# GPT-5.6 Sol vs Llama 2-70B

> GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 24.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-5-6-sol-vs-llama-2-70b
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GPT-5.6 Sol scores higher in 8 categories and Llama 2-70B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 7.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.6 Sol and 0% for Llama 2-70B.
- Llama 2-70B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 64.4 | 24.1 |
| Rank | 6 | 343 |
| Context | 1.05M | — |
| Input $/M | $4 | — |
| Output $/M | $20 | — |
| Weights | Proprietary | Open |

## Coding

- GPT-5.6 Sol: 61.1 (#7)
- Llama 2-70B: 30.8 (#281)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Coding | 1493 | 1079 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |

## Agentic & Tool Use

- GPT-5.6 Sol: 50.2 (#7)
- Llama 2-70B: —

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |

## Reasoning

- GPT-5.6 Sol: 74.7 (#6)
- Llama 2-70B: 13.7 (#324)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Hard Prompts | 1481 | 1073 |
| DTBench | 96% | 41.6% |
| Epoch Capabilities Index | 161.99 | 113.59 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| BIG-Bench Hard | — | 64.9% |
| CommonsenseQA 2.0 | — | 50% |
| ForecastBench | — | 51.4 |
| HellaSwag | — | 85.3% |
| LAMBADA | — | 78.9% |
| PIQA | — | 82.8% |
| WinoGrande | — | 80.2% |

## Math

- GPT-5.6 Sol: 85.5 (#7)
- Llama 2-70B: 7.5 (#320)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 0% |
| LMArena Math | 1472 | 1091 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| MATH Level 5 | — | 3.3% |
| FrontierMath Erdős | 0% | — |
| GSM8K | — | 69.6% |

## Knowledge

- GPT-5.6 Sol: 64.3 (#16)
- Llama 2-70B: 7.4 (#305)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| GPQA Diamond | 93.5% | 26.3% |
| LMArena Expert | 1517 | 1039 |
| SimpleQA Verified | 69.7% | — |
| Vectara Hallucination Rate | 12.4% | — |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 88.6% |
| MMLU | — | 69.9% |
| OpenBookQA | — | 60.2% |
| TriviaQA | — | 87.6% |

## Multimodal

- GPT-5.6 Sol: 48.9 (#8)
- Llama 2-70B: —

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |

## Multilingual

- GPT-5.6 Sol: 55.4 (#32)
- Llama 2-70B: 27.9 (#269)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Non-English | 1450 | 1045 |
| LMArena Chinese | 1523 | 994 |
| LMArena French | 1475 | 1089 |
| LMArena German | 1476 | 1041 |
| LMArena Japanese | 1475 | 927 |
| LMArena Korean | 1434 | 964 |
| LMArena Russian | 1466 | 1083 |
| LMArena Spanish | 1449 | 1142 |

## Instruction Following

- GPT-5.6 Sol: 77.8 (#16)
- Llama 2-70B: 55.1 (#273)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1071 |

## Long Context

- GPT-5.6 Sol: 45.3 (#42)
- Llama 2-70B: 32.2 (#265)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Longer Query | 1479 | 1062 |

## Writing & Preference

- GPT-5.6 Sol: 73.5 (#12)
- Llama 2-70B: 32.5 (#274)

| Benchmark | GPT-5.6 Sol | Llama 2-70B |
|---|---|---|
| LMArena Text | 1456 | 1115 |
| LMArena Creative Writing | 1450 | 1075 |
| LMArena Multi-Turn | 1456 | 1088 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |

## FAQ

### Is GPT-5.6 Sol better than Llama 2-70B?

GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 24.1 on the Noometry Index.

### Is GPT-5.6 Sol or Llama 2-70B better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 30.8 in the Noometry coding category.

### How many benchmarks do GPT-5.6 Sol and Llama 2-70B share?

21 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and Llama 2-70B has 35.