nvidia · Open weights
OpenReasoning Nemotron 32B
OpenReasoning-Nemotron-32B is a reasoning model derived from Qwen2.5-32B-Instruct, post-trained for math, science, and code solution generation. Evaluated with up to 64K output tokens. Available in multiple sizes: 1.5B, 7B, 14B, and 32B.
- Reasoning
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.10
- Output
- $0.40
Reference prices as published by the model provider. API example
Specifications
- Context window
- 32.8K
- Max output
- —
Side by side
OpenReasoning Nemotron 32B compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| OpenReasoning Nemotron 32BnvidiaThis model | Qwen 3.8 27B AntislopQwenRemove | |
|---|---|---|
| Provider | nvidia | Qwen |
| Discovery tier | Open weights | Open weights |
| Added | Aug 21, 2025 | Oct 5, 2026 |
| Limits | ||
| Context window | 32.77K | 262.14KBest |
| Max output | — | 32.77K |
| Average output | — | 299 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10 | $0.10 |
| Output | $0.40Best | $0.60 |
| Cache read | — | $0.05 |
| Performance | ||
| Throughput | — | 34.6 tok/s |
| Time to first token | — | 1.9 s |
| Request success (24h) | — | 99.1% |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
There are no public benchmark results for these models yet.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "pamanseau/OpenReasoning-Nemotron-32B",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="pamanseau/OpenReasoning-Nemotron-32B",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)nvidia
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