Compare / Sarvam 30B vs OLMo 3.1 32B Think
Sarvam 30B vs OLMo 3.1 32B Think
Updated November 2026
Verdict
Sarvam 30B scores higher on the Small LM Index: 61.6 vs 57.0, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, Sarvam 30B wins 2 and OLMo 3.1 32B Think wins 1. 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 Sarvam 30B if…
- you want the strongest model overall
- you need math and reasoning
- you work with long documents
Pick OLMo 3.1 32B Think if…
No clear advantage in the data we have.
Specifications
| Sarvam 30B | OLMo 3.1 32B Think | |
|---|---|---|
| Small LM Index | 61.6 (likely 58–65) | 57.0 (likely 53–61) |
| Lab | Sarvam AI · Rest of Asia | Ai2 (OLMo) · United States |
| Released | 2026-03-03 | 2025-12-10 |
| Parameters | 32.2B (2.4B active, MoE) | 32.2B (dense) |
| Memory at 4-bit | ~20.8 GB | ~22 GB |
| Context window | 131K tokens | 64K tokens |
| Input | text | text |
| Reasoning | Always thinks | Always thinks |
| License | Apache-2.0 | Apache-2.0 |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 5K | 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 | Sarvam 30B | OLMo 3.1 32B Think |
|---|---|---|
| MMLU-Pro Knowledge | 80.0 | ≈ 77 |
| GPQA Diamond Knowledge | 66.5 | 57.5 |
| AIME Math | 88.3 | 78.1 |
| LiveCodeBench Coding | ≈ 71 | 83.3 |
| τ²-Bench Agentic | 45.7 | — |
| IFBench Instruction following | — | 68.1 |
| MMLU Knowledge | 85.1 | 86.4 |
| IFEval Instruction following | — | 93.8 |
| MATH-500 Math | 97.0 | — |
| HMMT Math | 73.3 | — |
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, Sarvam 30B or OLMo 3.1 32B Think?
Sarvam 30B scores higher on the Small LM Index: 61.6 vs 57.0, although their likely ranges overlap, so the lead is not certain. On the 3 benchmarks both labs report, Sarvam 30B wins 2 and OLMo 3.1 32B Think wins 1.
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?
Sarvam 30B has the longer context window: 131K tokens against 64K.
Can I use Sarvam 30B and OLMo 3.1 32B Think 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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