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 対 Code Llama 34B
モデル概要
DeepSeek Coder 33B and Code Llama 34B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
主要仕様
| ベンダー | リリース日 | コンテキストウィンドウ | ライセンス |
|---|---|---|---|
| Deepseek / Meta | 2024-01-25 / 2023-08-24 | 16K / 16K | DeepSeek License / Llama 2 Community License |
ベンチマークパフォーマンス
| ベンチマーク | DeepSeek Coder 33B | Code Llama 34B | 勝者 |
|---|---|---|---|
| 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 |
料金比較
| 入力 | 出力 | キャッシュ読み取り | キャッシュ書き込み |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
100万トークンあたり — A / B
強み & 弱み
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。
編集者コメント
DeepSeek Coder 33B and Code Llama 34B 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.
参考文献
Editor's Take
See Editor's Take section.