Compare / EXAONE 4.0 32B vs OLMo 3.1 32B Think
EXAONE 4.0 32B vs OLMo 3.1 32B Think
Updated November 2026
Verdict
EXAONE 4.0 32B scores higher on the Small LM Index: 62.0 vs 57.0, although their likely ranges overlap, so the lead is not certain. On the 5 benchmarks both labs report, EXAONE 4.0 32B wins 3 and OLMo 3.1 32B Think wins 2. Both need about 22 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick EXAONE 4.0 32B if…
- you want the strongest model overall
- you need math and reasoning
- you work with long documents
Pick OLMo 3.1 32B Think if…
- you mostly write code
- you need precise instruction following
- you need a permissive license (Apache-2.0)
Specifications
| EXAONE 4.0 32B | OLMo 3.1 32B Think | |
|---|---|---|
| Small LM Index | 62.0 (likely 59–65) | 57.0 (likely 53–61) |
| Lab | LG AI Research (EXAONE) · Rest of Asia | Ai2 (OLMo) · United States |
| Released | 2025-07-15 | 2025-12-10 |
| Parameters | 32B (dense) | 32.2B (dense) |
| Memory at 4-bit | ~21.9 GB | ~22 GB |
| Context window | 131K tokens | 64K tokens |
| Input | text | text |
| Reasoning | Hybrid (thinking on/off) | Always thinks |
| License | EXAONE License (non-commercial) | Apache-2.0 |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 14.8K | 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 | EXAONE 4.0 32B | OLMo 3.1 32B Think |
|---|---|---|
| MMLU-Pro Knowledge | 81.8 | ≈ 77 |
| GPQA Diamond Knowledge | 75.4 | 57.5 |
| AIME Math | 85.3 | 78.1 |
| LiveCodeBench Coding | 66.7 | 83.3 |
| IFBench Instruction following | — | 68.1 |
| MMLU Knowledge | 92.3 | 86.4 |
| Multilingual MMLU Multilingual | 85.6 | — |
| IFEval Instruction following | 83.7 | 93.8 |
| BFCL Agentic | 63.9 | — |
| HMMT Math | 72.9 | — |
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, EXAONE 4.0 32B or OLMo 3.1 32B Think?
EXAONE 4.0 32B scores higher on the Small LM Index: 62.0 vs 57.0, although their likely ranges overlap, so the lead is not certain. On the 5 benchmarks both labs report, EXAONE 4.0 32B wins 3 and OLMo 3.1 32B Think wins 2.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 22 GB of memory at 4-bit quantization.
Which has the longer context window?
EXAONE 4.0 32B has the longer context window: 131K tokens against 64K.
Can I use EXAONE 4.0 32B and OLMo 3.1 32B Think commercially?
EXAONE 4.0 32B is released under EXAONE License (non-commercial) 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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