Llama 3.1 405B vs Qwen2.5 72B: Benchmark Comparison
Detailed comparison of Llama 3.1 405B and Qwen2.5 72B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.1 405B | Qwen2.5 72B |
|---|---|---|
| Vendor | meta | alibaba |
| Version | 3.1-405b | 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 405B | Qwen2.5 72B | Winner |
|---|---|---|---|
| BBH | 82.9 | 82.4 | Llama 3.1 405B |
| GSM8K | 89.2 | 88.4 | Llama 3.1 405B |
| HUMANEVAL | 89 | 86.6 | Llama 3.1 405B |
| MATH | 73.8 | 83.1 | Qwen2.5 72B |
| MMLU | 88.6 | 86.1 | Llama 3.1 405B |
Pricing Comparison
| Tier (per Mtok) | Llama 3.1 405B | Qwen2.5 72B |
|---|---|---|
| Input | $5 | $0.5 |
| Output | $15 | $0.8 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.1 405B gegen Qwen2.5 72B
Modellübersicht
Llama 3.1 405B and Qwen2.5 72B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Hauptspezifikationen
| Anbieter | Veröffentlichungsdatum | Kontextfenster | Lizenz |
|---|---|---|---|
| Meta / Alibaba | 2024-07-23 / 2024-09-19 | 128K / 131K | Llama 3 Community License / Qwen License |
Benchmark-Leistung
| Benchmark | Llama 3.1 405B | Qwen2.5 72B | Gewinner |
|---|---|---|---|
| BBH (BIG-Bench Hard) | 82.9 | 82.4 | A |
| GSM8K (Grade School Math 8K) | 89.2 | 88.4 | A |
| HumanEval | 89.0 | 86.6 | A |
| MATH | 73.8 | 83.1 | B |
| MMLU (Massive Multitask Language Understanding) | 88.6 | 86.1 | A |
Preisvergleich
| Eingabe | Ausgabe | Cache-Lesen | Cache-Schreiben |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
pro Million Token — A / B
Stärken & Schwächen
Llama 3.1 405B
- ✅ MMLU score 88.6, strong knowledge reasoning.
- ✅ HumanEval 89.0, excellent code generation.
- ✅ GSM8K 89.2, robust math reasoning.
- ⚠️ 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。
Redaktionsmeinung
Llama 3.1 405B 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.
Referenzen
Editor's Take
See Editor's Take section.