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

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 25.3 on the Noometry Index. Llama 3-8B costs 2.1× 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-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 1.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 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 8K.

## Snapshot

| | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.9 | 25.3 |
| Rank | 68 | 338 |
| Context | 164K | 8K |
| Input $/M | $0.26 | $0.14 |
| Output $/M | $0.38 | $0.14 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- Llama 3-8B: 30.5 (#285)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1454 | 1152 |
| 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 | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- Llama 3-8B: —

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- Llama 3-8B: 13.8 (#321)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1433 | 1133 |
| DTBench | 87.7% | 43.9% |
| Epoch Capabilities Index | 146.28 | 116.33 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Thematic Generalization | 65% | — |
| LMCA | 29.1% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- Llama 3-8B: 8.3 (#317)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 1.9% |
| LMArena Math | 1435 | 1151 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- Llama 3-8B: 7.8 (#303)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 83.4% | 26.1% |
| LMArena Expert | 1436 | 1113 |
| Vectara Hallucination Rate | 5.3% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- Llama 3-8B: 31.0 (#256)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1409 | 1098 |
| LMArena Chinese | 1461 | 1076 |
| LMArena French | 1435 | 1158 |
| LMArena German | 1440 | 1104 |
| LMArena Japanese | 1377 | 967 |
| LMArena Korean | 1372 | 1003 |
| LMArena Russian | 1423 | 1109 |
| LMArena Spanish | 1441 | 1172 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- Llama 3-8B: 58.7 (#255)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1127 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- Llama 3-8B: 34.2 (#246)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1428 | 1128 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- Llama 3-8B: 37.7 (#251)

| Benchmark | DeepSeek-V3.2-Exp | Llama 3-8B |
|---|---|---|
| LMArena Text | 1425 | 1166 |
| LMArena Creative Writing | 1403 | 1150 |
| LMArena Multi-Turn | 1427 | 1152 |
| EQ-Bench Creative Writing | 1515 | — |

## FAQ

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

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 25.3 on the Noometry Index. Llama 3-8B costs 2.1× 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 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

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

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 8K.

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

22 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Llama 3-8B has 34.