gpt-oss-20b
Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
Params
21.5 B
Context
131,072
Downloads 30d
5.8 M
Likes
5,292
Download history
daily snapshots · 106 days
▲ 121 K in the last 30 days (2.0%)
6.1 M5.7 M
Oct 12Oct 22Nov 1Nov 10
8.1 M5.7 M
Aug 13Sep 12Oct 12Nov 10
8.6 M5.7 M
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 | u8 | 27.5 GB | 34.0 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 128K ctx | 27.5 GB | 82.4 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/gpt-oss-20b
{
"hf_id": "openai/gpt-oss-20b",
"params_b": 21.51,
"context_length": 131072,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 5830063,
"vram_estimates": [
{ "quant": "u8", "gb": 34.0 }
],
"updated_at": "2026-07-28T17:06:36Z"
}
Use via API
via OpenRouter · USD per 1M tokens · synced 2026-11-10$0.03
input / 1M
$0.13
output / 1M
$0.03
cached input / 1M
$0.16
1M in + 1M out
:batch variant
$0.024 in · $0.11 out
- Context window
- 131,072 tokens
- Max output
- 117,964 tokens
- Providers
- 10
- Cheapest provider
- PrimeIntellect · $0.02 / $0.03
- Best throughput
- —
- AA Intelligence / Coding / Agentic
- — / 20.7 / 1.2
✓ Tool calling
✓ Reasoning
✓ Structured outputs
✗ Image input
✗ File input
✗ Audio input
✓ Prompt caching
Specifications
- Architecture
- GptOssForCausalLM
- Parameters
- 21.5 B
- Tensor type
- U8
- Context length
- 131,072
- Vocabulary
- 201,088
- Layers / heads
- 24 / 64
- Licence
- apache-2.0
- First seen on the Hub
- 2025-08-04
- 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
Appears in