# Gemini 2.5 Flash vs Llama 3.1-70B

> Gemini 2.5 Flash is the stronger model overall, scoring 39.0 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.1× less per token, which makes it the better buy when Gemini 2.5 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-2-5-flash-vs-llama-3-1-70b
- Last updated: 2026-10-10
- Shared benchmarks: 30

## Summary

- They share 30 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 8 categories and Llama 3.1-70B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Gemini 2.5 Flash leads 38.1 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 73.1% for Gemini 2.5 Flash and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.30 / $2.50 for Gemini 2.5 Flash.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| Provider | Google | Meta |
| Noometry Index | 39.0 | 29.4 |
| Rank | 162 | 299 |
| Context | 1.05M | 128K |
| Input $/M | $0.30 | $0.40 |
| Output $/M | $2.50 | $0.40 |
| Weights | Proprietary | Open |

## Coding

- Gemini 2.5 Flash: 35.5 (#210)
- Llama 3.1-70B: 29.8 (#292)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| WeirdML | 41.9% | 9% |
| LMArena Coding | 1424 | 1260 |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 661.88 | — |

## Agentic & Tool Use

- Gemini 2.5 Flash: 30.6 (#67)
- Llama 3.1-70B: 24.9 (#104)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| TheAgentCompany | 41.1% | 6.9% |
| BALROG | 33.5% | 27.9% |
| Terminal-Bench | 17.1% | — |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| Vending-Bench 2 | 548.84 | — |

## Reasoning

- Gemini 2.5 Flash: 18.2 (#271)
- Llama 3.1-70B: 20.8 (#215)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1241 |
| DTBench | 76.5% | 60% |
| LMCA | 27.5% | 14.8% |
| Epoch Capabilities Index | 143.04 | 125.91 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| CritPt | 1.1% | — |
| EnigmaEval | 2.7% | — |
| ForecastBench | 60.6 | — |

## Math

- Gemini 2.5 Flash: 38.1 (#87)
- Llama 3.1-70B: 12.7 (#298)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.1% | 3.6% |
| Omni-MATH | 38.5% | 21% |
| LMArena Math | 1415 | 1252 |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Gemini 2.5 Flash: 36.1 (#162)
- Llama 3.1-70B: 24.1 (#264)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| MMLU-Pro | 63.9% | 65.3% |
| GPQA (HELM) | 39% | 42.6% |
| LMArena Expert | 1426 | 1209 |
| GPQA Diamond | — | 44.2% |
| Humanity's Last Exam | 12.1% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| MMLU | — | 80.1% |

## Multimodal

- Gemini 2.5 Flash: 41.2 (#31)
- Llama 3.1-70B: —

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1254 | — |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| SpatialViz-Bench | 36.9% | — |

## Multilingual

- Gemini 2.5 Flash: 52.5 (#83)
- Llama 3.1-70B: 38.9 (#220)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1410 | 1219 |
| LMArena Chinese | 1450 | 1215 |
| LMArena French | 1433 | 1260 |
| LMArena German | 1419 | 1222 |
| LMArena Japanese | 1403 | 1132 |
| LMArena Korean | 1385 | 1140 |
| LMArena Russian | 1415 | 1234 |
| LMArena Spanish | 1421 | 1253 |

## Instruction Following

- Gemini 2.5 Flash: 75.9 (#50)
- Llama 3.1-70B: 65.5 (#218)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| IFEval | 89.8% | 82.1% |
| LMArena Instruction Following | 1405 | 1231 |

## Long Context

- Gemini 2.5 Flash: 47.5 (#16)
- Llama 3.1-70B: 37.6 (#208)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1419 | 1241 |
| Fiction.LiveBench | 77.8% | — |

## Writing & Preference

- Gemini 2.5 Flash: 54.0 (#153)
- Llama 3.1-70B: 35.6 (#262)

| Benchmark | Gemini 2.5 Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1417 | 1261 |
| LMArena Creative Writing | 1400 | 1232 |
| EQ-Bench Creative Writing | 1137 | 784 |
| WildBench | 81.7% | 75.8% |
| LMArena Multi-Turn | 1408 | 1256 |
| Short-Story Creative Writing | 76.5% | — |

## FAQ

### Is Gemini 2.5 Flash better than Llama 3.1-70B?

Gemini 2.5 Flash is the stronger model overall, scoring 39.0 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.1× less per token, which makes it the better buy when Gemini 2.5 Flash's lead doesn't matter for your workload.

### Which is cheaper, Gemini 2.5 Flash or Llama 3.1-70B?

Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Gemini 2.5 Flash lists at $0.30 and $2.50.

### Is Gemini 2.5 Flash or Llama 3.1-70B better for coding?

Gemini 2.5 Flash scores higher on coding benchmarks: 35.5 versus 29.8 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do Gemini 2.5 Flash and Llama 3.1-70B share?

30 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and Llama 3.1-70B has 35.