# DeepSeek-V3.2-Exp vs Llama 3.2 1B

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 4.1× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-llama-3-2-1b
- Last updated: 2026-10-12
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. DeepSeek-V3.2-Exp 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 knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp 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 $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 60K.

## Snapshot

| | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.3 | 20.1 |
| Rank | 78 | 354 |
| Context | 164K | 60K |
| Input $/M | $0.26 | $0.027 |
| Output $/M | $0.38 | $0.20 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.5 (#65)
- Llama 3.2 1B: 21.1 (#338)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1454 | 1070 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 32.7 (#59)
- Llama 3.2 1B: 14.6 (#150)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 10.8% |
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| TheAgentCompany | 42.9% | — |
| BALROG | — | 6.6% |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.1 (#208)
- Llama 3.2 1B: 16.2 (#308)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1434 | 1044 |
| Epoch Capabilities Index | 146.27 | 101.99 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |

## Math

- DeepSeek-V3.2-Exp: 41.7 (#87)
- Llama 3.2 1B: 10.4 (#313)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 0.6% |
| LMArena Math | 1435 | 1086 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.7 (#66)
- Llama 3.2 1B: 7.2 (#312)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 83.4% | 23.9% |
| LMArena Expert | 1436 | 1007 |
| Vectara Hallucination Rate | 5.3% | — |

## Multilingual

- DeepSeek-V3.2-Exp: 52.2 (#90)
- Llama 3.2 1B: 23.8 (#292)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1409 | 973 |
| LMArena Chinese | 1461 | 959 |
| LMArena German | 1440 | 1014 |
| LMArena Russian | 1424 | 941 |
| LMArena French | 1433 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Spanish | 1440 | — |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.5 (#93)
- Llama 3.2 1B: 52.4 (#290)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1031 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#16)
- Llama 3.2 1B: 31.9 (#274)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1428 | 1050 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.4 (#77)
- Llama 3.2 1B: 21.3 (#310)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1425 | 1055 |
| LMArena Creative Writing | 1403 | 1033 |
| EQ-Bench Creative Writing | 1515 | 200 |
| LMArena Multi-Turn | 1427 | 1030 |

## FAQ

### Is DeepSeek-V3.2-Exp better than Llama 3.2 1B?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 4.1× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Exp 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-V3.2-Exp lists at $0.26 and $0.38.

### Is DeepSeek-V3.2-Exp or Llama 3.2 1B better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 21.1 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 60K.

### How many benchmarks do DeepSeek-V3.2-Exp and Llama 3.2 1B share?

19 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Llama 3.2 1B has 22.