Qwen3-Coder-Next
Today, we're announcing Qwen3-Coder-Next, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements:
Params
79.7 B
Context
262,144
Downloads 30d
426 K
Likes
1,739
Download history
daily snapshots · 106 days
▲ 168 K in the last 30 days (28.3%)
593 K425 K
Oct 12Oct 22Nov 1Nov 10
600 K385 K
Aug 13Sep 12Oct 12Nov 10
729 K385 K
Jul 28Sep 1Oct 6Nov 10
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 | 159.4 GB | 187.7 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 256K ctx | 159.4 GB | 558.2 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-coder-next
{
"hf_id": "Qwen/Qwen3-Coder-Next",
"params_b": 79.67,
"context_length": 262144,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 426195,
"vram_estimates": [
{ "quant": "bf16", "gb": 187.7 }
],
"updated_at": "2026-07-28T18:04:12Z"
}
Use via API
via OpenRouter · USD per 1M tokens · synced 2026-11-10$0.12
input / 1M
$0.80
output / 1M
$0.07
cached input / 1M
$0.92
1M in + 1M out
- Context window
- 262,144 tokens
- Max output
- 235,929 tokens
- Providers
- 1
- Cheapest provider
- Parasail · $0.12 / $0.80
- Best throughput
- —
- AA Intelligence / Coding / Agentic
- — / 36.2 / 0.9
✓ Tool calling
✗ Reasoning
✓ Structured outputs
✗ Image input
✗ File input
✗ Audio input
✓ Prompt caching
Specifications
- Architecture
- Qwen3NextForCausalLM
- Parameters
- 79.7 B
- Tensor type
- BF16
- Context length
- 262,144
- Vocabulary
- 151,936
- Layers / heads
- 48 / 16
- Licence
- apache-2.0
- First seen on the Hub
- 2026-01-30
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any text-generation model
Popular comparisons
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