Model comparison
GPT-4o mini vs Llama 3.2 1B
GPT-4o mini is the stronger model overall, scoring 25.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.7× less per token, which makes it the better buy when GPT-4o mini's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
MetaSummary
- They share 21 benchmarks with published results for both. GPT-4o mini scores higher in 7 categories and Llama 3.2 1B in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GPT-4o mini leads 42.0 to 23.8.
- The biggest single-benchmark swing is BigCodeBench Complete: 57.4% for GPT-4o mini and 11.3% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.15 / $0.60 for GPT-4o mini.
- GPT-4o mini accepts more context: 128K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | Llama 3.2 1B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 25.5 | 20.1 |
| Released | 2024-07-18 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 128K | 60K |
| Max output | 16K | 54K |
| Input $ / M tokens | $0.15 | $0.027 |
| Output $ / M tokens | $0.60 | $0.20 |
| Results tracked | 60 | 22 |
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Category by category
Coding Too close to call
GPT-4o mini: 22.0 (#335), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| BigCodeBench Instruct | 46.1% | 8.2% |
| LMArena Coding | 1290 | 1070 |
| BigCodeBench Complete | 57.4% | 11.3% |
| Aider Polyglot | 3.6% | — |
| WeirdML | 11.8% | — |
| LiveBench Coding | 43.1% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use GPT-4o mini leads
GPT-4o mini: 27.5 (#101), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| BALROG | 17.4% | 6.6% |
| Berkeley Function Calling Leaderboard | — | 10.8% |
Reasoning Llama 3.2 1B leads
GPT-4o mini: 8.7 (#347), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| LMArena Hard Prompts | 1267 | 1044 |
| Epoch Capabilities Index | 126.56 | 101.99 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| DTBench | 54.4% | — |
| LiveBench Data Analysis | 50% | — |
| LMCA | 10.4% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Too close to call
GPT-4o mini: 10.4 (#314), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.9% | 0.6% |
| LMArena Math | 1267 | 1086 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge GPT-4o mini leads
GPT-4o mini: 17.7 (#284), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 37.7% | 23.9% |
| LMArena Expert | 1235 | 1007 |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), Llama 3.2 1B: —
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual GPT-4o mini leads
GPT-4o mini: 42.0 (#199), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1266 | 973 |
| LMArena Chinese | 1265 | 959 |
| LMArena German | 1272 | 1014 |
| LMArena Russian | 1275 | 941 |
| LMArena French | 1297 | — |
| LMArena Japanese | 1216 | — |
| LMArena Korean | 1195 | — |
| LMArena Spanish | 1276 | — |
Instruction Following GPT-4o mini leads
GPT-4o mini: 61.9 (#239), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1258 | 1031 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
Long Context GPT-4o mini leads
GPT-4o mini: 39.1 (#186), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1289 | 1050 |
Writing & Preference GPT-4o mini leads
GPT-4o mini: 39.5 (#248), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GPT-4o mini | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1286 | 1055 |
| LMArena Creative Writing | 1268 | 1033 |
| EQ-Bench Creative Writing | 873 | 200 |
| LMArena Multi-Turn | 1285 | 1030 |
| Short-Story Creative Writing | 67.2% | — |
| WildBench | 79.1% | — |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Llama 3.2 1B?
GPT-4o mini is the stronger model overall, scoring 25.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 3.7× less per token, which makes it the better buy when GPT-4o mini's lead doesn't matter for your workload.
Which is cheaper, GPT-4o mini 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; GPT-4o mini lists at $0.15 and $0.60.
Is GPT-4o mini or Llama 3.2 1B better for coding?
They score almost the same on coding (22.0 vs 21.1); test both on your own repository before choosing.
Which has the bigger context window?
GPT-4o mini does, with 128K tokens against 60K.
How many benchmarks do GPT-4o mini and Llama 3.2 1B share?
21 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Llama 3.2 1B has 22.