# DeepSeek-R1 vs Llama 3.2 1B

> DeepSeek-R1 is the stronger model overall, scoring 42.0 to 19.9 on the Noometry Index. Llama 3.2 1B costs 13× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-llama-3-2-1b
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-R1 scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-R1 leads 61.5 to 21.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.0 | 19.9 |
| Rank | 110 | 349 |
| Context | 164K | 60K |
| Input $/M | $0.50 | $0.027 |
| Output $/M | $2.15 | $0.20 |
| Weights | Proprietary | Open |

## Coding

- DeepSeek-R1: 45.9 (#63)
- Llama 3.2 1B: 20.6 (#333)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1427 | 1071 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| BigCodeBench Instruct | — | 8.2% |
| LiveBench Coding | 66.7% | — |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- Llama 3.2 1B: 14.5 (#142)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| BALROG | 34.9% | 6.6% |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| DeepResearch Bench | 35.1% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- Llama 3.2 1B: 15.6 (#304)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1415 | 1044 |
| Epoch Capabilities Index | 141.3 | 101.95 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 0% |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- Llama 3.2 1B: 9.7 (#308)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 0.6% |
| LMArena Math | 1401 | 1086 |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- Llama 3.2 1B: 7.2 (#307)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 76.3% | 23.9% |
| LMArena Expert | 1394 | 1007 |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- Llama 3.2 1B: 23.9 (#287)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1412 | 973 |
| LMArena Chinese | 1443 | 958 |
| LMArena German | 1405 | 1014 |
| LMArena Russian | 1423 | 941 |
| LMArena French | 1415 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1359 | — |
| LMArena Spanish | 1410 | — |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- Llama 3.2 1B: 52.6 (#285)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1382 | 1031 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Llama 3.2 1B: 31.9 (#269)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1391 | 1050 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- Llama 3.2 1B: 21.4 (#305)

| Benchmark | DeepSeek-R1 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1428 | 1055 |
| LMArena Creative Writing | 1406 | 1033 |
| EQ-Bench Creative Writing | 1500 | 200 |
| LMArena Multi-Turn | 1404 | 1030 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Llama 3.2 1B?

DeepSeek-R1 is the stronger model overall, scoring 42.0 to 19.9 on the Noometry Index. Llama 3.2 1B costs 13× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 or Llama 3.2 1B?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.

### Is DeepSeek-R1 or Llama 3.2 1B better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 20.6 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-R1 does, with 164K tokens against 60K.

### How many benchmarks do DeepSeek-R1 and Llama 3.2 1B share?

18 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Llama 3.2 1B has 22.