Compare / OLMo 3.1 32B Think vs DeepSeek-R1-Distill-Qwen-32B
OLMo 3.1 32B Think vs DeepSeek-R1-Distill-Qwen-32B
Updated November 2026
Verdict
OLMo 3.1 32B Think scores higher on the Small LM Index: 57.0 vs 50.9, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, OLMo 3.1 32B Think wins 2 and DeepSeek-R1-Distill-Qwen-32B wins 1. Both need about 23.2 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick OLMo 3.1 32B Think if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
Pick DeepSeek-R1-Distill-Qwen-32B if…
- you work with long documents
- you want the more widely used model (more quantized builds and community support)
Specifications
| OLMo 3.1 32B Think | DeepSeek-R1-Distill-Qwen-32B | |
|---|---|---|
| Small LM Index | 57.0 (likely 53–61) | 50.9 (likely 46–56) |
| Lab | Ai2 (OLMo) · United States | DeepSeek · China |
| Released | 2025-12-10 | 2025-01-20 |
| Parameters | 32.2B (dense) | 32.8B (dense) |
| Memory at 4-bit | ~22 GB | ~23.2 GB |
| Context window | 64K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Always thinks | Always thinks |
| License | Apache-2.0 | MIT |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 6.9K | 348.2K |
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 | OLMo 3.1 32B Think | DeepSeek-R1-Distill-Qwen-32B |
|---|---|---|
| GPQA Diamond Knowledge | 57.5 | 62.1 |
| AIME Math | 78.1 | 72.6 |
| LiveCodeBench Coding | 83.3 | 57.2 |
| IFBench Instruction following | 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, OLMo 3.1 32B Think or DeepSeek-R1-Distill-Qwen-32B?
OLMo 3.1 32B Think scores higher on the Small LM Index: 57.0 vs 50.9, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, OLMo 3.1 32B Think wins 2 and DeepSeek-R1-Distill-Qwen-32B wins 1.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 23.2 GB of memory at 4-bit quantization.
Which has the longer context window?
DeepSeek-R1-Distill-Qwen-32B has the longer context window: 128K tokens against 64K.
Can I use OLMo 3.1 32B Think and DeepSeek-R1-Distill-Qwen-32B commercially?
OLMo 3.1 32B Think is released under Apache-2.0 and DeepSeek-R1-Distill-Qwen-32B under MIT. 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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