# DeepSeek V4.1 Flash vs Llama 3.2 1B

> DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-llama-3-2-1b
- Last updated: 2026-10-12
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek V4.1 Flash 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 math, where DeepSeek V4.1 Flash leads 66.7 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for DeepSeek V4.1 Flash 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 $0.15 / $0.60 for DeepSeek V4.1 Flash.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 60K.

## Snapshot

| | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 52.8 | 20.1 |
| Rank | 38 | 354 |
| Context | 1M | 60K |
| Input $/M | $0.15 | $0.027 |
| Output $/M | $0.60 | $0.20 |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.9 (#32)
- Llama 3.2 1B: 21.1 (#338)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1506 | 1070 |
| LMArena WebDev | 1619 | — |
| SciCode | 51.9% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,092 | — |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: 31.2 (#69)
- Llama 3.2 1B: 14.6 (#150)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 39.5% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| GDP.pdf | 19.8% | — |

## Reasoning

- DeepSeek V4.1 Flash: 50.2 (#36)
- Llama 3.2 1B: 16.2 (#308)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Hard Prompts | 1483 | 1044 |
| Epoch Capabilities Index | 154.9 | 101.99 |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 14.3% | — |
| Chess Puzzles | — | 0% |
| Mystery Game Puzzles | 43% | — |
| DTBench | 89.9% | — |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |

## Math

- DeepSeek V4.1 Flash: 66.7 (#25)
- Llama 3.2 1B: 10.4 (#313)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 0.6% |
| LMArena Math | 1477 | 1086 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| ProofBench | 54% | — |

## Knowledge

- DeepSeek V4.1 Flash: 57.9 (#38)
- Llama 3.2 1B: 7.2 (#312)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 89.8% | 23.9% |
| LMArena Expert | 1506 | 1007 |

## Multimodal

- DeepSeek V4.1 Flash: 39.1 (#61)
- Llama 3.2 1B: —

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |

## Multilingual

- DeepSeek V4.1 Flash: 55.0 (#35)
- Llama 3.2 1B: 23.8 (#292)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1448 | 973 |
| LMArena Chinese | 1497 | 959 |
| LMArena German | 1484 | 1014 |
| LMArena Russian | 1471 | 941 |
| LMArena French | 1452 | — |
| LMArena Japanese | 1412 | — |
| LMArena Korean | 1452 | — |
| LMArena Spanish | 1459 | — |

## Instruction Following

- DeepSeek V4.1 Flash: 77.3 (#26)
- Llama 3.2 1B: 52.4 (#290)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1474 | 1031 |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#47)
- Llama 3.2 1B: 31.9 (#274)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1475 | 1050 |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.4 (#48)
- Llama 3.2 1B: 21.3 (#310)

| Benchmark | DeepSeek V4.1 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1462 | 1055 |
| LMArena Creative Writing | 1435 | 1033 |
| EQ-Bench Creative Writing | 1540 | 200 |
| LMArena Multi-Turn | 1457 | 1030 |

## FAQ

### Is DeepSeek V4.1 Flash better than Llama 3.2 1B?

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4.1 Flash 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 V4.1 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4.1 Flash or Llama 3.2 1B better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4.1 Flash does, with 1M tokens against 60K.

### How many benchmarks do DeepSeek V4.1 Flash and Llama 3.2 1B share?

17 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Llama 3.2 1B has 22.