateway.zeroDataRetention | boolean | No | Route only to providers with a zero-data-retention policy for this model. |
Routing across providers
AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.
Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.
Tool calling
Expose tools the model can call. Define each tool’s inputs with a Zod schema.
import { generateText, tool } from 'ai';import { z } from 'zod';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-next-80b-a3b-instruct', prompt: 'What is the weather in San Francisco?', tools: { getWeather: tool({ description: 'Get the current weather for a location', inputSchema: z.object({ location: z.string() }), execute: async ({ location }) => ({ location, temperatureC: 18 }), }), }, });
console.log(result.text);}
main().catch(console.error);Copy link to headingMore models by Alibaba Cloud
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| 991K | 4.0 s | 50 tps | $2/M | $6/M | Read$0.25/M Write$2.50/M | — | +2 | | 09/01/2026 | |||||
| 991K | 1.1 s | 96 tps | $0.16/M | $0.47/M | Read$0.02/M Write$0.20/M | — | +2 | | 08/26/2026 | |||||
| 1M | 0.6 s | 133 tps | $0.10/M | $0.40/M | Read$0.01/M Write$0.63/M | — | +1 |   +3 | 08/14/2026 | |||||
| 1M | 2.5 s | 160 tps | $2/M | $6/M | Read$0.25/M Write$2.50/M | — | +1 |  | 08/02/2026 | |||||
| 991K | 2.5 s | 140 tps | $0.03/M+2 more | $0.13/M+2 more | Read$0.006/M Write$0.04/M | — | +2 | | 07/28/2026 | |||||
| 1M | 2.0 s | 58 tps | $0.40/M+1 more | $1.60/M+1 more | Read$0.08/M Write$0.50/M | — | +2 | | 06/02/2026 |
Copy link to headingAbout Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B Instruct introduces a Hybrid Transformer-Mamba architecture that alternates between Gated DeltaNet (a linear attention mechanism) and standard Gated Attention within a 48-layer, 512-expert MoE stack. The layout follows a 12-block repeating pattern: three Gated DeltaNet + MoE layers followed by one Gated Attention + MoE layer. This design is purpose-built for ultra-long-context efficiency: linear attention handles the vast majority of layers at sub-quadratic cost, while sparse Gated Attention layers maintain the precision needed for complex cross-token reasoning.
With only 10 of 512 experts activated per token (plus one shared expert), Qwen3 Next 80B A3B Instruct achieves a sparse activation profile. Combined with Multi-Token Prediction during inference, this delivers notably higher throughput over comparable 32B dense models on sequences of 32K tokens or longer, a meaningful operational advantage for workloads that process long documents or transcripts at scale. See live metrics on this page for current throughput. The Instruct variant is tuned for direct instruction following and doesn't generate thinking traces (that variant is Qwen3-Next-80B-A3B-Thinking).
On the 1M RULER benchmark for extreme-length context, Qwen3 Next 80B A3B Instruct scores 80.3% accuracy, and its context of 262.1K tokens is extensible to approximately one million tokens via YaRN rope scaling. On knowledge benchmarks, it scores 80.6 on MMLU-Pro and 82.7 on Arena-Hard v2, tracking competitively with models that require far more compute per token.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: Providers vary in their support for ultra-long context windows; confirm that your selected provider can handle requests approaching 262.1K tokens before deploying at scale.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use Qwen3 Next 80B A3B Instruct
Best for
- High-throughput document pipelines: Workloads where long context and inference speed must coexist
- Long-document professional workloads: Legal, financial, or research tasks that regularly process 100K+ token documents without needing reasoning traces
- Long-session conversational assistants: Summarization and chat services that require reliable instruction following across very long sessions
- Multi-document synthesis: Tasks where entire reports must remain in-context simultaneously
- Cost-sensitive deployments: Production traffic that can't afford dense model pricing at high query volume
Consider alternatives when
- Explicit chain-of-thought needed: Hard math or coding problems are better served by Qwen3-Next-80B-A3B-Thinking
- Short-context exchanges: The architectural advantages for long sequences don't apply to short prompts
- Multimodal input required: This model processes text only; use a vision-language model for images or video
- Narrow-task accuracy ceiling: Maximum benchmark accuracy on a specific task can outweigh throughput efficiency gains
Copy link to headingConclusion
Qwen3 Next 80B A3B Instruct fits production deployments that combine long context with high throughput requirements. Its Hybrid Transformer-Mamba architecture delivers the efficiency of linear attention without surrendering the precision of sparse full attention, making it practical for document-scale workloads that would otherwise require expensive dense models.
Copy link to headingFrequently Asked Questions
What does "80B-A3B" mean in this model's name?
"80B" refers to 80 billion total parameters in the MoE pool; "A3B" indicates that approximately 3 billion parameters are activated per token. Only 10 of 512 experts fire for each token, giving the model a sparse activation profile.
How does Hybrid Transformer-Mamba architecture affect performance on long contexts?
The architecture alternates Gated DeltaNet (linear attention) with sparse Gated Attention. Linear attention scales sub-quadratically with sequence length, enabling the model to process sequences of 262.1K tokens with significantly lower compute than a fully quadratic attention model.
What is the throughput advantage over a dense model?
On sequences of 32K tokens or longer, Qwen3 Next 80B A3B Instruct achieves notably higher throughput than a comparable Qwen3-32B dense model thanks to its sparse MoE activation and Multi-Token Prediction. See live metrics on this page for current throughput.
Does this model support a thinking or reasoning mode?
No. The Instruct variant is optimized for direct instruction following without thinking traces. The Qwen3-Next-80B-A3B-Thinking variant provides reasoning mode.
What is the maximum context length?
The native context is 262.1K tokens. Using YaRN rope scaling, this can be extended to approximately one million tokens. The model achieves 80.3% accuracy on the 1M RULER extreme-context benchmark.
What benchmarks has this model been evaluated on?
Key scores include MMLU-Pro (80.6), MMLU-Redux (90.9), GPQA (72.9), AIME25 (69.5), LiveCodeBench (56.6), Arena-Hard v2 (82.7), and BFCL-v3 (70.3) for function calling.
Is Multi-Token Prediction supported?
Yes. Multi-Token Prediction further accelerates inference beyond the baseline throughput gains from the sparse MoE architecture.
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