DeepSeek Coder 33B vs Code Llama 34B: Benchmark Comparison
Detailed comparison of DeepSeek Coder 33B and Code Llama 34B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | DeepSeek Coder 33B | Code Llama 34B |
|---|---|---|
| Vendor | deepseek | meta |
| Version | coder-33b | code-llama-34b |
| Release Date | 2024-01-25 | 2023-08-24 |
| Context Window | 16384 tokens | 16000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | DeepSeek License | Llama 2 Community License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | DeepSeek Coder 33B | Code Llama 34B | Winner |
|---|---|---|---|
| ARC | 92.2 | 88.7 | DeepSeek Coder 33B |
| BBH | 61.6 | 59.1 | DeepSeek Coder 33B |
| GPQA | 26.8 | 27 | Code Llama 34B |
| GSM8K | 69.7 | 59.8 | DeepSeek Coder 33B |
| HUMANEVAL | 71.8 | 71.7 | DeepSeek Coder 33B |
| IFEVAL | 58.8 | 58.7 | DeepSeek Coder 33B |
| MATH | 32.1 | 44.7 | Code Llama 34B |
| MMLU | 65.9 | 74.5 | Code Llama 34B |
| MUSR | 45.8 | 44.9 | DeepSeek Coder 33B |
| WINOGRANDE | 79.8 | 80 | Code Llama 34B |
Pricing Comparison
| Tier (per Mtok) | DeepSeek Coder 33B | Code Llama 34B |
|---|---|---|
| Input | $0.28 | $0.5 |
| Output | $0.28 | $0.5 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
DeepSeek Coder 33B ضد Code Llama 34B
نظرة عامة على النموذج
DeepSeek Coder 33B and Code Llama 34B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
المواصفات الرئيسية
| المزود | تاريخ الإصدار | نافذة السياق | الترخيص |
|---|---|---|---|
| Deepseek / Meta | 2024-01-25 / 2023-08-24 | 16K / 16K | DeepSeek License / Llama 2 Community License |
أداء المعايير
| المعيار | DeepSeek Coder 33B | Code Llama 34B | الفائز |
|---|---|---|---|
| ARC | 92.2 | 88.7 | A |
| BBH (BIG-Bench Hard) | 61.6 | 59.1 | A |
| GPQA | 26.8 | 27.0 | Tie |
| GSM8K (Grade School Math 8K) | 69.7 | 59.8 | A |
| HumanEval | 71.8 | 71.7 | Tie |
| IFEval | 58.8 | 58.7 | Tie |
| MATH | 32.1 | 44.7 | B |
| MMLU (Massive Multitask Language Understanding) | 65.9 | 74.5 | B |
| MUSR | 45.8 | 44.9 | A |
| WinoGrande | 79.8 | 80.0 | Tie |
مقارنة الأسعار
| الإدخال | الإخراج | قراءة الذاكرة المؤقتة | كتابة الذاكرة المؤقتة |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
لكل مليون رمز — A / B
نقاط القوة & نقاط الضعف
DeepSeek Coder 33B
- ✅ 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。
رأي المحرر
DeepSeek Coder 33B and Code Llama 34B each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
الأسئلة الشائعة
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.
المراجع
Editor's Take
See Editor's Take section.