# Gemini 3.6 Flash vs Llama 3.2 1B

> Gemini 3.6 Flash is the stronger model overall, scoring 54.1 to 19.9 on the Noometry Index. Llama 3.2 1B costs 21× 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-3-2-1b
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. Gemini 3.6 Flash scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Gemini 3.6 Flash leads 67.8 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.2% for Gemini 3.6 Flash and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 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 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 54.1 | 19.9 |
| Rank | 30 | 349 |
| Context | 1.05M | 60K |
| Input $/M | $0.75 | $0.027 |
| Output $/M | $3.75 | $0.20 |
| Weights | Proprietary | Open |

## Coding

- Gemini 3.6 Flash: 49.6 (#45)
- Llama 3.2 1B: 20.6 (#333)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1492 | 1071 |
| DeepSWE | 46.7% | — |
| FrontierCode | 34.4% | — |
| LMArena WebDev | 1536 | — |
| SciCode | 52.7% | — |
| WeirdML | 56.1% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 715.52 | — |

## Agentic & Tool Use

- Gemini 3.6 Flash: 32.4 (#56)
- Llama 3.2 1B: 14.5 (#142)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 46.9% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| GDP.pdf | 14% | — |

## Reasoning

- Gemini 3.6 Flash: 58.9 (#20)
- Llama 3.2 1B: 15.6 (#304)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 43% | 0% |
| LMArena Hard Prompts | 1486 | 1044 |
| Epoch Capabilities Index | 154.36 | 101.95 |
| ARC-AGI-2 | 60.4% | — |
| NYT Connections (extended) | 89% | — |
| ARC-AGI-1 | 91.2% | — |
| CritPt | 10.6% | — |
| Mystery Game Puzzles | 30% | — |
| DTBench | 95.5% | — |
| LMCA | 44.9% | — |

## Math

- Gemini 3.6 Flash: 57.0 (#33)
- Llama 3.2 1B: 9.7 (#308)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.2% | 0.6% |
| LMArena Math | 1504 | 1086 |
| FrontierMath (Tiers 1-3) | 58.9% | — |
| FrontierMath Tier 4 | 22% | — |
| MathArena Final-Answer Competitions | 70.8% | — |
| ProofBench | 36% | — |

## Knowledge

- Gemini 3.6 Flash: 67.8 (#7)
- Llama 3.2 1B: 7.2 (#307)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 94.1% | 23.9% |
| LMArena Expert | 1489 | 1007 |
| SimpleQA Verified | 66.2% | — |

## Multimodal

- Gemini 3.6 Flash: 38.7 (#55)
- Llama 3.2 1B: —

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 31.2% | — |
| Furniture Assembly | 23.3% | — |
| LMArena Document | 1456 | — |

## Multilingual

- Gemini 3.6 Flash: 56.8 (#17)
- Llama 3.2 1B: 23.9 (#287)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1470 | 973 |
| LMArena Chinese | 1530 | 958 |
| LMArena German | 1478 | 1014 |
| LMArena Russian | 1490 | 941 |
| LMArena French | 1501 | — |
| LMArena Japanese | 1477 | — |
| LMArena Korean | 1435 | — |
| LMArena Spanish | 1477 | — |

## Instruction Following

- Gemini 3.6 Flash: 77.2 (#27)
- Llama 3.2 1B: 52.6 (#285)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1467 | 1031 |

## Long Context

- Gemini 3.6 Flash: 45.1 (#47)
- Llama 3.2 1B: 31.9 (#269)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1475 | 1050 |

## Writing & Preference

- Gemini 3.6 Flash: 68.3 (#26)
- Llama 3.2 1B: 21.4 (#305)

| Benchmark | Gemini 3.6 Flash | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1478 | 1055 |
| LMArena Creative Writing | 1464 | 1033 |
| EQ-Bench Creative Writing | 1604 | 200 |
| LMArena Multi-Turn | 1481 | 1030 |

## FAQ

### Is Gemini 3.6 Flash better than Llama 3.2 1B?

Gemini 3.6 Flash is the stronger model overall, scoring 54.1 to 19.9 on the Noometry Index. Llama 3.2 1B costs 21× 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 3.2 1B?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Gemini 3.6 Flash lists at $0.75 and $3.75.

### Is Gemini 3.6 Flash or Llama 3.2 1B better for coding?

Gemini 3.6 Flash scores higher on coding benchmarks: 49.6 versus 20.6 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.6 Flash does, with 1.05M tokens against 60K.

### How many benchmarks do Gemini 3.6 Flash and Llama 3.2 1B share?

18 benchmarks have published results for both models. Gemini 3.6 Flash has 46 scored results on Noometry and Llama 3.2 1B has 22.