Qwen3 Next 80B A3B Thinking
Qwen3 Next 80B A3B Thinking is a hybrid Transformer-Mamba reasoning model that combines 80 billion total parameters (3B active per token) with a dedicated thinking mode, achieving strong results on AIME25 while supporting ultra-long contexts of 262.1K tokens.
import { streamText } from 'ai'
const result = streamText({ model: 'alibaba/qwen3-next-80b-a3b-thinking', prompt: 'Why is the sky blue?'})Playground
Try out Qwen3 Next 80B A3B Thinking by Alibaba. 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 Thinking
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Providers
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.
| Provider |
|---|
Alibaba | 131K | 0.9s | 298tps | $0.15/M | $1.20/M | — | — | 09/11/2025 | ||||
 Novita AI | 66K | 0.9s | 435tps | $0.15/M | $1.50/M | — | — | 09/11/2025 | ||||
 Google Vertex AI | 262K | 0.4s | 147tps | $0.15/M | $1.20/M | — | — | 09/11/2025 |
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
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About Qwen3 Next 80B A3B Thinking
Qwen3 Next 80B A3B Thinking is the reasoning-mode counterpart to Qwen3-Next-80B-A3B-Instruct. It shares the identical Hybrid Transformer-Mamba architecture, 48 layers in a 12-block pattern of three Gated DeltaNet + MoE layers followed by one Gated Attention + MoE layer, with 512 total experts and only 10 activated per token. What distinguishes the Thinking variant is that thinking mode is the only mode: the model always generates a <think> reasoning trace before its final answer, and the recommended token budget for that trace ranges from 32,768 tokens for typical queries to 81,920 tokens for difficult mathematical or coding problems.
This exclusive thinking mode is a deliberate design choice. By eliminating mode switching, the model is specialized for tasks where getting the right answer matters more than minimizing output length. The architecture's linear-attention Gated DeltaNet layers keep context processing efficient even as reasoning traces extend the total sequence length substantially beyond the prompt, which helps when reasoning chains grow long.
Benchmark results reflect this specialization. Across math and coding benchmarks the model outperforms both the Qwen3-30B-A3B-Thinking-2507 and Qwen3-32B-Thinking predecessors, as well as several proprietary reasoning models in Qwen's published comparisons. See https://modelstudio.console.alibabacloud.com/?tab=doc#/doc/?type=model&url=2840914_2&modelId=qwen3-next-80b-a3b-thinking for detailed benchmark tables.
What To Consider When Choosing a Provider
- Configuration: Because thinking-mode responses can exceed 32K output tokens for complex reasoning tasks, verify that your provider and application timeout settings accommodate extended generation times before deploying.
- Zero Data Retention: AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Qwen3 Next 80B A3B Thinking
Best For
- Competitive mathematics and science: Rigorous reasoning problems where step-by-step derivation is required
- Hard coding challenges: Competitive programming and algorithmic design that benefit from explicit problem decomposition before code generation
- Cross-reference long-document analysis: Tasks that reason across 100K+ token inputs while maintaining structured thought
- Tutoring and explanation systems: Applications where visible reasoning chains are pedagogically valuable
- Auditable research workflows: Use cases where a transparent inference process allows human review of the model's logic
Consider Alternatives When
- High-throughput instruction following: Use Qwen3-Next-80B-A3B-Instruct for short-to-medium tasks without reasoning overhead
- Strict token budgets: Thinking traces add significant output volume and cost per request
- Multimodal input required: This model is text-only; use a vision-language variant for images or video
- Real-time latency requirements: Extended reasoning generation can't meet hard low-latency response targets
Conclusion
Qwen3 Next 80B A3B Thinking occupies a distinct space: an architecture built for long-context efficiency that is simultaneously dedicated exclusively to extended reasoning. Teams working on hard STEM problems, detailed code analysis, or any domain where a visible reasoning chain adds quality and auditability can use it without resorting to a fully dense trillion-parameter alternative.