[Alibaba Cloud](/ai-gateway/models/labs/alibaba) # 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. Your use is subject to Alibaba Cloud's [Terms](https://www.alibabacloud.com/help/en/legal/latest/alibaba-cloud-international-website-product-terms-of-service-v-3-8-0) & [Privacy](https://www.alibabacloud.com/help/en/legal/latest/alibaba-cloud-international-website-privacy-policy) Policies. Tool Use [Use with AI Gateway](https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai%3Futm_source%3Dgateway-model-page%26utm_campaign%3Dai-gateway-models&title=Get+Started+with+Vercel+AI+Gateway) [View docs](https://vercel.com/docs/ai-gateway) AI SDKChat CompletionsMessagesResponses ``` 1import { streamText } from 'ai' 2 3const result = streamText({ 4 model: 'alibaba/qwen3-next-80b-a3b-instruct', 5 prompt: 'Why is the sky blue?' 6}) ``` [Read docs](https://vercel.com/docs/ai-gateway/sdks-and-apis/ai-sdk) [Overview](/ai-gateway/models/qwen3-next-80b-a3b-instruct) [API](/ai-gateway/models/qwen3-next-80b-a3b-instruct/api) [About](/ai-gateway/models/qwen3-next-80b-a3b-instruct/about) [Providers](/ai-gateway/models/qwen3-next-80b-a3b-instruct/providers) [Throughput](/ai-gateway/models/qwen3-next-80b-a3b-instruct/throughput) [Latency](/ai-gateway/models/qwen3-next-80b-a3b-instruct/latency) [Uptime](/ai-gateway/models/qwen3-next-80b-a3b-instruct/uptime) [Status](/ai-gateway/models/qwen3-next-80b-a3b-instruct/status) [Similar](/ai-gateway/models/qwen3-next-80b-a3b-instruct/similar) [FAQ](/ai-gateway/models/qwen3-next-80b-a3b-instruct/faq) ## [Copy link to heading](#playground)Playground 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. ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75)Qwen3 Next 80B A3B Instruct ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=96&q=75) Qwen3 Next 80B A3B Instruct ## [Copy link to heading](#providers)Providers Route requests across multiple providers. Copy a provider slug to set your preference. Visit the [docs](/docs/ai-gateway/provider-options) for more info. Using a provider means you agree to their terms, listed under Legal. | Provider | | --- | | Context | Max Output | Latency | Throughput | Input | Output | Cache | Web Search | Capabilities | ZDR | No Training | Free Tier | Release Date | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75) [Alibaba Cloud](/ai-gateway/models/providers/alibaba) Legal:[Terms](https://www.alibabacloud.com/help/en/legal/latest/alibaba-cloud-international-website-product-terms-of-service-v-3-8-0)•[Privacy](https://www.alibabacloud.com/help/en/legal/latest/alibaba-cloud-international-website-privacy-policy) | 131K | 33K | 0.4s | | $0.15/M | $1.20/M | | — | | | | | 09/11/2025 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![novita logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fnovita.png&w=48&q=75) [Novita AI](/ai-gateway/models/providers/novita) Legal:[Terms](https://novita.ai/legal/terms-of-service)•[Privacy](https://novita.ai/legal/privacy-policy) | 66K | 66K | 0.6s | | $0.15/M | $1.50/M | | — | | | | | 09/11/2025 | | | ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) [Google Vertex AI](/ai-gateway/models/providers/vertex) Legal:[Terms](https://cloud.google.com/terms/service-terms)•[Privacy](https://cloud.google.com/privacy) | 262K | 262K | 0.3s | 103tps | $0.15/M | $1.20/M | | — | | | | | 09/11/2025 | | | ![deepinfra logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fdeepinfra.png&w=48&q=75) [DeepInfra](/ai-gateway/models/providers/deepinfra) Legal:[Terms](https://deepinfra.com/terms)•[Privacy](https://deepinfra.com/privacy) | 262K | 33K | 0.3s | | $0.09/M | $1.10/M | | — | | | | | 09/11/2025 | | ## [Copy link to heading](#throughput)Throughput24 hours 1W 1D P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the [docs](https://vercel.com/docs/ai-gateway/models-and-providers/metrics) for more info. ## [Copy link to heading](#latency)Latency24 hours 1W 1D P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the [docs](https://vercel.com/docs/ai-gateway/models-and-providers/metrics) for more info. ## [Copy link to heading](#uptime)Uptime24 hours 1W 1D 1H Direct request success rate on AI Gateway and per-provider. Visit the [docs](https://vercel.com/docs/ai-gateway/models-and-providers/uptime) for more info. 1W 1D 1H ## [Copy link to heading](#more-models-by-alibaba-cloud)More models by Alibaba Cloud All Text Code | Model | | --- | | Context | Latency | Throughput | Input | Output | Cache | Web Search | Capabilities | Providers | ZDR | No Training | Free Tier | Release Date | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%20model%20logo.png&w=48&q=75) [alibaba/qwen3.8-flash](/ai-gateway/models/qwen3.8-flash) | 991K | 3.4s | 98tps | $0.16/M | $0.47/M | Read:$0.02/M Write:$0.20/M | — | +2 | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75) | | | | 08/26/2026 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%20model%20logo.png&w=48&q=75) [alibaba/qwen3.8-27b](/ai-gateway/models/qwen3.8-27b) | 1M | 0.6s | 136tps | $0.10/M | $0.40/M | Read:$0.01/M Write:$0.63/M | — | +1 | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75) ![deepinfra logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fdeepinfra.png&w=48&q=75) ![morph logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmorph.png&w=48&q=75) +2 | | | | 08/14/2026 | | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%20model%20logo.png&w=48&q=75) [alibaba/qwen3.8-2.4t-a95b](/ai-gateway/models/qwen3.8-2.4t-a95b) | 262K | 0.4s | 80tps | $1.65/M | $4.95/M | Read:$0.12/M Write:$2.50/M | — | | ![deepinfra logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fdeepinfra.png&w=48&q=75) ![fireworks logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Ffireworks.png&w=48&q=75) ![gmicloud logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgmicloud.png%3Fv%3D1785888489595&w=48&q=75) +4 | | | | 08/03/2026 | | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%20model%20logo.png&w=48&q=75) [alibaba/qwen3.8-max](/ai-gateway/models/qwen3.8-max) | 1M | 1.0s | 67tps | $2/M | $6/M | Read:$0.25/M Write:$2.50/M | — | +1 | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75) ![fireworks logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Ffireworks.png&w=48&q=75) | | | | 08/02/2026 | | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%20model%20logo.png&w=48&q=75) [alibaba/qwen3.7-flash](/ai-gateway/models/qwen3.7-flash) | 991K | 2.2s | 145tps | $0.03/M+2 more | $0.13/M+2 more | Read: $0.006/M+2 more Write: $0.04/M+2 more | — | +2 | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75) | | | | 07/28/2026 | | | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%20model%20logo.png&w=48&q=75) [alibaba/qwen3.7-plus](/ai-gateway/models/qwen3.7-plus) | 1M | 2.1s | 58tps | $0.40/M+1 more | $1.60/M+1 more | Read: $0.08/M+1 more Write: $0.50/M+1 more | — | +2 | ![alibaba logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Falibaba%2520cloud.png&w=48&q=75) | | | | 06/02/2026 | | ## [Copy link to heading](#about-qwen3-next-80b-a3b-instruct)About 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 heading](#what-to-consider-when-choosing-a-provider)What 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](https://vercel.com/docs/ai-gateway/security-and-compliance/zdr) for details. - Authentication: AI Gateway authenticates requests using an [API key](https://vercel.com/docs/ai-gateway/authentication-and-byok#api-key-authentication) or [OIDC token](https://vercel.com/docs/ai-gateway/authentication-and-byok#oidc-token-authentication). You do not need to manage provider credentials directly. ## [Copy link to heading](#when-to-use-qwen3-next-80b-a3b-instruct)When 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 heading](#conclusion)Conclusion 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.