# Llama 3-8B vs Llama 4 Maverick

> Llama 4 Maverick is the stronger model overall, scoring 30.6 to 25.3 on the Noometry Index. Llama 3-8B costs 2.2× less per token, which makes it the better buy when Llama 4 Maverick's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-8b-vs-llama-4-maverick
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. Llama 3-8B scores higher in 3 categories and Llama 4 Maverick in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Maverick leads 33.3 to 7.8.
- The biggest single-benchmark swing is MATH Level 5: 6.1% for Llama 3-8B and 73% for Llama 4 Maverick.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.19 / $0.65 for Llama 4 Maverick.
- Llama 4 Maverick accepts more context: 128K tokens versus 8K.

## Snapshot

| | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 25.3 | 30.6 |
| Rank | 338 | 276 |
| Context | 8K | 128K |
| Input $/M | $0.14 | $0.19 |
| Output $/M | $0.14 | $0.65 |
| Weights | Open | Open |

## Coding

- Llama 3-8B: 30.5 (#285)
- Llama 4 Maverick: 26.3 (#318)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| BigCodeBench Instruct | 31.9% | 49.7% |
| LMArena Coding | 1152 | 1302 |
| BigCodeBench Complete | 36.9% | 61.4% |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| SciCode | — | 33.1% |
| WeirdML | — | 24.5% |
| ALE-Bench | — | 172.97 |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |

## Agentic & Tool Use

- Llama 3-8B: —
- Llama 4 Maverick: 27.9 (#86)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.3% |

## Reasoning

- Llama 3-8B: 13.8 (#321)
- Llama 4 Maverick: 10.1 (#337)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Hard Prompts | 1133 | 1281 |
| DTBench | 43.9% | 61.9% |
| Epoch Capabilities Index | 116.33 | 132.2 |
| ForecastBench | 58.6 | 57.5 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| Kagi LLM Benchmark | — | 55.9% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| EnigmaEval | — | 0.6% |
| LMCA | — | 15.9% |
| Adversarial NLI | 57.3% | — |
| WinoGrande | 75.7% | — |

## Math

- Llama 3-8B: 8.3 (#317)
- Llama 4 Maverick: 24.6 (#256)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | 20.6% |
| LMArena Math | 1151 | 1299 |
| MATH Level 5 | 6.1% | 73% |
| Omni-MATH | — | 42.2% |
| FrontierMath (Feb 2025 set) | — | 0.7% |

## Knowledge

- Llama 3-8B: 7.8 (#303)
- Llama 4 Maverick: 33.3 (#196)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| GPQA Diamond | 26.1% | 67% |
| LMArena Expert | 1113 | 1259 |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| GPQA (HELM) | — | 65% |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |

## Multimodal

- Llama 3-8B: —
- Llama 4 Maverick: 30.9 (#100)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | — | 1141 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |

## Multilingual

- Llama 3-8B: 31.0 (#256)
- Llama 4 Maverick: 42.4 (#190)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1098 | 1269 |
| LMArena Chinese | 1076 | 1277 |
| LMArena French | 1158 | 1258 |
| LMArena German | 1104 | 1291 |
| LMArena Japanese | 967 | 1207 |
| LMArena Korean | 1003 | 1203 |
| LMArena Russian | 1109 | 1286 |
| LMArena Spanish | 1172 | 1293 |

## Instruction Following

- Llama 3-8B: 58.7 (#255)
- Llama 4 Maverick: 71.9 (#140)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Instruction Following | 1127 | 1267 |
| IFEval | — | 90.8% |

## Long Context

- Llama 3-8B: 34.2 (#246)
- Llama 4 Maverick: 31.4 (#274)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1128 | 1280 |
| Fiction.LiveBench | — | 46.2% |

## Writing & Preference

- Llama 3-8B: 37.7 (#251)
- Llama 4 Maverick: 39.0 (#247)

| Benchmark | Llama 3-8B | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1166 | 1287 |
| LMArena Creative Writing | 1150 | 1266 |
| LMArena Multi-Turn | 1152 | 1289 |
| Short-Story Creative Writing | — | 62% |
| EQ-Bench Creative Writing | — | 860 |
| WildBench | — | 80% |

## FAQ

### Is Llama 3-8B better than Llama 4 Maverick?

Llama 4 Maverick is the stronger model overall, scoring 30.6 to 25.3 on the Noometry Index. Llama 3-8B costs 2.2× less per token, which makes it the better buy when Llama 4 Maverick's lead doesn't matter for your workload.

### Which is cheaper, Llama 3-8B or Llama 4 Maverick?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; Llama 4 Maverick lists at $0.19 and $0.65.

### Is Llama 3-8B or Llama 4 Maverick better for coding?

Llama 3-8B scores higher on coding benchmarks: 30.5 versus 26.3 in the Noometry coding category.

### Which has the bigger context window?

Llama 4 Maverick does, with 128K tokens against 8K.

### How many benchmarks do Llama 3-8B and Llama 4 Maverick share?

25 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and Llama 4 Maverick has 54.