Model comparison
GPT-4 Turbo vs Llama 3.2 3B
GPT-4 Turbo is the stronger model overall, scoring 30.5 to 28.9 on the Noometry Index. Llama 3.2 3B costs 125× less per token, which makes it the better buy when GPT-4 Turbo's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
MetaSummary
- They share 15 benchmarks with published results for both. GPT-4 Turbo scores higher in 5 categories and Llama 3.2 3B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Llama 3.2 3B leads 32.4 to 9.0.
- The biggest single-benchmark swing is BigCodeBench Complete: 58.2% for GPT-4 Turbo 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 $10 / $30 for GPT-4 Turbo.
- 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-4 Turbo | Llama 3.2 3B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 30.5 | 28.9 |
| Released | 2023-11-06 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 4K | 118K |
| Input $ / M tokens | $10 | $0.05 |
| Output $ / M tokens | $30 | $0.33 |
| Results tracked | 36 | 18 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-4 Turbo leads
GPT-4 Turbo: 33.8 (#249), Llama 3.2 3B: 27.6 (#319)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| BigCodeBench Instruct | 48.2% | 23.4% |
| LMArena Coding | 1268 | 1098 |
| BigCodeBench Complete | 58.2% | 28.3% |
| WeirdML | 18% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Llama 3.2 3B: 20.1 (#143)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |
| METR Time Horizons | 36.7% | — |
Reasoning Llama 3.2 3B leads
GPT-4 Turbo: 15.3 (#317), Llama 3.2 3B: 21.0 (#228)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1251 | 1095 |
| SimpleBench | 25.1% | — |
| Chess Puzzles | 6% | — |
| DTBench | 61.6% | — |
| LMCA | 9.8% | — |
| Epoch Capabilities Index | 127.25 | — |
| ForecastBench | 59.4 | — |
Math Llama 3.2 3B leads
GPT-4 Turbo: 9.0 (#322), Llama 3.2 3B: 32.4 (#214)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1272 | 1126 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.7% | — |
| MATH Level 5 | 46.7% | — |
Knowledge Llama 3.2 3B leads
GPT-4 Turbo: 24.3 (#268), Llama 3.2 3B: 29.7 (#235)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1223 | 1090 |
| GPQA Diamond | 46.6% | — |
| Confabulations | 28.4% | — |
| MMLU | 81.3% | — |
Multimodal Not comparable
GPT-4 Turbo: 30.6 (#110), Llama 3.2 3B: —
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1090 | — |
Multilingual GPT-4 Turbo leads
GPT-4 Turbo: 40.5 (#216), Llama 3.2 3B: 26.2 (#281)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1245 | 1019 |
| LMArena Chinese | 1242 | 1017 |
| LMArena German | 1259 | 1056 |
| LMArena Russian | 1259 | 949 |
| LMArena French | 1276 | — |
| LMArena Japanese | 1194 | — |
| LMArena Korean | 1187 | — |
| LMArena Spanish | 1260 | — |
Instruction Following GPT-4 Turbo leads
GPT-4 Turbo: 65.8 (#216), Llama 3.2 3B: 56.0 (#275)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1249 | 1089 |
Long Context GPT-4 Turbo leads
GPT-4 Turbo: 38.0 (#206), Llama 3.2 3B: 33.4 (#261)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1254 | 1100 |
Writing & Preference GPT-4 Turbo leads
GPT-4 Turbo: 47.7 (#206), Llama 3.2 3B: 24.7 (#307)
| Benchmark | GPT-4 Turbo | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1272 | 1110 |
| LMArena Creative Writing | 1269 | 1094 |
| LMArena Multi-Turn | 1267 | 1105 |
| EQ-Bench Creative Writing | — | 595 |
Frequently asked questions
Is GPT-4 Turbo better than Llama 3.2 3B?
GPT-4 Turbo is the stronger model overall, scoring 30.5 to 28.9 on the Noometry Index. Llama 3.2 3B costs 125× less per token, which makes it the better buy when GPT-4 Turbo's lead doesn't matter for your workload.
Which is cheaper, GPT-4 Turbo 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-4 Turbo lists at $10 and $30.
Is GPT-4 Turbo or Llama 3.2 3B better for coding?
GPT-4 Turbo scores higher on coding benchmarks: 33.8 versus 27.6 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-4 Turbo and Llama 3.2 3B share?
15 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Llama 3.2 3B has 18.