Llama 3.2 11B Vision vs Gemma 2 9B: Benchmark Comparison
Detailed comparison of Llama 3.2 11B Vision and Gemma 2 9B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.2 11B Vision | Gemma 2 9B |
|---|---|---|
| Vendor | meta | |
| Version | 3.2-11b-vision | gemma-2-9b |
| Release Date | 2024-09-25 | 2024-06-27 |
| Context Window | 128000 tokens | 8192 tokens |
| Input Modalities | text, image | text |
| Output Modalities | text | text |
| License | Llama 3.2 Community License | Gemma License |
| SOC2 | ✗ | ✓ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✓ |
| ISO 27001 | ✗ | ✓ |
Benchmark Results
| Benchmark | Llama 3.2 11B Vision | Gemma 2 9B | Winner |
|---|---|---|---|
| ARC | 86 | 90.3 | Gemma 2 9B |
| BBH | 69.1 | 66.7 | Llama 3.2 11B Vision |
| GPQA | 35.5 | 30.3 | Llama 3.2 11B Vision |
| GSM8K | 68.2 | 64.3 | Llama 3.2 11B Vision |
| HUMANEVAL | 50.1 | 60.9 | Gemma 2 9B |
| IFEVAL | 58.4 | 64.3 | Gemma 2 9B |
| MATH | 29.9 | 28.9 | Llama 3.2 11B Vision |
| MMLU | 61.9 | 64.3 | Gemma 2 9B |
| MUSR | 41.7 | 44.1 | Gemma 2 9B |
| WINOGRANDE | 72.7 | 72.5 | Llama 3.2 11B Vision |
Pricing Comparison
| Tier (per Mtok) | Llama 3.2 11B Vision | Gemma 2 9B |
|---|---|---|
| Input | $0.55 | $0.3 |
| Output | $0.55 | $0.3 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.2 11B Vision kontra Gemma 2 9B
Przegląd modelu
Llama 3.2 11B Vision and Gemma 2 9B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Kluczowe specyfikacje
| Dostawca | Data wydania | Okno kontekstowe | Licencja |
|---|---|---|---|
| Meta / Google | 2024-09-25 / 2024-06-27 | 128K / 8K | Llama 3.2 Community License / Gemma License |
Wydajność benchmarków
| Benchmark | Llama 3.2 11B Vision | Gemma 2 9B | Zwycięzca |
|---|---|---|---|
| ARC | 86.0 | 90.3 | B |
| BBH (BIG-Bench Hard) | 69.1 | 66.7 | A |
| GPQA | 35.5 | 30.3 | A |
| GSM8K (Grade School Math 8K) | 68.2 | 64.3 | A |
| HumanEval | 50.1 | 60.9 | B |
| IFEval | 58.4 | 64.3 | B |
| MATH | 29.9 | 28.9 | A |
| MMLU (Massive Multitask Language Understanding) | 61.9 | 64.3 | B |
| MUSR | 41.7 | 44.1 | B |
| WinoGrande | 72.7 | 72.5 | Tie |
Porównanie cen
| Wejście | Wyjście | Odczyt pamięci podręcznej | Zapis pamięci podręcznej |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
za milion tokenów — A / B
Mocne strony & Słabe strony
Llama 3.2 11B Vision
- ✅ 支持文本、图像、音频多模态输入。
- ⚠️ 闭源专有模型，不支持自托管。
Gemma 2 9B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
- ⚠️ 上下文窗口 8K 偏小。
Opinia redakcji
Llama 3.2 11B Vision and Gemma 2 9B 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.
Referencje
Editor's Take
See Editor's Take section.