# Gemini 3.8 Flash vs Llama 3.1-8B

> Gemini 3.8 Flash is the stronger model overall, scoring 59.5 to 22.8 on the Noometry Index. Llama 3.1-8B costs 26× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-8-flash-vs-llama-3-1-8b
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. Gemini 3.8 Flash scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Gemini 3.8 Flash leads 74.9 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for Gemini 3.8 Flash and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.8 Flash.
- Gemini 3.8 Flash accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 59.5 | 22.8 |
| Rank | 13 | 347 |
| Context | 1.05M | 128K |
| Input $/M | $0.75 | $0.05 |
| Output $/M | $3.75 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- Gemini 3.8 Flash: 56.4 (#17)
- Llama 3.1-8B: 19.9 (#335)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| SciCode | 56.6% | 13.2% |
| LMArena Coding | 1512 | 1195 |
| DeepSWE | 73.8% | — |
| FrontierCode | 41.2% | — |
| CursorBench | 39.6% | — |
| LMArena WebDev | 1581 | — |
| FrontierSWE | 19.6% | — |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,270 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Gemini 3.8 Flash: 40.3 (#22)
- Llama 3.1-8B: 22.3 (#123)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 64.3% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
| GDP.pdf | 23.4% | — |
| Vending-Bench 2 | 5,094 | — |

## Reasoning

- Gemini 3.8 Flash: 64.7 (#13)
- Llama 3.1-8B: 14.5 (#316)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| CritPt | 18.3% | 0% |
| Chess Puzzles | 61% | 0% |
| LMArena Hard Prompts | 1506 | 1175 |
| DTBench | 95.7% | 50.9% |
| LMCA | 52.9% | 5.4% |
| Epoch Capabilities Index | 157.13 | 116.48 |
| NYT Connections (extended) | 97.4% | — |
| Mystery Game Puzzles | 47% | — |
| Surface Evolver Bench | 76.9% | — |
| PIQA | — | 81.2% |

## Math

- Gemini 3.8 Flash: 65.2 (#22)
- Llama 3.1-8B: 9.5 (#311)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 1.7% |
| LMArena Math | 1528 | 1179 |
| FrontierMath (Tiers 1-3) | 68.4% | — |
| FrontierMath Tier 4 | 22% | — |
| ProofBench | 48% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- Gemini 3.8 Flash: 74.9 (#2)
- Llama 3.1-8B: 8.0 (#302)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 95.4% | 27% |
| LMArena Expert | 1529 | 1144 |
| Humanity's Last Exam | 44.5% | — |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Gemini 3.8 Flash: 40.9 (#33)
- Llama 3.1-8B: —

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1312 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 31.7% | — |

## Multilingual

- Gemini 3.8 Flash: 57.9 (#5)
- Llama 3.1-8B: 34.2 (#244)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1486 | 1148 |
| LMArena Chinese | 1547 | 1151 |
| LMArena French | 1502 | 1176 |
| LMArena German | 1470 | 1144 |
| LMArena Japanese | 1505 | 1061 |
| LMArena Korean | 1479 | 1053 |
| LMArena Russian | 1507 | 1158 |
| LMArena Spanish | 1492 | 1169 |

## Instruction Following

- Gemini 3.8 Flash: 78.2 (#11)
- Llama 3.1-8B: 59.1 (#253)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1489 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- Gemini 3.8 Flash: 46.2 (#23)
- Llama 3.1-8B: 35.8 (#233)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1506 | 1182 |

## Writing & Preference

- Gemini 3.8 Flash: 72.1 (#16)
- Llama 3.1-8B: 29.9 (#285)

| Benchmark | Gemini 3.8 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1494 | 1187 |
| LMArena Creative Writing | 1485 | 1154 |
| EQ-Bench Creative Writing | 1748 | 713 |
| LMArena Multi-Turn | 1503 | 1172 |
| WildBench | — | 68.7% |

## FAQ

### Is Gemini 3.8 Flash better than Llama 3.1-8B?

Gemini 3.8 Flash is the stronger model overall, scoring 59.5 to 22.8 on the Noometry Index. Llama 3.1-8B costs 26× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.

### Which is cheaper, Gemini 3.8 Flash or Llama 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Gemini 3.8 Flash lists at $0.75 and $3.75.

### Is Gemini 3.8 Flash or Llama 3.1-8B better for coding?

Gemini 3.8 Flash scores higher on coding benchmarks: 56.4 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.8 Flash does, with 1.05M tokens against 128K.

### How many benchmarks do Gemini 3.8 Flash and Llama 3.1-8B share?

26 benchmarks have published results for both models. Gemini 3.8 Flash has 46 scored results on Noometry and Llama 3.1-8B has 43.