Model comparison
GPT-5.6 Terra vs Llama 3.2 1B
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 20.1 on the Noometry Index. Llama 3.2 1B costs 64× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. GPT-5.6 Terra 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-5.6 Terra leads 81.6 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.7% for GPT-5.6 Terra 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 $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra 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-5.6 Terra | Llama 3.2 1B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 59.2 | 20.1 |
| Released | 2026-07-09 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 60K |
| Max output | 128K | 54K |
| Input $ / M tokens | $2 | $0.027 |
| Output $ / M tokens | $12 | $0.20 |
| Results tracked | 52 | 22 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.7 (#19), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1484 | 1070 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1522 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use GPT-5.6 Terra leads
GPT-5.6 Terra: 40.1 (#25), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| BALROG | 53.2% | 6.6% |
| APEX-Agents | 58.2% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.7 (#21), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 54% | 0% |
| LMArena Hard Prompts | 1468 | 1044 |
| Epoch Capabilities Index | 159.62 | 101.99 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| NYT Connections (extended) | 78.4% | — |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30% | — |
| Mystery Game Puzzles | 35% | — |
| DTBench | 93.3% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.6 (#12), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.7% | 0.6% |
| LMArena Math | 1466 | 1086 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.2 (#30), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 93.3% | 23.9% |
| LMArena Expert | 1492 | 1007 |
| SimpleQA Verified | 43.2% | — |
Multimodal Not comparable
GPT-5.6 Terra: 47.3 (#11), Llama 3.2 1B: —
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.4 (#44), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1439 | 973 |
| LMArena Chinese | 1513 | 959 |
| LMArena German | 1460 | 1014 |
| LMArena Russian | 1450 | 941 |
| LMArena French | 1471 | — |
| LMArena Japanese | 1457 | — |
| LMArena Korean | 1425 | — |
| LMArena Spanish | 1448 | — |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.4 (#40), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1454 | 1031 |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#68), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1451 | 1050 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.2 (#23), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GPT-5.6 Terra | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1447 | 1055 |
| LMArena Creative Writing | 1410 | 1033 |
| EQ-Bench Creative Writing | 1855 | 200 |
| LMArena Multi-Turn | 1449 | 1030 |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Llama 3.2 1B?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 20.1 on the Noometry Index. Llama 3.2 1B costs 64× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Terra 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-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Llama 3.2 1B better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 60K.
How many benchmarks do GPT-5.6 Terra and Llama 3.2 1B share?
19 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Llama 3.2 1B has 22.