Code Llama 70B vs Codestral: Benchmark Comparison
Detailed comparison of Code Llama 70B and Codestral covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Code Llama 70B | Codestral |
|---|---|---|
| Vendor | meta | mistral |
| Version | code-llama-70b | codestral |
| Release Date | 2024-01-29 | 2024-05-29 |
| Context Window | 16000 tokens | 32000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Llama 2 Community License | MNPL |
| SOC2 | ✗ | ✓ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✓ |
| ISO 27001 | ✗ | ✓ |
Benchmark Results
| Benchmark | Code Llama 70B | Codestral | Winner |
|---|---|---|---|
| ARC | 89.8 | 87.4 | Code Llama 70B |
| BBH | 72.1 | 58.3 | Code Llama 70B |
| GPQA | 25.3 | 32.4 | Codestral |
| GSM8K | 61 | 63.3 | Codestral |
| HUMANEVAL | 65.6 | 69.2 | Codestral |
| IFEVAL | 63.8 | 59.4 | Code Llama 70B |
| MATH | 34 | 29.4 | Code Llama 70B |
| MMLU | 62.1 | 75.1 | Codestral |
| MUSR | 40.1 | 47.2 | Codestral |
| WINOGRANDE | 75.5 | 75.8 | Codestral |
Pricing Comparison
| Tier (per Mtok) | Code Llama 70B | Codestral |
|---|---|---|
| Input | $0.9 | $0.3 |
| Output | $0.9 | $0.9 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Code Llama 70B 対 Codestral
モデル概要
Code Llama 70B and Codestral 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 |
|---|---|---|---|
| Meta / Mistral | 2024-01-29 / 2024-05-29 | 16K / 32K | Llama 2 Community License / MNPL |
ベンチマークパフォーマンス
| ベンチマーク | Code Llama 70B | Codestral | 勝者 |
|---|---|---|---|
| ARC | 89.8 | 87.4 | A |
| BBH (BIG-Bench Hard) | 72.1 | 58.3 | A |
| GPQA | 25.3 | 32.4 | B |
| GSM8K (Grade School Math 8K) | 61.0 | 63.3 | B |
| HumanEval | 65.6 | 69.2 | B |
| IFEval | 63.8 | 59.4 | A |
| MATH | 34.0 | 29.4 | A |
| MMLU (Massive Multitask Language Understanding) | 62.1 | 75.1 | B |
| MUSR | 40.1 | 47.2 | B |
| WinoGrande | 75.5 | 75.8 | 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
Code Llama 70B
- ✅ 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。
Codestral
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
編集者コメント
Code Llama 70B and Codestral 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.