Model comparison
GPT-4o vs Llama 3.2 3B
GPT-4o and Llama 3.2 3B score almost the same on the Noometry Index (28.6 vs 28.9), so choose on price, context window or the category you care about most.
Last verified . 16 shared benchmarks.
MetaSummary
- They share 16 benchmarks with published results for both. GPT-4o scores higher in 5 categories and Llama 3.2 3B in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4o leads 52.6 to 24.7.
- The biggest single-benchmark swing is BigCodeBench Complete: 61.1% for GPT-4o 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 $2.50 / $10 for GPT-4o.
- Llama 3.2 3B accepts more context: 131K tokens versus 128K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Llama 3.2 3B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 28.6 | 28.9 |
| Released | 2024-05-13 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 16K | 118K |
| Input $ / M tokens | $2.50 | $0.05 |
| Output $ / M tokens | $10 | $0.33 |
| Results tracked | 72 | 18 |
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Category by category
Coding Llama 3.2 3B leads
GPT-4o: 24.8 (#328), Llama 3.2 3B: 27.6 (#319)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| BigCodeBench Instruct | 51.1% | 23.4% |
| LMArena Coding | 1297 | 1098 |
| BigCodeBench Complete | 61.1% | 28.3% |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| LiveBench Coding | 51.4% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Too close to call
GPT-4o: 21.0 (#141), Llama 3.2 3B: 20.1 (#143)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| BALROG | 32.3% | 10.1% |
| Berkeley Function Calling Leaderboard | — | 21.9% |
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Llama 3.2 3B leads
GPT-4o: 9.4 (#343), Llama 3.2 3B: 21.0 (#228)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1281 | 1095 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| DTBench | 64.5% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 16.6% | — |
| Epoch Capabilities Index | 128.97 | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Llama 3.2 3B leads
GPT-4o: 10.6 (#312), Llama 3.2 3B: 32.4 (#214)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1285 | 1126 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Too close to call
GPT-4o: 28.8 (#242), Llama 3.2 3B: 29.7 (#235)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1250 | 1090 |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Llama 3.2 3B: —
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual GPT-4o leads
GPT-4o: 43.2 (#186), Llama 3.2 3B: 26.2 (#281)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1283 | 1019 |
| LMArena Chinese | 1277 | 1017 |
| LMArena German | 1282 | 1056 |
| LMArena Russian | 1286 | 949 |
| LMArena French | 1304 | — |
| LMArena Japanese | 1257 | — |
| LMArena Korean | 1234 | — |
| LMArena Spanish | 1292 | — |
Instruction Following GPT-4o leads
GPT-4o: 66.6 (#207), Llama 3.2 3B: 56.0 (#275)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1089 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context GPT-4o leads
GPT-4o: 39.4 (#179), Llama 3.2 3B: 33.4 (#261)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1289 | 1100 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference GPT-4o leads
GPT-4o: 52.6 (#166), Llama 3.2 3B: 24.7 (#307)
| Benchmark | GPT-4o | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1300 | 1110 |
| LMArena Creative Writing | 1292 | 1094 |
| LMArena Multi-Turn | 1302 | 1105 |
| Short-Story Creative Writing | 81.8% | — |
| EQ-Bench Creative Writing | — | 595 |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Llama 3.2 3B?
GPT-4o and Llama 3.2 3B score almost the same on the Noometry Index (28.6 vs 28.9), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-4o 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 lists at $2.50 and $10.
Is GPT-4o or Llama 3.2 3B better for coding?
Llama 3.2 3B scores higher on coding benchmarks: 27.6 versus 24.8 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 and Llama 3.2 3B share?
16 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Llama 3.2 3B has 18.