Compare / Sarvam 30B vs DeepSeek-R1-Distill-Qwen-32B
Sarvam 30B vs DeepSeek-R1-Distill-Qwen-32B
Updated November 2026
Verdict
Sarvam 30B scores higher on the Small LM Index: 61.6 vs 50.9. On the 2 benchmarks both labs report, Sarvam 30B wins 2 and DeepSeek-R1-Distill-Qwen-32B wins 0. Sarvam 30B is smaller: it needs about 20.8 GB of memory at 4-bit, against 23.2 GB for DeepSeek-R1-Distill-Qwen-32B. 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 have limited memory (about 20.8 GB at 4-bit)
- you work with long documents
Pick DeepSeek-R1-Distill-Qwen-32B if…
- you want the more widely used model (more quantized builds and community support)
Specifications
| Sarvam 30B | DeepSeek-R1-Distill-Qwen-32B | |
|---|---|---|
| Small LM Index | 61.6 (likely 58–65) | 50.9 (likely 46–56) |
| Lab | Sarvam AI · Rest of Asia | DeepSeek · China |
| Released | 2026-03-03 | 2025-01-20 |
| Parameters | 32.2B (2.4B active, MoE) | 32.8B (dense) |
| Memory at 4-bit | ~20.8 GB | ~23.2 GB |
| Context window | 131K 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) | 5K | 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 | Sarvam 30B | DeepSeek-R1-Distill-Qwen-32B |
|---|---|---|
| MMLU-Pro Knowledge | 80.0 | ≈ 73 |
| GPQA Diamond Knowledge | 66.5 | 62.1 |
| AIME Math | 88.3 | 72.6 |
| LiveCodeBench Coding | ≈ 71 | 57.2 |
| τ²-Bench Agentic | 45.7 | — |
| MMLU Knowledge | 85.1 | — |
| 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 DeepSeek-R1-Distill-Qwen-32B?
Sarvam 30B scores higher on the Small LM Index: 61.6 vs 50.9. On the 2 benchmarks both labs report, Sarvam 30B wins 2 and DeepSeek-R1-Distill-Qwen-32B wins 0.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Sarvam 30B is smaller: it needs about 20.8 GB of memory at 4-bit, against 23.2 GB for DeepSeek-R1-Distill-Qwen-32B.
Which has the longer context window?
Sarvam 30B has the longer context window: 131K tokens against 128K.
Can I use Sarvam 30B and DeepSeek-R1-Distill-Qwen-32B commercially?
Sarvam 30B 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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