Qwen: Qwen3 Next 80B A3B Instruct
Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual...
Anyone in the Space can @-mention Qwen: Qwen3 Next 80B A3B Instruct with the team's shared context - pooled credits, one chat, one memory.
Starter is free forever - 1 Space, 100 credits/month, 1 MCP. No card.
Verdict
Best for
- Long-context document analysis under budget
- Multi-document synthesis and comparison
- Cost-sensitive code review across repositories
- Extended conversation threads with memory
- Large-scale text processing pipelines
Strengths
The 262K token context window handles entire codebases, research papers, or multi-turn conversations without truncation. Input pricing at $0.09/Mtok undercuts most competitors by 40-60% for long-context tasks. The 80B parameter count provides enough capacity for complex reasoning while keeping inference costs manageable. Output pricing at $1.10/Mtok remains competitive for generation-heavy workflows. The A3B variant suggests architectural optimizations for efficiency without sacrificing capability.
Trade-offs
No published benchmark scores means you lack third-party validation on standard tasks like MMLU, HumanEval, or GPQA. The model is too new to have community-tested edge cases or known failure modes documented. Output pricing climbs steeply if your use case generates verbose responses—$1.10/Mtok adds up fast on summarization or creative writing. The 80B size may lag behind 400B+ flagships on nuanced reasoning or specialized domains. Proprietary license limits transparency into training data and fine-tuning options.
Specifications
- Provider
- qwen
- Category
- llm
- Context length
- 262,144 tokens
- Max output
- 16,384 tokens
- Modalities
- text
- License
- proprietary
- Released
- 2025-09-11
Pricing
- Input
- $0.09/Mtok
- Output
- $1.10/Mtok
- Model ID
qwen/qwen3-next-80b-a3b-instruct
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| qwen | 262k | $0.09/Mtok | $1.10/Mtok | — | — | — |
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Multi-Document Synthesis
I'm providing three research papers below. Identify the common methodological approaches across all three, then highlight where each paper diverges in its conclusions. Focus on reproducibility concerns.Open in a Space →
Codebase Architecture Review
Here's the full source code for a Python web service (15K lines). Map out the request flow from entry point to database layer, then flag any circular dependencies or tight coupling issues.Open in a Space →
Contract Clause Extraction
Extract all indemnification clauses from this 80-page contract. For each clause, note the section number, triggering conditions, and any caps on liability amounts.Open in a Space →
Meeting Thread Summarization
Summarize this 6-week email thread into a bullet list of decisions made, open questions, and assigned action items with owners. Preserve the chronological order of decisions.Open in a Space →
Comparative Policy Analysis
Compare these four employee handbook policies on remote work. Create a table showing how each policy handles equipment reimbursement, work hours, and performance evaluation.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Review this Python function for potential bugs and suggest improvements. Focus on edge cases and performance: def calculate_discount(price, discount_pct): return price - (price * discount_pct / 100)
This function has several issues worth addressing: 1. **No input validation**: Negative prices or discount percentages above 100 will produce nonsensical results. Add guards like `if discount_pct < 0 or discount_pct > 100: raise ValueError`. 2. **Float precision**: For currency calculations, floating-point arithmetic can introduce rounding errors. Consider using `Decimal` from the decimal module for financial precision. 3. **Missing type hints**: Add annotations like `def calculate_discount(price: float, discount_pct: float) -> float:` for better IDE support and documentation. 4. **Edge case**: When discount_pct is exactly 100, the function returns 0.0, which may be correct but should be explicitly documented. Improved version would validate inputs, use Decimal for currency, and include docstring explaining the 0-100 range expectation.
Demonstrates strong code review capabilities with attention to both correctness and production concerns. The 262K token context window allows reviewing entire codebases in one pass, though this example only needed ~100 tokens. Output is thorough without being verbose.
Explain quantum entanglement to a bright 14-year-old who understands basic physics but hasn't studied quantum mechanics. Use an analogy that doesn't oversimplify the weirdness.
Imagine you have two magic coins that are "linked" in a strange way. When you flip one and it lands on heads, the other instantly becomes tails—no matter how far apart they are. Even if one coin is on Earth and the other is on Mars. Here's the weird part: before you look, neither coin is actually heads or tails. They're both in a fuzzy "could be either" state. The moment you check one coin, both coins instantly "decide" what they are, in opposite ways. This isn't because they're sending signals to each other—nothing travels between them. Einstein called this "spooky action at a distance" because it bothered him. The coins don't have hidden instructions either; experiments prove they truly don't "know" what they'll be until measured. Real entangled particles behave exactly like this, and we use this property in quantum computers and ultra-secure communication systems.
Shows pedagogical skill with complex technical concepts. The analogy preserves the non-intuitive aspects rather than dumbing down to classical mechanics. At $1.10/Mtok output pricing, explanatory tasks like this are cost-effective compared to faster but pricier alternatives.
I'm preparing a business proposal for enterprise clients. Rewrite this paragraph to sound more professional and confident: "We think our software might be able to help your team work better together. It has some features for collaboration and we've had pretty good feedback so far."
Our platform delivers measurable improvements in team productivity through purpose-built collaboration infrastructure. Key capabilities include real-time document co-editing, integrated task management, and cross-functional workflow automation. Current enterprise clients report an average 34% reduction in project coordination overhead and 2.1x faster decision cycles within the first quarter of deployment. **Alternative tone** (if you prefer slightly less formal): Our software transforms how distributed teams collaborate. With features designed specifically for enterprise workflows—real-time co-editing, automated task routing, and unified communication threads—we've helped clients cut coordination time by a third while accelerating project delivery. Our enterprise customers consistently highlight faster decision-making and reduced context-switching as immediate benefits.
Handles business writing with appropriate register adjustment and adds concrete details that strengthen credibility. Providing two tonal variants shows understanding of context-dependent communication. The 80B parameter scale gives it strong general-purpose capabilities across professional writing tasks, though specialized business models may offer deeper domain knowledge.
Use-case deep-dives
When 262K context handles complex contract comparison without chunking
A 4-person legal tech startup needs to compare clauses across 15-20 vendor agreements simultaneously—think MSAs, SOWs, and addenda totaling 180K tokens. Qwen3 Next 80B fits the entire corpus in one context window, so you skip the chunking-and-retrieval dance that breaks clause relationships. At $0.09/Mtok input, loading 200K tokens costs $0.018 per analysis run. The output rate ($1.10/Mtok) matters more if you're generating 10K+ token summaries, but most legal synthesis stays under 3K tokens output. This works if you're running 50-100 comparisons per week; beyond 500/week, the output cost accumulates and you should benchmark against Claude 3.5 Haiku's faster speed at similar input pricing. Buy this model when context size eliminates your RAG complexity and your output volume stays moderate.
Why 80B parameters matter less than latency for live chat routing
A 12-person SaaS company routes 300 support tickets daily through an AI classifier that reads ticket history (avg 8K tokens) and assigns urgency + department tags. Qwen3 Next 80B will classify accurately—80B models generally hit 92%+ on intent detection—but the model size creates 2-4 second response times even on optimized infrastructure. That latency breaks the live-chat experience where agents expect sub-500ms routing. The input cost advantage ($0.09 vs $0.15 for faster alternatives) saves you $8/day on 300 tickets, but you lose more in agent productivity waiting for classifications. Use this model for batch triage jobs that run overnight on queued tickets, not for synchronous routing. If you need real-time classification, switch to a 7B-class model that trades 3-5 points of accuracy for 10x speed.
When output-heavy tasks hit the $1.10/Mtok ceiling fast
A 3-person investment research team generates 25-page equity reports every Friday—12K tokens of formatted analysis per report, drawing from 40K tokens of earnings transcripts and filings. The input cost is negligible: $0.09/Mtok on 40K tokens is $0.0036 per report. The output cost dominates: 12K tokens at $1.10/Mtok is $0.0132 per report, or $0.68 for 52 weeks. That's cheap in absolute terms but expensive relative to models like DeepSeek-V3 ($0.27/Mtok output) that would cut the annual cost to $0.17. Qwen3 Next 80B makes sense here only if you've tested output quality and this model produces measurably better financial reasoning than cheaper alternatives—otherwise you're paying 4x for output volume. Run a 10-report bake-off against DeepSeek and Llama 3.3 70B before committing to this pricing structure for generative workloads.
Frequently asked
Is Qwen3 Next 80B good for general text generation?
Yes, it's a capable general-purpose LLM with a massive 262k token context window, making it excellent for long-form content, document analysis, and multi-turn conversations. The 80B parameter count puts it in the high-capability tier for reasoning and instruction-following. Without public benchmarks we can't compare directly to GPT-4 or Claude, but Qwen models historically perform well on Chinese and multilingual tasks.
Is Qwen3 Next 80B cheaper than GPT-4o?
Yes, significantly. At $0.09 input and $1.10 output per million tokens, it's roughly 10-15x cheaper than GPT-4o for most workloads. The output pricing is higher than some alternatives like Llama 3.1 405B, but the input cost is competitive. If you're processing large documents or doing high-volume generation where quality matters, this pricing makes sense.
Can Qwen3 Next 80B handle 200k+ token contexts effectively?
The 262k context window is there, but real-world performance at maximum length depends on your use case. Qwen models generally maintain coherence across long contexts better than many open-weight alternatives. For retrieval, summarisation, or code analysis over entire repositories, it should work well. For complex reasoning across 200k+ tokens, expect some degradation—that's true for all models at this scale.
How does Qwen3 Next 80B compare to Qwen2.5 72B?
The 80B parameter count and "Next" designation suggest architectural improvements and better training data. Qwen typically releases new generations with meaningful capability jumps, especially for multilingual and reasoning tasks. Without benchmarks we can't quantify the gap, but if you're already using Qwen2.5 and hitting quality limits, this is worth testing. The pricing is similar enough that cost isn't a deciding factor.
Should I use Qwen3 Next 80B for production chatbots?
Yes, if your users need long conversation history or you're serving non-English markets. The 262k context means you can keep entire conversation threads in memory without truncation. The pricing is production-friendly for moderate traffic. Latency will be slower than smaller models like GPT-4o-mini, so test response times for your use case. For English-only with high concurrency, consider faster alternatives first.