# DeepSeek-V3.2-Speciale vs Llama 3-8B

> DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 25.3 on the Noometry Index. Llama 3-8B costs 6.1× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-speciale-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 13.8.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- DeepSeek-V3.2-Speciale accepts more context: 128K tokens versus 8K.

## Snapshot

| | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.1 | 25.3 |
| Rank | 159 | 338 |
| Context | 128K | 8K |
| Input $/M | $0.58 | $0.14 |
| Output $/M | $1.68 | $0.14 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Speciale: 39.9 (#136)
- Llama 3-8B: 30.5 (#285)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| WeirdML | 46.7% | — |
| BigCodeBench Instruct | — | 31.9% |
| LMArena Coding | — | 1152 |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Reasoning

- DeepSeek-V3.2-Speciale: 32.1 (#65)
- Llama 3-8B: 13.8 (#321)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| SimpleBench | 52.6% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | — | 1133 |
| DTBench | — | 43.9% |
| Adversarial NLI | — | 57.3% |
| Epoch Capabilities Index | — | 116.33 |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- DeepSeek-V3.2-Speciale: —
- Llama 3-8B: 8.3 (#317)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 1.9% |
| LMArena Math | — | 1151 |
| MATH Level 5 | — | 6.1% |

## Knowledge

- DeepSeek-V3.2-Speciale: —
- Llama 3-8B: 7.8 (#303)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| GPQA Diamond | — | 26.1% |
| LMArena Expert | — | 1113 |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- DeepSeek-V3.2-Speciale: —
- Llama 3-8B: 31.0 (#256)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| LMArena Non-English | — | 1098 |
| LMArena Chinese | — | 1076 |
| LMArena French | — | 1158 |
| LMArena German | — | 1104 |
| LMArena Japanese | — | 967 |
| LMArena Korean | — | 1003 |
| LMArena Russian | — | 1109 |
| LMArena Spanish | — | 1172 |

## Instruction Following

- DeepSeek-V3.2-Speciale: —
- Llama 3-8B: 58.7 (#255)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | — | 1127 |

## Long Context

- DeepSeek-V3.2-Speciale: —
- Llama 3-8B: 34.2 (#246)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | — | 1128 |

## Writing & Preference

- DeepSeek-V3.2-Speciale: 46.1 (#217)
- Llama 3-8B: 37.7 (#251)

| Benchmark | DeepSeek-V3.2-Speciale | Llama 3-8B |
|---|---|---|
| LMArena Text | — | 1166 |
| LMArena Creative Writing | — | 1150 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1152 |

## FAQ

### Is DeepSeek-V3.2-Speciale better than Llama 3-8B?

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 25.3 on the Noometry Index. Llama 3-8B costs 6.1× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Speciale or Llama 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.

### Is DeepSeek-V3.2-Speciale or Llama 3-8B better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 39.9 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Speciale does, with 128K tokens against 8K.

### How many benchmarks do DeepSeek-V3.2-Speciale and Llama 3-8B share?

0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Llama 3-8B has 34.