StarCoder2 15B vs Code Llama 34B: Benchmark Comparison
Detailed comparison of StarCoder2 15B and Code Llama 34B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | StarCoder2 15B | Code Llama 34B |
|---|---|---|
| Vendor | other | meta |
| Version | starcoder2-15b | code-llama-34b |
| Release Date | 2024-02-28 | 2023-08-24 |
| Context Window | 16384 tokens | 16000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | BigCode Open Model License | Llama 2 Community License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | StarCoder2 15B | Code Llama 34B | Winner |
|---|---|---|---|
| ARC | 87.6 | 88.7 | Code Llama 34B |
| BBH | 62.8 | 59.1 | StarCoder2 15B |
| GPQA | 35.3 | 27 | StarCoder2 15B |
| GSM8K | 69.6 | 59.8 | StarCoder2 15B |
| HUMANEVAL | 73.5 | 71.7 | StarCoder2 15B |
| IFEVAL | 59.6 | 58.7 | StarCoder2 15B |
| MATH | 46.2 | 44.7 | StarCoder2 15B |
| MMLU | 60.6 | 74.5 | Code Llama 34B |
| MUSR | 51.7 | 44.9 | StarCoder2 15B |
| WINOGRANDE | 70.2 | 80 | Code Llama 34B |
Pricing Comparison
| Tier (per Mtok) | StarCoder2 15B | Code Llama 34B |
|---|---|---|
| Input | $0.3 | $0.5 |
| Output | $0.3 | $0.5 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
StarCoder2 15B против Code Llama 34B
Обзор модели
StarCoder2 15B and Code Llama 34B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Ключевые характеристики
| Поставщик | Дата выпуска | Окно контекста | Лицензия |
|---|---|---|---|
| Other / Meta | 2024-02-28 / 2023-08-24 | 16K / 16K | BigCode Open Model License / Llama 2 Community License |
Производительность
| Бенчмарк | StarCoder2 15B | Code Llama 34B | Победитель |
|---|---|---|---|
| ARC | 87.6 | 88.7 | B |
| BBH (BIG-Bench Hard) | 62.8 | 59.1 | A |
| GPQA | 35.3 | 27.0 | A |
| GSM8K (Grade School Math 8K) | 69.6 | 59.8 | A |
| HumanEval | 73.5 | 71.7 | A |
| IFEval | 59.6 | 58.7 | A |
| MATH | 46.2 | 44.7 | A |
| MMLU (Massive Multitask Language Understanding) | 60.6 | 74.5 | B |
| MUSR | 51.7 | 44.9 | A |
| WinoGrande | 70.2 | 80.0 | B |
Сравнение цен
| Вход | Выход | Чтение кэша | Запись кэша |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
за миллион токенов — A / B
Сильные стороны & Слабые стороны
StarCoder2 15B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
- ⚠️ 上下文窗口 16K 偏小。
Code Llama 34B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
- ⚠️ 上下文窗口 16K 偏小。
Мнение редактора
StarCoder2 15B and Code Llama 34B each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
ЧЗВ
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.
Ссылки
Editor's Take
See Editor's Take section.