# DeepSeek-V3.1 vs Llama 3.2 1B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 6.0× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-llama-3-2-1b
- Last updated: 2026-10-12
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Llama 3.2 1B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 21.3.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 60K.

## Snapshot

| | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 20.1 |
| Rank | 108 | 354 |
| Context | 164K | 60K |
| Input $/M | $0.25 | $0.027 |
| Output $/M | $0.95 | $0.20 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Llama 3.2 1B: 21.1 (#338)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1417 | 1070 |
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Llama 3.2 1B: 14.6 (#150)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Llama 3.2 1B: 16.2 (#308)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1044 |
| Epoch Capabilities Index | 139.92 | 101.99 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 0% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Llama 3.2 1B: 10.4 (#313)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Math | 1420 | 1086 |
| OTIS Mock AIME 2024-2025 | — | 0.6% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Llama 3.2 1B: 7.2 (#312)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Expert | 1405 | 1007 |
| GPQA Diamond | — | 23.9% |
| Vectara Hallucination Rate | 5.5% | — |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Llama 3.2 1B: 23.8 (#292)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1400 | 973 |
| LMArena Chinese | 1469 | 959 |
| LMArena German | 1411 | 1014 |
| LMArena Russian | 1405 | 941 |
| LMArena French | 1447 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1431 | — |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Llama 3.2 1B: 52.4 (#290)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1031 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Llama 3.2 1B: 31.9 (#274)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1422 | 1050 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Llama 3.2 1B: 21.3 (#310)

| Benchmark | DeepSeek-V3.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1420 | 1055 |
| LMArena Creative Writing | 1401 | 1033 |
| EQ-Bench Creative Writing | 1436 | 200 |
| LMArena Multi-Turn | 1408 | 1030 |

## FAQ

### Is DeepSeek-V3.1 better than Llama 3.2 1B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 6.0× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.

### Is DeepSeek-V3.1 or Llama 3.2 1B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 60K.

### How many benchmarks do DeepSeek-V3.1 and Llama 3.2 1B share?

15 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 3.2 1B has 22.