Compare / Nemotron 3 Nano 30B-A3B vs OLMo 3.1 32B Think
Nemotron 3 Nano 30B-A3B vs OLMo 3.1 32B Think
Updated November 2026
Verdict
Nemotron 3 Nano 30B-A3B scores higher on the Small LM Index: 62.6 vs 57.0, although their likely ranges overlap, so the lead is not certain. On the 4 benchmarks both labs report, Nemotron 3 Nano 30B-A3B wins 3 and OLMo 3.1 32B Think wins 1. Both need about 22 GB of memory at 4-bit quantization. Only Nemotron 3 Nano 30B-A3B has a hosted API on OpenRouter ($0.10 per million tokens); the other has to be self-hosted.
Pick Nemotron 3 Nano 30B-A3B if…
- you want the strongest model overall
- you need math and reasoning
- you need precise instruction following
- you want a hosted API rather than running it yourself
- you work with long documents
- you want the more widely used model (more quantized builds and community support)
Pick OLMo 3.1 32B Think if…
- you mostly write code
- you need a permissive license (Apache-2.0)
Specifications
| Nemotron 3 Nano 30B-A3B | OLMo 3.1 32B Think | |
|---|---|---|
| Small LM Index | 62.6 (likely 59–66) | 57.0 (likely 53–61) |
| Lab | NVIDIA · United States | Ai2 (OLMo) · United States |
| Released | 2025-12-15 | 2025-12-10 |
| Parameters | 31.6B (3.5B active, MoE) | 32.2B (dense) |
| Memory at 4-bit | ~20.3 GB | ~22 GB |
| Context window | 1M tokens | 64K tokens |
| Input | text | text |
| Reasoning | Hybrid (thinking on/off) | Always thinks |
| License | NVIDIA Open Model | Apache-2.0 |
| API price / 1M tokens | $0.10 ($0.060 in, $0.24 out) | Self-host only |
| Hugging Face downloads (30 days) | 1.7M | 6.9K |
Benchmarks
Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.
| Benchmark | Nemotron 3 Nano 30B-A3B | OLMo 3.1 32B Think |
|---|---|---|
| MMLU-Pro Knowledge | 78.3 | ≈ 77 |
| GPQA Diamond Knowledge | 73.0 | 57.5 |
| Humanity's Last Exam Reasoning | 10.6 | ≈ 9 |
| AIME Math | 89.1 | 78.1 |
| LiveCodeBench Coding | 68.3 | 83.3 |
| SWE-bench Verified Coding | 38.8 | ≈ 41 |
| τ²-Bench Agentic | 49.0 | — |
| IFBench Instruction following | 71.5 | 68.1 |
| MMLU Knowledge | — | 86.4 |
| IFEval Instruction following | — | 93.8 |
Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.
Questions
Which is better, Nemotron 3 Nano 30B-A3B or OLMo 3.1 32B Think?
Nemotron 3 Nano 30B-A3B scores higher on the Small LM Index: 62.6 vs 57.0, although their likely ranges overlap, so the lead is not certain. On the 4 benchmarks both labs report, Nemotron 3 Nano 30B-A3B wins 3 and OLMo 3.1 32B Think wins 1.
Which is cheaper to run?
Only Nemotron 3 Nano 30B-A3B has a hosted API on OpenRouter ($0.10 per million tokens); the other has to be self-hosted. Both need about 22 GB of memory at 4-bit quantization.
Which has the longer context window?
Nemotron 3 Nano 30B-A3B has the longer context window: 1M tokens against 64K.
Can I use Nemotron 3 Nano 30B-A3B and OLMo 3.1 32B Think commercially?
Nemotron 3 Nano 30B-A3B is released under NVIDIA Open Model and OLMo 3.1 32B Think under Apache-2.0. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
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