Model comparison
GPT-5.5 vs Llama 3.2 1B
GPT-5.5 is the stronger model overall, scoring 63.4 to 20.1 on the Noometry Index. Llama 3.2 1B costs 160× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. GPT-5.5 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 math, where GPT-5.5 leads 81.7 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.5 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 $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 | Llama 3.2 1B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 63.4 | 20.1 |
| Released | 2026-04-23 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 60K |
| Max output | 128K | 54K |
| Input $ / M tokens | $5 | $0.027 |
| Output $ / M tokens | $30 | $0.20 |
| Results tracked | 71 | 22 |
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Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.2 (#17), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1494 | 1070 |
| SWE-bench Verified | 80.6% | — |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| LMArena WebDev | 1513 | — |
| SciCode | 56.1% | — |
| GSO | 40.2% | — |
| WeirdML | 84.9% | — |
| BigCodeBench Instruct | — | 8.2% |
| MirrorCode | 10% | — |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,943 | — |
Agentic & Tool Use GPT-5.5 leads
GPT-5.5: 50.7 (#6), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| Terminal-Bench | 84.7% | — |
| APEX-Agents | 55.1% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| OSWorld 2.0 | 13% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| BALROG | — | 6.6% |
| ExploitBench | 47.4% | — |
| GBAEval | 53.2% | — |
| GDP.pdf | 26% | — |
| LMArena Search | 1242 | — |
| Vending-Bench 2 | 7,524 | — |
Reasoning GPT-5.5 leads
GPT-5.5: 72.8 (#11), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 54% | 0% |
| LMArena Hard Prompts | 1489 | 1044 |
| Epoch Capabilities Index | 159.1 | 101.99 |
| ARC-AGI-2 | 85% | — |
| SimpleBench | 69% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 96.2% | — |
| ARC-AGI-1 | 95% | — |
| CritPt | 27.1% | — |
| EBR-Bench | 34.3% | — |
| Mystery Game Puzzles | 56% | — |
| DTBench | 96% | — |
| LMCA | 54.3% | — |
| Surface Evolver Bench | 88.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 60.6 | — |
Math GPT-5.5 leads
GPT-5.5: 81.7 (#11), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 0.6% |
| LMArena Math | 1486 | 1086 |
| FrontierMath (Tiers 1-3) | 85.3% | — |
| FrontierMath Tier 4 | 72.5% | — |
| MathArena Final-Answer Competitions | 94.3% | — |
| ProofBench | 50% | — |
| FrontierMath (Feb 2025 set) | 51.7% | — |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#17), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 94% | 23.9% |
| LMArena Expert | 1508 | 1007 |
| SimpleQA Verified | 63% | — |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GPT-5.5: 46.9 (#12), Llama 3.2 1B: —
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
| LMArena Document | 1486 | — |
Multilingual GPT-5.5 leads
GPT-5.5: 56.4 (#20), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1467 | 973 |
| LMArena Chinese | 1533 | 959 |
| LMArena German | 1480 | 1014 |
| LMArena Russian | 1473 | 941 |
| LMArena French | 1486 | — |
| LMArena Japanese | 1498 | — |
| LMArena Korean | 1460 | — |
| LMArena Spanish | 1468 | — |
Instruction Following GPT-5.5 leads
GPT-5.5: 77.5 (#18), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1479 | 1031 |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1484 | 1050 |
| CL-bench Life | 22.2% | — |
Writing & Preference GPT-5.5 leads
GPT-5.5: 72.7 (#13), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GPT-5.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1472 | 1055 |
| LMArena Creative Writing | 1455 | 1033 |
| EQ-Bench Creative Writing | 1844 | 200 |
| LMArena Multi-Turn | 1476 | 1030 |
| EQ-Bench 4 | 1315 | — |
Frequently asked questions
Is GPT-5.5 better than Llama 3.2 1B?
GPT-5.5 is the stronger model overall, scoring 63.4 to 20.1 on the Noometry Index. Llama 3.2 1B costs 160× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 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-5.5 lists at $5 and $30.
Is GPT-5.5 or Llama 3.2 1B better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.2 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 60K.
How many benchmarks do GPT-5.5 and Llama 3.2 1B share?
18 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Llama 3.2 1B has 22.