# GPT-4o mini vs Llama 3.2 3B

> Llama 3.2 3B is the stronger model overall, scoring 28.4 to 25.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-4o-mini-vs-llama-3-2-3b
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. GPT-4o mini scores higher in 5 categories and Llama 3.2 3B in 4 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Llama 3.2 3B leads 29.9 to 9.9.
- The biggest single-benchmark swing is BigCodeBench Complete: 57.4% for GPT-4o mini and 28.3% for Llama 3.2 3B.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.15 / $0.60 for GPT-4o mini.
- Llama 3.2 3B accepts more context: 131K tokens versus 128K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 25.4 | 28.4 |
| Rank | 337 | 318 |
| Context | 128K | 131K |
| Input $/M | $0.15 | $0.05 |
| Output $/M | $0.60 | $0.33 |
| Weights | Proprietary | Open |

## Coding

- GPT-4o mini: 21.6 (#330)
- Llama 3.2 3B: 27.1 (#314)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| BigCodeBench Instruct | 46.1% | 23.4% |
| LMArena Coding | 1290 | 1098 |
| BigCodeBench Complete | 57.4% | 28.3% |
| Aider Polyglot | 3.6% | — |
| WeirdML | 11.8% | — |
| LiveBench Coding | 43.1% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |

## Agentic & Tool Use

- GPT-4o mini: 27.2 (#95)
- Llama 3.2 3B: 19.9 (#135)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| BALROG | 17.4% | 10.1% |
| Berkeley Function Calling Leaderboard | — | 21.9% |

## Reasoning

- GPT-4o mini: 8.7 (#343)
- Llama 3.2 3B: 20.1 (#225)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1267 | 1095 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| DTBench | 54.4% | — |
| LiveBench Data Analysis | 50% | — |
| LMCA | 10.4% | — |
| Epoch Capabilities Index | 126.56 | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |

## Math

- GPT-4o mini: 9.9 (#307)
- Llama 3.2 3B: 29.9 (#213)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1267 | 1126 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.9% | — |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |

## Knowledge

- GPT-4o mini: 17.7 (#279)
- Llama 3.2 3B: 29.1 (#232)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1235 | 1090 |
| GPQA Diamond | 37.7% | — |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |

## Multimodal

- GPT-4o mini: 25.3 (#117)
- Llama 3.2 3B: —

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |

## Multilingual

- GPT-4o mini: 42.2 (#194)
- Llama 3.2 3B: 26.4 (#276)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1266 | 1019 |
| LMArena Chinese | 1264 | 1017 |
| LMArena German | 1272 | 1056 |
| LMArena Russian | 1275 | 949 |
| LMArena French | 1296 | — |
| LMArena Japanese | 1216 | — |
| LMArena Korean | 1195 | — |
| LMArena Spanish | 1276 | — |

## Instruction Following

- GPT-4o mini: 62.1 (#234)
- Llama 3.2 3B: 56.3 (#270)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1259 | 1089 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |

## Long Context

- GPT-4o mini: 39.1 (#181)
- Llama 3.2 3B: 33.4 (#256)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1289 | 1101 |

## Writing & Preference

- GPT-4o mini: 39.7 (#243)
- Llama 3.2 3B: 24.9 (#302)

| Benchmark | GPT-4o mini | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1286 | 1110 |
| LMArena Creative Writing | 1268 | 1094 |
| EQ-Bench Creative Writing | 873 | 595 |
| LMArena Multi-Turn | 1285 | 1105 |
| Short-Story Creative Writing | 67.2% | — |
| WildBench | 79.1% | — |
| LiveBench Language | 28.6% | — |

## FAQ

### Is GPT-4o mini better than Llama 3.2 3B?

Llama 3.2 3B is the stronger model overall, scoring 28.4 to 25.4 on the Noometry Index.

### Which is cheaper, GPT-4o mini or Llama 3.2 3B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; GPT-4o mini lists at $0.15 and $0.60.

### Is GPT-4o mini or Llama 3.2 3B better for coding?

Llama 3.2 3B scores higher on coding benchmarks: 27.1 versus 21.6 in the Noometry coding category.

### Which has the bigger context window?

Llama 3.2 3B does, with 131K tokens against 128K.

### How many benchmarks do GPT-4o mini and Llama 3.2 3B share?

17 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Llama 3.2 3B has 18.