Claude 3.5 Haiku vs Llama 3.1 8B: Benchmark Comparison
Detailed comparison of Claude 3.5 Haiku and Llama 3.1 8B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Claude 3.5 Haiku | Llama 3.1 8B |
|---|---|---|
| Vendor | anthropic | meta |
| Version | 3.5-haiku | 3.1-8b |
| Release Date | 2024-11-04 | 2024-07-23 |
| Context Window | 200000 tokens | 128000 tokens |
| Input Modalities | text, image | text |
| Output Modalities | text | text |
| License | Proprietary | Llama 3 Community License |
| SOC2 | ✓ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✓ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | Claude 3.5 Haiku | Llama 3.1 8B | Winner |
|---|---|---|---|
| ARC | 91.8 | 87.4 | Claude 3.5 Haiku |
| BBH | 70.2 | 67.1 | Claude 3.5 Haiku |
| GPQA | 31.1 | 34.8 | Llama 3.1 8B |
| GSM8K | 75.5 | 58.8 | Claude 3.5 Haiku |
| HUMANEVAL | 73.8 | 63.4 | Claude 3.5 Haiku |
| IFEVAL | 73.6 | 62.1 | Claude 3.5 Haiku |
| MATH | 53 | 31.1 | Claude 3.5 Haiku |
| MMLU | 77.6 | 73 | Claude 3.5 Haiku |
| MUSR | 52.3 | 41.7 | Claude 3.5 Haiku |
| WINOGRANDE | 83.8 | 77.6 | Claude 3.5 Haiku |
Pricing Comparison
| Tier (per Mtok) | Claude 3.5 Haiku | Llama 3.1 8B |
|---|---|---|
| Input | $0.8 | $0.18 |
| Output | $4 | $0.18 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Claude 3.5 Haiku ضد Llama 3.1 8B
نظرة عامة على النموذج
Claude 3.5 Haiku and Llama 3.1 8B 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 |
|---|---|---|---|
| Anthropic / Meta | 2024-11-04 / 2024-07-23 | 200K / 128K | Proprietary / Llama 3 Community License |
أداء المعايير
| المعيار | Claude 3.5 Haiku | Llama 3.1 8B | الفائز |
|---|---|---|---|
| ARC | 91.8 | 87.4 | A |
| BBH (BIG-Bench Hard) | 70.2 | 67.1 | A |
| GPQA | 31.1 | 34.8 | B |
| GSM8K (Grade School Math 8K) | 75.5 | 58.8 | A |
| HumanEval | 73.8 | 63.4 | A |
| IFEval | 73.6 | 62.1 | A |
| MATH | 53.0 | 31.1 | A |
| MMLU (Massive Multitask Language Understanding) | 77.6 | 73.0 | A |
| MUSR | 52.3 | 41.7 | A |
| WinoGrande | 83.8 | 77.6 | A |
مقارنة الأسعار
| الإدخال | الإخراج | قراءة الذاكرة المؤقتة | كتابة الذاكرة المؤقتة |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
لكل مليون رمز — A / B
نقاط القوة & نقاط الضعف
Claude 3.5 Haiku
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
Llama 3.1 8B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
رأي المحرر
Claude 3.5 Haiku and Llama 3.1 8B 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.