Qwen3-Next-80B-A3B-Thinking
Over the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models Qwen3-Next.
Params
81.3 B
Context
262,144
Downloads 30d
38 K
Likes
502
Download history
tracking started — chart appears after 7 days of snapshots (5 recorded)38 K downloads in the last 30 days
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | bf16 | 162.7 GB | 191.6 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 256K ctx | 162.7 GB | 569.8 GB | ❌ Won’t fit |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/qwen3-next-80b-a3b-thinking
{
"hf_id": "Qwen/Qwen3-Next-80B-A3B-Thinking",
"params_b": 81.32,
"context_length": 262144,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 38286,
"vram_estimates": [
{ "quant": "bf16", "gb": 191.6 }
],
"updated_at": "2026-10-19T06:00:37Z"
}
Use via API
via OpenRouter · USD per 1M tokens · synced 2026-10-24$0.15
input / 1M
$1.20
output / 1M
—
cached input / 1M
$1.35
1M in + 1M out
- Context window
- 262,144 tokens
- Max output
- 235,929 tokens
- Providers
- 1
- Cheapest provider
- Google · $0.15 / $1.20
- Best throughput
- —
- AA Intelligence / Coding / Agentic
- 11.2 / 17.4 / —
✓ Tool calling
✓ Reasoning
✓ Structured outputs
✗ Image input
✗ File input
✗ Audio input
✗ Prompt caching
Specifications
- Architecture
- Qwen3NextForCausalLM
- Parameters
- 81.3 B
- Tensor type
- BF16
- Context length
- 262,144
- Vocabulary
- 151,936
- Layers / heads
- 48 / 16
- Licence
- apache-2.0
- First seen on the Hub
- 2025-09-09
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-20
Compare with any text-generation model