# Gemini 3.7 Flash vs Llama 3.1-8B

> Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 26× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-7-flash-vs-llama-3-1-8b
- Last updated: 2026-10-12
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. Gemini 3.7 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 3.7 Flash leads 69.7 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.2% for Gemini 3.7 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 3.7 Flash.
- Gemini 3.7 Flash accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 59.8 | 23.0 |
| Rank | 14 | 352 |
| Context | 1.05M | 128K |
| Input $/M | $0.75 | $0.05 |
| Output $/M | $3.75 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- Gemini 3.7 Flash: 56.2 (#22)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| SciCode | 59.8% | 13.2% |
| LMArena Coding | 1497 | 1195 |
| DeepSWE | 65.5% | — |
| FrontierCode | 43.6% | — |
| LMArena WebDev | 1592 | — |
| FrontierSWE | 20.3% | — |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 904.3 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Gemini 3.7 Flash: 42.1 (#19)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 67.8% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| Remote Labor Index | 5% | — |
| BALROG | — | 15.1% |
| GDP.pdf | 23.8% | — |

## Reasoning

- Gemini 3.7 Flash: 70.0 (#15)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| CritPt | 14.3% | 0% |
| Chess Puzzles | 47% | 0% |
| LMArena Hard Prompts | 1494 | 1175 |
| DTBench | 96.8% | 50.9% |
| LMCA | 50.4% | 5.4% |
| Epoch Capabilities Index | 157.27 | 116.57 |
| ARC-AGI-2 | 84.6% | — |
| NYT Connections (extended) | 94% | — |
| ARC-AGI-1 | 95.5% | — |
| Mystery Game Puzzles | 37% | — |
| PIQA | — | 81.2% |

## Math

- Gemini 3.7 Flash: 69.6 (#23)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.2% | 1.7% |
| LMArena Math | 1507 | 1179 |
| FrontierMath (Tiers 1-3) | 71.6% | — |
| FrontierMath Tier 4 | 36.6% | — |
| ProofBench | 58% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- Gemini 3.7 Flash: 69.7 (#5)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 94.8% | 27% |
| LMArena Expert | 1508 | 1144 |
| SimpleQA Verified | 69.2% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Gemini 3.7 Flash: 37.3 (#73)
- Llama 3.1-8B: —

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1316 | — |
| Furniture Assembly | 26.7% | — |

## Multilingual

- Gemini 3.7 Flash: 57.6 (#7)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1484 | 1148 |
| LMArena Chinese | 1548 | 1151 |
| LMArena French | 1505 | 1177 |
| LMArena German | 1498 | 1144 |
| LMArena Japanese | 1512 | 1061 |
| LMArena Korean | 1483 | 1053 |
| LMArena Russian | 1516 | 1158 |
| LMArena Spanish | 1503 | 1169 |

## Instruction Following

- Gemini 3.7 Flash: 77.7 (#15)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1483 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- Gemini 3.7 Flash: 45.7 (#30)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1492 | 1182 |

## Writing & Preference

- Gemini 3.7 Flash: 71.2 (#20)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | Gemini 3.7 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1486 | 1187 |
| LMArena Creative Writing | 1490 | 1154 |
| EQ-Bench Creative Writing | 1723 | 713 |
| LMArena Multi-Turn | 1489 | 1172 |
| WildBench | — | 68.7% |

## FAQ

### Is Gemini 3.7 Flash better than Llama 3.1-8B?

Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 26× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.

### Which is cheaper, Gemini 3.7 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 3.7 Flash lists at $0.75 and $3.75.

### Is Gemini 3.7 Flash or Llama 3.1-8B better for coding?

Gemini 3.7 Flash scores higher on coding benchmarks: 56.2 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.7 Flash does, with 1.05M tokens against 128K.

### How many benchmarks do Gemini 3.7 Flash and Llama 3.1-8B share?

26 benchmarks have published results for both models. Gemini 3.7 Flash has 44 scored results on Noometry and Llama 3.1-8B has 43.