Model comparison
GPT-3.5-turbo vs Llama 3.2 1B
GPT-3.5-turbo is the stronger model overall, scoring 23.2 to 20.1 on the Noometry Index. Llama 3.2 1B costs 11× less per token, which makes it the better buy when GPT-3.5-turbo's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
MetaSummary
- They share 20 benchmarks with published results for both. GPT-3.5-turbo scores higher in 6 categories and Llama 3.2 1B in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GPT-3.5-turbo leads 31.5 to 23.8.
- The biggest single-benchmark swing is BigCodeBench Complete: 50.6% for GPT-3.5-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 $0.50 / $1.50 for GPT-3.5-turbo.
- Llama 3.2 1B accepts more context: 60K tokens versus 16K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| GPT-3.5-turbo | Llama 3.2 1B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 23.2 | 20.1 |
| Released | 2023-03-01 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 16K | 60K |
| Max output | 4K | 54K |
| Input $ / M tokens | $0.50 | $0.027 |
| Output $ / M tokens | $1.50 | $0.20 |
| Results tracked | 44 | 22 |
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Category by category
Coding GPT-3.5-turbo leads
GPT-3.5-turbo: 23.9 (#331), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| BigCodeBench Instruct | 39.1% | 8.2% |
| LMArena Coding | 1136 | 1070 |
| BigCodeBench Complete | 50.6% | 11.3% |
| WeirdML | 3.5% | — |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, Llama 3.2 1B: 14.6 (#150)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| METR Time Horizons | 21.5% | — |
Reasoning Llama 3.2 1B leads
GPT-3.5-turbo: 13.8 (#332), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| LMArena Hard Prompts | 1108 | 1044 |
| Epoch Capabilities Index | 118.55 | 101.99 |
| Mystery Game Puzzles | 3% | — |
| DTBench | 48.5% | — |
| LMCA | 9.7% | — |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |
Math Llama 3.2 1B leads
GPT-3.5-turbo: 6.3 (#327), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 2.2% | 0.6% |
| LMArena Math | 1142 | 1086 |
| FrontierMath (Tiers 1-3) | 0% | — |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |
Knowledge GPT-3.5-turbo leads
GPT-3.5-turbo: 10.0 (#303), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 28% | 23.9% |
| LMArena Expert | 1070 | 1007 |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multilingual GPT-3.5-turbo leads
GPT-3.5-turbo: 31.5 (#258), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1108 | 973 |
| LMArena Chinese | 1075 | 959 |
| LMArena German | 1090 | 1014 |
| LMArena Russian | 1123 | 941 |
| LMArena French | 1118 | — |
| LMArena Japanese | 1043 | — |
| LMArena Korean | 1019 | — |
| LMArena Spanish | 1121 | — |
Instruction Following GPT-3.5-turbo leads
GPT-3.5-turbo: 57.9 (#262), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1119 | 1031 |
Long Context GPT-3.5-turbo leads
GPT-3.5-turbo: 34.0 (#254), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1121 | 1050 |
Writing & Preference GPT-3.5-turbo leads
GPT-3.5-turbo: 25.3 (#305), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GPT-3.5-turbo | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1125 | 1055 |
| LMArena Creative Writing | 1092 | 1033 |
| EQ-Bench Creative Writing | 451 | 200 |
| LMArena Multi-Turn | 1117 | 1030 |
Frequently asked questions
Is GPT-3.5-turbo better than Llama 3.2 1B?
GPT-3.5-turbo is the stronger model overall, scoring 23.2 to 20.1 on the Noometry Index. Llama 3.2 1B costs 11× less per token, which makes it the better buy when GPT-3.5-turbo's lead doesn't matter for your workload.
Which is cheaper, GPT-3.5-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-3.5-turbo lists at $0.50 and $1.50.
Is GPT-3.5-turbo or Llama 3.2 1B better for coding?
GPT-3.5-turbo scores higher on coding benchmarks: 23.9 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Llama 3.2 1B does, with 60K tokens against 16K.
How many benchmarks do GPT-3.5-turbo and Llama 3.2 1B share?
20 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and Llama 3.2 1B has 22.