Compare / OLMo 3 7B Think vs DeepSeek-R1-Distill-Qwen-7B
OLMo 3 7B Think vs DeepSeek-R1-Distill-Qwen-7B
Updated November 2026
Verdict
OLMo 3 7B Think scores higher on the Small LM Index: 43.4 vs 39.8, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, OLMo 3 7B Think wins 1 and DeepSeek-R1-Distill-Qwen-7B wins 1. DeepSeek-R1-Distill-Qwen-7B is smaller: it needs about 5.7 GB of memory at 4-bit, against 7.8 GB for OLMo 3 7B Think. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick OLMo 3 7B Think if…
- you want the strongest model overall
- you need math and reasoning
Pick DeepSeek-R1-Distill-Qwen-7B if…
- you have limited memory (about 5.7 GB at 4-bit)
- you work with long documents
- you want the more widely used model (more quantized builds and community support)
Specifications
| OLMo 3 7B Think | DeepSeek-R1-Distill-Qwen-7B | |
|---|---|---|
| Small LM Index | 43.4 (likely 39–48) | 39.8 (likely 35–45) |
| Lab | Ai2 (OLMo) · United States | DeepSeek · China |
| Released | 2025-11-20 | 2025-01-20 |
| Parameters | 7.3B (dense) | 7.6B (dense) |
| Memory at 4-bit | ~7.8 GB | ~5.7 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) | 94.8K | 321.5K |
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 7B Think | DeepSeek-R1-Distill-Qwen-7B |
|---|---|---|
| GPQA Diamond Knowledge | 46.2 | 49.1 |
| AIME Math | 64.6 | 55.5 |
| LiveCodeBench Coding | ≈ 49 | 37.6 |
| IFBench Instruction following | 41.6 | — |
| MMLU Knowledge | 77.8 | — |
| IFEval Instruction following | 88.2 | — |
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 7B Think or DeepSeek-R1-Distill-Qwen-7B?
OLMo 3 7B Think scores higher on the Small LM Index: 43.4 vs 39.8, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, OLMo 3 7B Think wins 1 and DeepSeek-R1-Distill-Qwen-7B wins 1.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. DeepSeek-R1-Distill-Qwen-7B is smaller: it needs about 5.7 GB of memory at 4-bit, against 7.8 GB for OLMo 3 7B Think.
Which has the longer context window?
DeepSeek-R1-Distill-Qwen-7B has the longer context window: 128K tokens against 64K.
Can I use OLMo 3 7B Think and DeepSeek-R1-Distill-Qwen-7B commercially?
OLMo 3 7B Think is released under Apache-2.0 and DeepSeek-R1-Distill-Qwen-7B under MIT. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.