Model comparison
GPT-5.5 vs Llama 2-13B
GPT-5.5 is the stronger model overall, scoring 63.4 to 29.1 on the Noometry Index.
Last verified . 20 shared benchmarks.
MetaSummary
- They share 20 benchmarks with published results for both. GPT-5.5 scores higher in 8 categories and Llama 2-13B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.7 to 12.3.
- The biggest single-benchmark swing is Chess Puzzles: 54% for GPT-5.5 and 0% for Llama 2-13B.
- Llama 2-13B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 | Llama 2-13B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 63.4 | 29.1 |
| Released | 2026-04-23 | 2023-07-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $5 | — |
| Output $ / M tokens | $30 | — |
| Results tracked | 71 | 32 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.3 (#13), Llama 2-13B: 30.4 (#287)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Coding | 1494 | 1062 |
| SWE-bench Verified | 80.6% | — |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| LMArena WebDev | 1512 | — |
| SciCode | 56.1% | — |
| GSO | 40.2% | — |
| WeirdML | 84.9% | — |
| MirrorCode | 10% | — |
| ALE-Bench | 1,943 | — |
Agentic & Tool Use Not comparable
GPT-5.5: 50.7 (#6), Llama 2-13B: —
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| Terminal-Bench | 84.7% | — |
| APEX-Agents | 55.1% | — |
| OSWorld 2.0 | 13% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| 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: 72.7 (#8), Llama 2-13B: 12.3 (#333)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| Chess Puzzles | 54% | 0% |
| LMArena Hard Prompts | 1489 | 1051 |
| DTBench | 96% | 42.2% |
| Epoch Capabilities Index | 159.25 | 105.8 |
| 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% | — |
| LMCA | 54.3% | — |
| Surface Evolver Bench | 88.1% | — |
| Bench to the Future 3 | 0.14 | — |
| BIG-Bench Hard | — | 58.2% |
| ForecastBench | 60.6 | — |
| HellaSwag | — | 80.7% |
| LAMBADA | — | 76.5% |
| PIQA | — | 80.8% |
| WinoGrande | — | 72.8% |
Math GPT-5.5 leads
GPT-5.5: 81.5 (#9), Llama 2-13B: 28.7 (#226)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Math | 1486 | 1065 |
| FrontierMath (Tiers 1-3) | 85.3% | — |
| FrontierMath Tier 4 | 72.5% | — |
| MathArena Final-Answer Competitions | 94.3% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 50% | — |
| FrontierMath (Feb 2025 set) | 51.7% | — |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
| GSM8K | — | 36.9% |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#14), Llama 2-13B: 27.5 (#244)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Expert | 1508 | 1030 |
| GPQA Diamond | 94% | — |
| SimpleQA Verified | 63% | — |
| Vectara Hallucination Rate | 9.3% | — |
| ARC (AI2) Challenge | — | 60.3% |
| BoolQ | — | 82.4% |
| MMLU | — | 55.6% |
| OpenBookQA | — | 57% |
| TriviaQA | — | 79.6% |
Multimodal Not comparable
GPT-5.5: 47.1 (#11), Llama 2-13B: —
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
| LMArena Document | 1486 | — |
| ScienceQA | — | 55.8% |
Multilingual GPT-5.5 leads
GPT-5.5: 56.5 (#19), Llama 2-13B: 26.7 (#274)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Non-English | 1467 | 1024 |
| LMArena Chinese | 1535 | 1001 |
| LMArena French | 1486 | 1043 |
| LMArena German | 1478 | 1009 |
| LMArena Japanese | 1498 | 894 |
| LMArena Korean | 1461 | 953 |
| LMArena Russian | 1473 | 1055 |
| LMArena Spanish | 1469 | 1086 |
Instruction Following GPT-5.5 leads
GPT-5.5: 77.7 (#18), Llama 2-13B: 53.5 (#282)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Instruction Following | 1479 | 1046 |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Llama 2-13B: 32.3 (#264)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Longer Query | 1484 | 1064 |
| CL-bench Life | 22.2% | — |
Writing & Preference GPT-5.5 leads
GPT-5.5: 72.8 (#13), Llama 2-13B: 30.0 (#284)
| Benchmark | GPT-5.5 | Llama 2-13B |
|---|---|---|
| LMArena Text | 1471 | 1085 |
| LMArena Creative Writing | 1455 | 1047 |
| LMArena Multi-Turn | 1477 | 1050 |
| EQ-Bench Creative Writing | 1844 | — |
| EQ-Bench 4 | 1315 | — |
Frequently asked questions
Is GPT-5.5 better than Llama 2-13B?
GPT-5.5 is the stronger model overall, scoring 63.4 to 29.1 on the Noometry Index.
Is GPT-5.5 or Llama 2-13B better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.3 versus 30.4 in the Noometry coding category.
How many benchmarks do GPT-5.5 and Llama 2-13B share?
20 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Llama 2-13B has 32.