Model comparison
GPT-5.4 vs Llama 2-7B
GPT-5.4 is the stronger model overall, scoring 59.4 to 29.1 on the Noometry Index.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. GPT-5.4 scores higher in 8 categories and Llama 2-7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 leads 61.8 to 15.7.
- The biggest single-benchmark swing is Chess Puzzles: 44% for GPT-5.4 and 0% for Llama 2-7B.
- Llama 2-7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 | Llama 2-7B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 59.4 | 29.1 |
| Released | 2026-03-05 | 2023-07-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $2.50 | — |
| Output $ / M tokens | $15 | — |
| Results tracked | 68 | 29 |
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Category by category
Coding GPT-5.4 leads
GPT-5.4: 52.6 (#33), Llama 2-7B: 29.2 (#307)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Coding | 1497 | 1002 |
| SWE-bench Verified | 76.9% | — |
| DeepSWE | 51.8% | — |
| LMArena WebDev | 1465 | — |
| SciCode | 56.6% | — |
| GSO | 31.4% | — |
| WeirdML | 77.7% | — |
| MirrorCode | 15.6% | — |
| ALE-Bench | 1,607 | — |
| AlgoTune | 1.85 | — |
Agentic & Tool Use Not comparable
GPT-5.4: 46.5 (#13), Llama 2-7B: —
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| Terminal-Bench | 81.8% | — |
| APEX-Agents | 52.4% | — |
| τ²-bench Banking | 39.4% | — |
| DeepResearch Bench | 35.1% | — |
| PostTrainBench | 19% | — |
| GBAEval | 45.1% | — |
| LMArena Search | 1197 | — |
| METR Time Horizons | 74.3% | — |
| Vending-Bench 2 | 6,144 | — |
Reasoning GPT-5.4 leads
GPT-5.4: 61.8 (#19), Llama 2-7B: 15.7 (#312)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| Chess Puzzles | 44% | 0% |
| LMArena Hard Prompts | 1485 | 1009 |
| Epoch Capabilities Index | 156.81 | 99.06 |
| ARC-AGI-2 | 74% | — |
| Kagi LLM Benchmark | 63.8% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 93.7% | — |
| CritPt | 23.4% | — |
| EnigmaEval | 16% | — |
| Thematic Generalization | 80% | — |
| EBR-Bench | 25.4% | — |
| Mystery Game Puzzles | 37% | — |
| DTBench | 94.4% | — |
| LMCA | 52% | — |
| BIG-Bench Hard | — | 39.2% |
| ForecastBench | 59.5 | — |
| HellaSwag | — | 77.2% |
| LAMBADA | — | 73.3% |
| PIQA | — | 78.8% |
| WinoGrande | — | 69.2% |
Math GPT-5.4 leads
GPT-5.4: 73.5 (#19), Llama 2-7B: 30.7 (#233)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Math | 1488 | 1042 |
| FrontierMath (Tiers 1-3) | 78.6% | — |
| FrontierMath Tier 4 | 49% | — |
| MathArena Final-Answer Competitions | 83.1% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 56% | — |
| FrontierMath (Feb 2025 set) | 47.6% | — |
| FrontierMath Tier 4 (v1) | 27.1% | — |
| GSM8K | — | 16.7% |
Knowledge GPT-5.4 leads
GPT-5.4: 65.3 (#14), Llama 2-7B: 28.2 (#248)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Expert | 1507 | 1036 |
| GPQA Diamond | 93.3% | — |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 45.1% | — |
| Vectara Hallucination Rate | 7% | — |
| ARC (AI2) Challenge | — | 45.9% |
| BoolQ | — | 77.9% |
| MMLU | — | 45.8% |
| OpenBookQA | — | 58.6% |
| TriviaQA | — | 73.7% |
Multimodal Not comparable
GPT-5.4: 43.7 (#20), Llama 2-7B: —
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Vision | 1303 | — |
| Blueprint-Bench 2 | 27.1% | — |
| Furniture Assembly | 37.5% | — |
| LMArena Document | 1471 | — |
| ScienceQA | — | 43.1% |
Multilingual GPT-5.4 leads
GPT-5.4: 56.2 (#23), Llama 2-7B: 23.8 (#293)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Non-English | 1465 | 973 |
| LMArena Chinese | 1519 | 973 |
| LMArena French | 1493 | 970 |
| LMArena German | 1472 | 978 |
| LMArena Russian | 1480 | 995 |
| LMArena Spanish | 1454 | 1007 |
| LMArena Japanese | 1485 | — |
| LMArena Korean | 1448 | — |
Instruction Following GPT-5.4 leads
GPT-5.4: 77.1 (#27), Llama 2-7B: 50.8 (#298)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | 1469 | 1006 |
Long Context GPT-5.4 leads
GPT-5.4: 50.3 (#8), Llama 2-7B: 30.4 (#287)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | 1473 | 999 |
| CL-bench | 27.9% | — |
| CL-bench Life | 21.7% | — |
Writing & Preference GPT-5.4 leads
GPT-5.4: 71.9 (#17), Llama 2-7B: 28.0 (#298)
| Benchmark | GPT-5.4 | Llama 2-7B |
|---|---|---|
| LMArena Text | 1469 | 1053 |
| LMArena Creative Writing | 1439 | 1033 |
| LMArena Multi-Turn | 1482 | 1029 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1272 | — |
Frequently asked questions
Is GPT-5.4 better than Llama 2-7B?
GPT-5.4 is the stronger model overall, scoring 59.4 to 29.1 on the Noometry Index.
Is GPT-5.4 or Llama 2-7B better for coding?
GPT-5.4 scores higher on coding benchmarks: 52.6 versus 29.2 in the Noometry coding category.
How many benchmarks do GPT-5.4 and Llama 2-7B share?
17 benchmarks have published results for both models. GPT-5.4 has 68 scored results on Noometry and Llama 2-7B has 29.