Llama 3.1 70B vs Qwen2.5 72B: Benchmark Comparison
Detailed comparison of Llama 3.1 70B and Qwen2.5 72B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.1 70B | Qwen2.5 72B |
|---|---|---|
| Vendor | meta | alibaba |
| Version | 3.1-70b | 2.5-72b |
| Release Date | 2024-07-23 | 2024-09-19 |
| Context Window | 128000 tokens | 131072 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Llama 3 Community License | Qwen License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | Llama 3.1 70B | Qwen2.5 72B | Winner |
|---|---|---|---|
| ARC | 92.3 | — | Llama 3.1 70B |
| BBH | 70.2 | 82.4 | Qwen2.5 72B |
| GPQA | 40 | — | Llama 3.1 70B |
| GSM8K | 78.8 | 88.4 | Qwen2.5 72B |
| HUMANEVAL | 79.7 | 86.6 | Qwen2.5 72B |
| IFEVAL | 73.7 | — | Llama 3.1 70B |
| MATH | 38.5 | 83.1 | Qwen2.5 72B |
| MMLU | 75.6 | 86.1 | Qwen2.5 72B |
| MUSR | 48.1 | — | Llama 3.1 70B |
| WINOGRANDE | 81 | — | Llama 3.1 70B |
Pricing Comparison
| Tier (per Mtok) | Llama 3.1 70B | Qwen2.5 72B |
|---|---|---|
| Input | $0.9 | $0.5 |
| Output | $0.9 | $0.8 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.1 70B kontra Qwen2.5 72B
Przegląd modelu
Llama 3.1 70B and Qwen2.5 72B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Kluczowe specyfikacje
| Dostawca | Data wydania | Okno kontekstowe | Licencja |
|---|---|---|---|
| Meta / Alibaba | 2024-07-23 / 2024-09-19 | 128K / 131K | Llama 3 Community License / Qwen License |
Wydajność benchmarków
| Benchmark | Llama 3.1 70B | Qwen2.5 72B | Zwycięzca |
|---|---|---|---|
| ARC | 92.3 | — | A |
| BBH (BIG-Bench Hard) | 70.2 | 82.4 | B |
| GPQA | 40.0 | — | A |
| GSM8K (Grade School Math 8K) | 78.8 | 88.4 | B |
| HumanEval | 79.7 | 86.6 | B |
| IFEval | 73.7 | — | A |
| MATH | 38.5 | 83.1 | B |
| MMLU (Massive Multitask Language Understanding) | 75.6 | 86.1 | B |
| MUSR | 48.1 | — | A |
| WinoGrande | 81.0 | — | A |
Porównanie cen
| Wejście | Wyjście | Odczyt pamięci podręcznej | Zapis pamięci podręcznej |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
za milion tokenów — A / B
Mocne strony & Słabe strony
Llama 3.1 70B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
Qwen2.5 72B
- ✅ MMLU score 86.1, strong knowledge reasoning.
- ✅ HumanEval 86.6, excellent code generation.
- ✅ GSM8K 88.4, robust math reasoning.
- ⚠️ 闭源专有模型，不支持自托管。
Opinia redakcji
Llama 3.1 70B and Qwen2.5 72B each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
FAQ
Which model is better for coding tasks?
Refer to the HumanEval benchmark table; the model with a higher score is better suited for coding tasks.
Which model is cheaper?
Refer to the pricing comparison table above; the model with lower input/output prices is more cost-effective.
Which has a longer context window?
Refer to the key specifications table; the model with a larger context window is better for long documents.
Referencje
Editor's Take
See Editor's Take section.