Compare / OLMo 3.1 32B Think vs Qwen3 32B
OLMo 3.1 32B Think vs Qwen3 32B
Updated November 2026
Verdict
OLMo 3.1 32B Think scores higher on the Small LM Index: 57.0 vs 52.4, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, OLMo 3.1 32B Think wins 2 and Qwen3 32B wins 0. Both need about 23.2 GB of memory at 4-bit quantization. Only Qwen3 32B has a hosted API on OpenRouter ($0.13 per million tokens); the other has to be self-hosted.
Pick OLMo 3.1 32B Think if…
- you want the strongest model overall
- you mostly write code
- you need math and reasoning
Pick Qwen3 32B if…
- 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)
Specifications
| OLMo 3.1 32B Think | Qwen3 32B | |
|---|---|---|
| Small LM Index | 57.0 (likely 53–61) | 52.4 (likely 47–57) |
| Lab | Ai2 (OLMo) · United States | Alibaba (Qwen) · China |
| Released | 2025-12-10 | 2025-04-29 |
| Parameters | 32.2B (dense) | 32.8B (dense) |
| Memory at 4-bit | ~22 GB | ~23.2 GB |
| Context window | 64K tokens | 131K tokens |
| Input | text | text |
| Reasoning | Always thinks | Hybrid (thinking on/off) |
| License | Apache-2.0 | Apache-2.0 |
| API price / 1M tokens | Self-host only | $0.13 ($0.080 in, $0.28 out) |
| Hugging Face downloads (30 days) | 6.9K | 3M |
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 | Qwen3 32B |
|---|---|---|
| GPQA Diamond Knowledge | 57.5 | ≈ 66 |
| AIME Math | 78.1 | 72.9 |
| LiveCodeBench Coding | 83.3 | 65.7 |
| 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 Qwen3 32B?
OLMo 3.1 32B Think scores higher on the Small LM Index: 57.0 vs 52.4, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, OLMo 3.1 32B Think wins 2 and Qwen3 32B wins 0.
Which is cheaper to run?
Only Qwen3 32B has a hosted API on OpenRouter ($0.13 per million tokens); the other has to be self-hosted. Both need about 23.2 GB of memory at 4-bit quantization.
Which has the longer context window?
Qwen3 32B has the longer context window: 131K tokens against 64K.
Can I use OLMo 3.1 32B Think and Qwen3 32B commercially?
Both are released under the Apache-2.0 license. 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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