# Gemini 2.5 Pro vs Llama 3.1-8B

> Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 60× 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-3-1-8b
- Last updated: 2026-10-12
- Shared benchmarks: 34

## Summary

- They share 34 benchmarks with published results for both. Gemini 2.5 Pro 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 2.5 Pro leads 56.0 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 84.7% for Gemini 2.5 Pro 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 $1.25 / $10 for Gemini 2.5 Pro.
- Gemini 2.5 Pro accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 45.0 | 23.0 |
| Rank | 75 | 352 |
| Context | 1.05M | 128K |
| Input $/M | $1.25 | $0.05 |
| Output $/M | $10 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- Gemini 2.5 Pro: 42.4 (#101)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| SciCode | 42.8% | 13.2% |
| WeirdML | 54% | 1.7% |
| LMArena Coding | 1452 | 1195 |
| SWE-bench Verified | 57.6% | — |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| LMArena WebDev | 1227 | — |
| GSO | 3.9% | — |
| BigCodeBench Instruct | — | 32.8% |
| LiveBench Coding | 85.9% | — |
| BigCodeBench Complete | — | 40.5% |
| CadEval | 64% | — |
| ALE-Bench | 785.52 | — |
| AlgoTune | 1.51 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Gemini 2.5 Pro: 29.2 (#88)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| BALROG | 43.3% | 15.1% |
| Terminal-Bench | 32.6% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| 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 (#99)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| CritPt | 2% | 0% |
| Chess Puzzles | 20% | 0% |
| LMArena Hard Prompts | 1455 | 1175 |
| DTBench | 82.4% | 50.9% |
| LMCA | 34.8% | 5.4% |
| Epoch Capabilities Index | 145.32 | 116.57 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 41% | — |
| EnigmaEval | 5.6% | — |
| LiveBench Reasoning | 89.8% | — |
| LiveBench Data Analysis | 79.9% | — |
| ForecastBench | 61.3 | — |
| LiveBench | 82.3% | — |
| PIQA | — | 81.2% |

## Math

- Gemini 2.5 Pro: 32.5 (#213)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.7% | 1.7% |
| Omni-MATH | 41.6% | 13.7% |
| LMArena Math | 1450 | 1179 |
| MATH Level 5 | 95.9% | 22.9% |
| FrontierMath (Tiers 1-3) | 24.6% | — |
| FrontierMath Tier 4 | 0% | — |
| LiveBench Math | 90.2% | — |
| FrontierMath (Feb 2025 set) | 14.1% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
| GSM8K | — | 82.4% |

## Knowledge

- Gemini 2.5 Pro: 56.0 (#46)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 85.3% | 27% |
| MMLU-Pro | 86.3% | 40.6% |
| GPQA (HELM) | 74.9% | 24.7% |
| LMArena Expert | 1452 | 1144 |
| Humanity's Last Exam | 21.6% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Gemini 2.5 Pro: 45.2 (#18)
- Llama 3.1-8B: —

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |

## Multilingual

- Gemini 2.5 Pro: 55.3 (#31)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1451 | 1148 |
| LMArena Chinese | 1507 | 1151 |
| LMArena French | 1472 | 1177 |
| LMArena German | 1487 | 1144 |
| LMArena Japanese | 1461 | 1061 |
| LMArena Korean | 1434 | 1053 |
| LMArena Russian | 1461 | 1158 |
| LMArena Spanish | 1473 | 1169 |

## Instruction Following

- Gemini 2.5 Pro: 75.0 (#75)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| IFEval | 84% | 74.3% |
| LMArena Instruction Following | 1437 | 1159 |
| LiveBench Instruction Following | 80.6% | — |

## Long Context

- Gemini 2.5 Pro: 59.8 (#5)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1449 | 1182 |
| Fiction.LiveBench | 91.7% | — |

## Writing & Preference

- Gemini 2.5 Pro: 63.7 (#62)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | Gemini 2.5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1458 | 1187 |
| LMArena Creative Writing | 1454 | 1154 |
| EQ-Bench Creative Writing | 1421 | 713 |
| WildBench | 85.7% | 68.7% |
| LMArena Multi-Turn | 1453 | 1172 |
| Short-Story Creative Writing | 83.8% | — |
| LiveBench Language | 67.8% | — |

## FAQ

### Is Gemini 2.5 Pro better than Llama 3.1-8B?

Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 60× 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 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.

### Is Gemini 2.5 Pro or Llama 3.1-8B better for coding?

Gemini 2.5 Pro scores higher on coding benchmarks: 42.4 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Pro does, with 1.05M tokens against 128K.

### How many benchmarks do Gemini 2.5 Pro and Llama 3.1-8B share?

34 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and Llama 3.1-8B has 43.