Model comparison
Hy4 preview vs Llama 2-70B
Hy4 preview is the stronger model overall, scoring 45.3 to 24.4 on the Noometry Index.
Last verified . 0 shared benchmarks.
Tencent
MetaSummary
- The widest gap is in math, where Hy4 preview leads 55.7 to 8.1.
Side by side
| Hy4 preview | Llama 2-70B | |
|---|---|---|
| Provider | Tencent | Meta |
| Noometry Index | 45.3 | 24.4 |
| Released | 2026-08-28 | 2023-07-18 |
| Weights | Open | Open |
| Context window | 1.05M | — |
| Max output | 64K | — |
| Input $ / M tokens | $0.83 | — |
| Output $ / M tokens | $2.50 | — |
| Results tracked | 3 | 35 |
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Category by category
Coding Hy4 preview leads
Hy4 preview: 51.6 (#38), Llama 2-70B: 31.4 (#286)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| LMArena WebDev | 1632 | — |
| LMArena Coding | — | 1079 |
Reasoning Hy4 preview leads
Hy4 preview: 31.9 (#79), Llama 2-70B: 14.4 (#325)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| NYT Connections (extended) | 68.2% | — |
| LMArena Hard Prompts | — | 1073 |
| DTBench | — | 41.6% |
| BIG-Bench Hard | — | 64.9% |
| CommonsenseQA 2.0 | — | 50% |
| Epoch Capabilities Index | — | 113.79 |
| ForecastBench | — | 51.4 |
| HellaSwag | — | 85.3% |
| LAMBADA | — | 78.9% |
| PIQA | — | 82.8% |
| WinoGrande | — | 80.2% |
Math Hy4 preview leads
Hy4 preview: 55.7 (#42), Llama 2-70B: 8.1 (#326)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 0% |
| ProofBench | 75% | — |
| LMArena Math | — | 1091 |
| MATH Level 5 | — | 3.3% |
| GSM8K | — | 69.6% |
Knowledge Not comparable
Hy4 preview: —, Llama 2-70B: 7.4 (#310)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| GPQA Diamond | — | 26.3% |
| LMArena Expert | — | 1039 |
| ARC (AI2) Challenge | — | 78.3% |
| BoolQ | — | 88.6% |
| MMLU | — | 69.9% |
| OpenBookQA | — | 60.2% |
| TriviaQA | — | 87.6% |
Multilingual Not comparable
Hy4 preview: —, Llama 2-70B: 27.7 (#274)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| LMArena Non-English | — | 1045 |
| LMArena Chinese | — | 995 |
| LMArena French | — | 1090 |
| LMArena German | — | 1041 |
| LMArena Japanese | — | 927 |
| LMArena Korean | — | 964 |
| LMArena Russian | — | 1083 |
| LMArena Spanish | — | 1143 |
Instruction Following Not comparable
Hy4 preview: —, Llama 2-70B: 54.9 (#278)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| LMArena Instruction Following | — | 1071 |
Long Context Not comparable
Hy4 preview: —, Llama 2-70B: 32.3 (#270)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| LMArena Longer Query | — | 1062 |
Writing & Preference Not comparable
Hy4 preview: —, Llama 2-70B: 32.3 (#279)
| Benchmark | Hy4 preview | Llama 2-70B |
|---|---|---|
| LMArena Text | — | 1115 |
| LMArena Creative Writing | — | 1075 |
| LMArena Multi-Turn | — | 1088 |
Frequently asked questions
Is Hy4 preview better than Llama 2-70B?
Hy4 preview is the stronger model overall, scoring 45.3 to 24.4 on the Noometry Index.
Is Hy4 preview or Llama 2-70B better for coding?
Hy4 preview scores higher on coding benchmarks: 51.6 versus 31.4 in the Noometry coding category.
How many benchmarks do Hy4 preview and Llama 2-70B share?
0 benchmarks have published results for both models. Hy4 preview has 3 scored results on Noometry and Llama 2-70B has 35.