# Gemini 2.5 Flash-Lite vs Llama 3-8B

> Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 25.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gemini-2-5-flash-lite-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 7 categories and Llama 3-8B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Gemini 2.5 Flash-Lite leads 35.5 to 8.3.
- The biggest single-benchmark swing is DTBench: 62.8% for Gemini 2.5 Flash-Lite and 43.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.10 / $0.40 for Gemini 2.5 Flash-Lite.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 36.4 | 25.3 |
| Rank | 208 | 338 |
| Context | 1.05M | 8K |
| Input $/M | $0.10 | $0.14 |
| Output $/M | $0.40 | $0.14 |
| Weights | Proprietary | Open |

## Coding

- Gemini 2.5 Flash-Lite: 38.0 (#167)
- Llama 3-8B: 30.5 (#285)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1373 | 1152 |
| WeirdML | 35.2% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 325.9 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- Gemini 2.5 Flash-Lite: 27.8 (#89)
- Llama 3-8B: —

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | — |

## Reasoning

- Gemini 2.5 Flash-Lite: 21.5 (#196)
- Llama 3-8B: 13.8 (#321)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1377 | 1133 |
| DTBench | 62.8% | 43.9% |
| Epoch Capabilities Index | 133.93 | 116.33 |
| Kagi LLM Benchmark | 40.5% | — |
| Chess Puzzles | — | 0% |
| LMCA | 18.1% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- Gemini 2.5 Flash-Lite: 35.5 (#144)
- Llama 3-8B: 8.3 (#317)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Math | 1373 | 1151 |
| OTIS Mock AIME 2024-2025 | — | 1.9% |
| Omni-MATH | 48% | — |
| MATH Level 5 | — | 6.1% |

## Knowledge

- Gemini 2.5 Flash-Lite: 32.1 (#206)
- Llama 3-8B: 7.8 (#303)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Expert | 1373 | 1113 |
| GPQA Diamond | — | 26.1% |
| MMLU-Pro | 53.7% | — |
| Vectara Hallucination Rate | 3.3% | — |
| GPQA (HELM) | 30.9% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multimodal

- Gemini 2.5 Flash-Lite: 28.6 (#109)
- Llama 3-8B: —

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |

## Multilingual

- Gemini 2.5 Flash-Lite: 49.5 (#129)
- Llama 3-8B: 31.0 (#256)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1369 | 1098 |
| LMArena Chinese | 1404 | 1076 |
| LMArena French | 1388 | 1158 |
| LMArena German | 1390 | 1104 |
| LMArena Japanese | 1359 | 967 |
| LMArena Korean | 1358 | 1003 |
| LMArena Russian | 1373 | 1109 |
| LMArena Spanish | 1396 | 1172 |

## Instruction Following

- Gemini 2.5 Flash-Lite: 70.2 (#163)
- Llama 3-8B: 58.7 (#255)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1366 | 1127 |
| IFEval | 81% | — |

## Long Context

- Gemini 2.5 Flash-Lite: 33.3 (#257)
- Llama 3-8B: 34.2 (#246)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1373 | 1128 |
| Fiction.LiveBench | 47.2% | — |

## Writing & Preference

- Gemini 2.5 Flash-Lite: 57.0 (#130)
- Llama 3-8B: 37.7 (#251)

| Benchmark | Gemini 2.5 Flash-Lite | Llama 3-8B |
|---|---|---|
| LMArena Text | 1379 | 1166 |
| LMArena Creative Writing | 1366 | 1150 |
| LMArena Multi-Turn | 1367 | 1152 |
| WildBench | 81.8% | — |

## FAQ

### Is Gemini 2.5 Flash-Lite better than Llama 3-8B?

Gemini 2.5 Flash-Lite is the stronger model overall, scoring 36.4 to 25.3 on the Noometry Index.

### Which is cheaper, Gemini 2.5 Flash-Lite 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; Gemini 2.5 Flash-Lite lists at $0.10 and $0.40.

### Is Gemini 2.5 Flash-Lite or Llama 3-8B better for coding?

Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.0 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Flash-Lite does, with 1.05M tokens against 8K.

### How many benchmarks do Gemini 2.5 Flash-Lite and Llama 3-8B share?

19 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and Llama 3-8B has 34.