# GPT-5.1 vs Llama 3.2 1B

> GPT-5.1 is the stronger model overall, scoring 48.8 to 19.9 on the Noometry Index. Llama 3.2 1B costs 49× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-1-vs-llama-3-2-1b
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. GPT-5.1 scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5.1 leads 64.7 to 21.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.6% for GPT-5.1 and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 48.8 | 19.9 |
| Rank | 47 | 349 |
| Context | 400K | 60K |
| Input $/M | $1.25 | $0.027 |
| Output $/M | $10 | $0.20 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.1: 46.9 (#59)
- Llama 3.2 1B: 20.6 (#333)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1454 | 1071 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1395 | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| BigCodeBench Instruct | — | 8.2% |
| LiveBench Coding | 72.5% | — |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,192 | — |

## Agentic & Tool Use

- GPT-5.1: 32.5 (#55)
- Llama 3.2 1B: 14.5 (#142)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| DeepResearch Bench | 42.8% | — |
| BALROG | — | 6.6% |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |

## Reasoning

- GPT-5.1: 39.9 (#50)
- Llama 3.2 1B: 15.6 (#304)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 32% | 0% |
| LMArena Hard Prompts | 1457 | 1044 |
| Epoch Capabilities Index | 149.66 | 101.95 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 90.1% | — |
| LiveBench Data Analysis | 72.1% | — |
| LMCA | 43.9% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |

## Math

- GPT-5.1: 50.3 (#45)
- Llama 3.2 1B: 9.7 (#308)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 0.6% |
| LMArena Math | 1446 | 1086 |
| Omni-MATH | 46.4% | — |
| LiveBench Math | 94.5% | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |

## Knowledge

- GPT-5.1: 50.5 (#65)
- Llama 3.2 1B: 7.2 (#307)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 87.6% | 23.9% |
| LMArena Expert | 1470 | 1007 |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| MMLU-Pro | 57.9% | — |
| Vectara Hallucination Rate | 10.9% | — |
| GPQA (HELM) | 44.2% | — |

## Multimodal

- GPT-5.1: 44.0 (#17)
- Llama 3.2 1B: —

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |

## Multilingual

- GPT-5.1: 54.0 (#51)
- Llama 3.2 1B: 23.9 (#287)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1431 | 973 |
| LMArena Chinese | 1495 | 958 |
| LMArena German | 1439 | 1014 |
| LMArena Russian | 1435 | 941 |
| LMArena French | 1451 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1430 | — |

## Instruction Following

- GPT-5.1: 84.0 (#1)
- Llama 3.2 1B: 52.6 (#285)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1442 | 1031 |
| LiveBench Instruction Following | 93.3% | — |
| IFEval | 93.5% | — |

## Long Context

- GPT-5.1: 47.6 (#14)
- Llama 3.2 1B: 31.9 (#269)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1448 | 1050 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |

## Writing & Preference

- GPT-5.1: 64.7 (#53)
- Llama 3.2 1B: 21.4 (#305)

| Benchmark | GPT-5.1 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1443 | 1055 |
| LMArena Creative Writing | 1427 | 1033 |
| LMArena Multi-Turn | 1449 | 1030 |
| EQ-Bench Creative Writing | — | 200 |
| WildBench | 86.3% | — |
| LiveBench Language | 80.2% | — |

## FAQ

### Is GPT-5.1 better than Llama 3.2 1B?

GPT-5.1 is the stronger model overall, scoring 48.8 to 19.9 on the Noometry Index. Llama 3.2 1B costs 49× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.1 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-5.1 lists at $1.25 and $10.

### Is GPT-5.1 or Llama 3.2 1B better for coding?

GPT-5.1 scores higher on coding benchmarks: 46.9 versus 20.6 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.1 does, with 400K tokens against 60K.

### How many benchmarks do GPT-5.1 and Llama 3.2 1B share?

17 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Llama 3.2 1B has 22.