# Gemini 3.6 Flash vs Llama 3-8B

> Gemini 3.6 Flash is the stronger model overall, scoring 54.1 to 25.3 on the Noometry Index. Llama 3-8B costs 11× less per token, which makes it the better buy when Gemini 3.6 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-6-flash-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. Gemini 3.6 Flash 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 Gemini 3.6 Flash leads 67.8 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.2% for Gemini 3.6 Flash and 1.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.6 Flash.
- Gemini 3.6 Flash accepts more context: 1.05M tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 54.1 | 25.3 |
| Rank | 30 | 338 |
| Context | 1.05M | 8K |
| Input $/M | $0.75 | $0.14 |
| Output $/M | $3.75 | $0.14 |
| Weights | Proprietary | Open |

## Coding

- Gemini 3.6 Flash: 49.6 (#45)
- Llama 3-8B: 30.5 (#285)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1492 | 1152 |
| DeepSWE | 46.7% | — |
| FrontierCode | 34.4% | — |
| LMArena WebDev | 1536 | — |
| SciCode | 52.7% | — |
| WeirdML | 56.1% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 715.52 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- Gemini 3.6 Flash: 32.4 (#56)
- Llama 3-8B: —

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| APEX-Agents | 46.9% | — |
| GDP.pdf | 14% | — |

## Reasoning

- Gemini 3.6 Flash: 58.9 (#20)
- Llama 3-8B: 13.8 (#321)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 43% | 0% |
| LMArena Hard Prompts | 1486 | 1133 |
| DTBench | 95.5% | 43.9% |
| Epoch Capabilities Index | 154.36 | 116.33 |
| ARC-AGI-2 | 60.4% | — |
| NYT Connections (extended) | 89% | — |
| ARC-AGI-1 | 91.2% | — |
| CritPt | 10.6% | — |
| Mystery Game Puzzles | 30% | — |
| LMCA | 44.9% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- Gemini 3.6 Flash: 57.0 (#33)
- Llama 3-8B: 8.3 (#317)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.2% | 1.9% |
| LMArena Math | 1504 | 1151 |
| FrontierMath (Tiers 1-3) | 58.9% | — |
| FrontierMath Tier 4 | 22% | — |
| MathArena Final-Answer Competitions | 70.8% | — |
| ProofBench | 36% | — |
| MATH Level 5 | — | 6.1% |

## Knowledge

- Gemini 3.6 Flash: 67.8 (#7)
- Llama 3-8B: 7.8 (#303)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 94.1% | 26.1% |
| LMArena Expert | 1489 | 1113 |
| SimpleQA Verified | 66.2% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multimodal

- Gemini 3.6 Flash: 38.7 (#55)
- Llama 3-8B: —

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 31.2% | — |
| Furniture Assembly | 23.3% | — |
| LMArena Document | 1456 | — |

## Multilingual

- Gemini 3.6 Flash: 56.8 (#17)
- Llama 3-8B: 31.0 (#256)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1470 | 1098 |
| LMArena Chinese | 1530 | 1076 |
| LMArena French | 1501 | 1158 |
| LMArena German | 1478 | 1104 |
| LMArena Japanese | 1477 | 967 |
| LMArena Korean | 1435 | 1003 |
| LMArena Russian | 1490 | 1109 |
| LMArena Spanish | 1477 | 1172 |

## Instruction Following

- Gemini 3.6 Flash: 77.2 (#27)
- Llama 3-8B: 58.7 (#255)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1467 | 1127 |

## Long Context

- Gemini 3.6 Flash: 45.1 (#47)
- Llama 3-8B: 34.2 (#246)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1475 | 1128 |

## Writing & Preference

- Gemini 3.6 Flash: 68.3 (#26)
- Llama 3-8B: 37.7 (#251)

| Benchmark | Gemini 3.6 Flash | Llama 3-8B |
|---|---|---|
| LMArena Text | 1478 | 1166 |
| LMArena Creative Writing | 1464 | 1150 |
| LMArena Multi-Turn | 1481 | 1152 |
| EQ-Bench Creative Writing | 1604 | — |

## FAQ

### Is Gemini 3.6 Flash better than Llama 3-8B?

Gemini 3.6 Flash is the stronger model overall, scoring 54.1 to 25.3 on the Noometry Index. Llama 3-8B costs 11× less per token, which makes it the better buy when Gemini 3.6 Flash's lead doesn't matter for your workload.

### Which is cheaper, Gemini 3.6 Flash 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 3.6 Flash lists at $0.75 and $3.75.

### Is Gemini 3.6 Flash or Llama 3-8B better for coding?

Gemini 3.6 Flash scores higher on coding benchmarks: 49.6 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.6 Flash does, with 1.05M tokens against 8K.

### How many benchmarks do Gemini 3.6 Flash and Llama 3-8B share?

22 benchmarks have published results for both models. Gemini 3.6 Flash has 46 scored results on Noometry and Llama 3-8B has 34.