Model comparison
GPT-4 Turbo vs Llama 3.2 1B
GPT-4 Turbo is the stronger model overall, scoring 30.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 213× less per token, which makes it the better buy when GPT-4 Turbo's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. GPT-4 Turbo scores higher in 6 categories and Llama 3.2 1B in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4 Turbo leads 47.7 to 21.3.
- The biggest single-benchmark swing is BigCodeBench Complete: 58.2% for GPT-4 Turbo 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 $10 / $30 for GPT-4 Turbo.
- GPT-4 Turbo accepts more context: 128K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| GPT-4 Turbo | Llama 3.2 1B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 30.5 | 20.1 |
| Released | 2023-11-06 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 128K | 60K |
| Max output | 4K | 54K |
| Input $ / M tokens | $10 | $0.027 |
| Output $ / M tokens | $30 | $0.20 |
| Results tracked | 36 | 22 |
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Category by category
Coding GPT-4 Turbo leads
GPT-4 Turbo: 33.8 (#249), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| BigCodeBench Instruct | 48.2% | 8.2% |
| LMArena Coding | 1268 | 1070 |
| BigCodeBench Complete | 58.2% | 11.3% |
| WeirdML | 18% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Llama 3.2 1B: 14.6 (#150)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| METR Time Horizons | 36.7% | — |
Reasoning Too close to call
GPT-4 Turbo: 15.3 (#317), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 6% | 0% |
| LMArena Hard Prompts | 1251 | 1044 |
| Epoch Capabilities Index | 127.25 | 101.99 |
| SimpleBench | 25.1% | — |
| DTBench | 61.6% | — |
| LMCA | 9.8% | — |
| ForecastBench | 59.4 | — |
Math Llama 3.2 1B leads
GPT-4 Turbo: 9.0 (#322), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.7% | 0.6% |
| LMArena Math | 1272 | 1086 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| MATH Level 5 | 46.7% | — |
Knowledge GPT-4 Turbo leads
GPT-4 Turbo: 24.3 (#268), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 46.6% | 23.9% |
| LMArena Expert | 1223 | 1007 |
| Confabulations | 28.4% | — |
| MMLU | 81.3% | — |
Multimodal Not comparable
GPT-4 Turbo: 30.6 (#110), Llama 3.2 1B: —
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1090 | — |
Multilingual GPT-4 Turbo leads
GPT-4 Turbo: 40.5 (#216), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1245 | 973 |
| LMArena Chinese | 1242 | 959 |
| LMArena German | 1259 | 1014 |
| LMArena Russian | 1259 | 941 |
| 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 1B: 52.4 (#290)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1249 | 1031 |
Long Context GPT-4 Turbo leads
GPT-4 Turbo: 38.0 (#206), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1254 | 1050 |
Writing & Preference GPT-4 Turbo leads
GPT-4 Turbo: 47.7 (#206), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GPT-4 Turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1272 | 1055 |
| LMArena Creative Writing | 1269 | 1033 |
| LMArena Multi-Turn | 1267 | 1030 |
| EQ-Bench Creative Writing | — | 200 |
Frequently asked questions
Is GPT-4 Turbo better than Llama 3.2 1B?
GPT-4 Turbo is the stronger model overall, scoring 30.5 to 20.1 on the Noometry Index. Llama 3.2 1B costs 213× 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 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is GPT-4 Turbo or Llama 3.2 1B better for coding?
GPT-4 Turbo scores higher on coding benchmarks: 33.8 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4 Turbo does, with 128K tokens against 60K.
How many benchmarks do GPT-4 Turbo and Llama 3.2 1B share?
19 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Llama 3.2 1B has 22.