# Gemini 2.5 Pro vs Llama 3.2 1B

> Gemini 2.5 Pro is the stronger model overall, scoring 44.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 49× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-2-5-pro-vs-llama-3-2-1b
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. Gemini 2.5 Pro 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 2.5 Pro leads 55.8 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 84.7% for Gemini 2.5 Pro 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 $1.25 / $10 for Gemini 2.5 Pro.
- Gemini 2.5 Pro accepts more context: 1.05M tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 44.9 | 19.9 |
| Rank | 67 | 349 |
| Context | 1.05M | 60K |
| Input $/M | $1.25 | $0.027 |
| Output $/M | $10 | $0.20 |
| Weights | Proprietary | Open |

## Coding

- Gemini 2.5 Pro: 42.8 (#86)
- Llama 3.2 1B: 20.6 (#333)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1452 | 1071 |
| SWE-bench Verified | 57.6% | — |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| LMArena WebDev | 1227 | — |
| SciCode | 42.8% | — |
| GSO | 3.9% | — |
| WeirdML | 54% | — |
| BigCodeBench Instruct | — | 8.2% |
| LiveBench Coding | 85.9% | — |
| BigCodeBench Complete | — | 11.3% |
| CadEval | 64% | — |
| ALE-Bench | 785.52 | — |
| AlgoTune | 1.51 | — |

## Agentic & Tool Use

- Gemini 2.5 Pro: 29.1 (#81)
- Llama 3.2 1B: 14.5 (#142)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| BALROG | 43.3% | 6.6% |
| Terminal-Bench | 32.6% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| GDPval | 23.3% | — |
| Remote Labor Index | 0.8% | — |
| TheAgentCompany | 30.3% | — |
| τ²-bench Banking | 13.7% | — |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1142 | — |
| METR Time Horizons | 55.4% | — |
| Vending-Bench 2 | 573.64 | — |

## Reasoning

- Gemini 2.5 Pro: 28.8 (#85)
- Llama 3.2 1B: 15.6 (#304)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 20% | 0% |
| LMArena Hard Prompts | 1455 | 1044 |
| Epoch Capabilities Index | 145.32 | 101.95 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 41% | — |
| CritPt | 2% | — |
| EnigmaEval | 5.6% | — |
| LiveBench Reasoning | 89.8% | — |
| DTBench | 82.4% | — |
| LiveBench Data Analysis | 79.9% | — |
| LMCA | 34.8% | — |
| ForecastBench | 61.3 | — |
| LiveBench | 82.3% | — |

## Math

- Gemini 2.5 Pro: 31.9 (#191)
- Llama 3.2 1B: 9.7 (#308)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.7% | 0.6% |
| LMArena Math | 1450 | 1086 |
| FrontierMath (Tiers 1-3) | 24.6% | — |
| FrontierMath Tier 4 | 0% | — |
| Omni-MATH | 41.6% | — |
| LiveBench Math | 90.2% | — |
| MATH Level 5 | 95.9% | — |
| FrontierMath (Feb 2025 set) | 14.1% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Gemini 2.5 Pro: 55.8 (#41)
- Llama 3.2 1B: 7.2 (#307)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 85.3% | 23.9% |
| LMArena Expert | 1452 | 1007 |
| Humanity's Last Exam | 21.6% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| GPQA (HELM) | 74.9% | — |

## Multimodal

- Gemini 2.5 Pro: 44.5 (#16)
- Llama 3.2 1B: —

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |

## Multilingual

- Gemini 2.5 Pro: 55.5 (#30)
- Llama 3.2 1B: 23.9 (#287)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1452 | 973 |
| LMArena Chinese | 1507 | 958 |
| LMArena German | 1487 | 1014 |
| LMArena Russian | 1460 | 941 |
| LMArena French | 1473 | — |
| LMArena Japanese | 1462 | — |
| LMArena Korean | 1435 | — |
| LMArena Spanish | 1473 | — |

## Instruction Following

- Gemini 2.5 Pro: 75.2 (#71)
- Llama 3.2 1B: 52.6 (#285)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1436 | 1031 |
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |

## Long Context

- Gemini 2.5 Pro: 59.7 (#5)
- Llama 3.2 1B: 31.9 (#269)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1449 | 1050 |
| Fiction.LiveBench | 91.7% | — |

## Writing & Preference

- Gemini 2.5 Pro: 63.9 (#60)
- Llama 3.2 1B: 21.4 (#305)

| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1458 | 1055 |
| LMArena Creative Writing | 1453 | 1033 |
| EQ-Bench Creative Writing | 1421 | 200 |
| LMArena Multi-Turn | 1453 | 1030 |
| Short-Story Creative Writing | 83.8% | — |
| WildBench | 85.7% | — |
| LiveBench Language | 67.8% | — |

## FAQ

### Is Gemini 2.5 Pro better than Llama 3.2 1B?

Gemini 2.5 Pro is the stronger model overall, scoring 44.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 49× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.

### Which is cheaper, Gemini 2.5 Pro 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 2.5 Pro lists at $1.25 and $10.

### Is Gemini 2.5 Pro or Llama 3.2 1B better for coding?

Gemini 2.5 Pro scores higher on coding benchmarks: 42.8 versus 20.6 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Pro does, with 1.05M tokens against 60K.

### How many benchmarks do Gemini 2.5 Pro and Llama 3.2 1B share?

19 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and Llama 3.2 1B has 22.