GroqSonic 1 is better suited for applications requiring high-speed inference and large context windows, while Inflection AI Pi 3 is a better choice for applications prioritizing safety, ethical considerations, and strong reasoning abilities. The choice depends heavily on the specific needs and priorities of the user.
| Attribute | Inflection AI Pi 3 | GroqSonic 1 |
|---|---|---|
| Context Window Size (Tokens) | 8k (though one source mentions a limited context window of 1000 tokens) | Groq's architecture allows for context windows from 10k to 100k tokens. Llama 3.3 70B model on Groq has an 8196 context length. |
| Training Data Size | Vast datasets of deeply emotional conversations between real people and billions of lines of text on the open web. | The size of the training data impacts a model's biases and performance in different domains. Unrepresentative data can lead to skewed and misleading responses. |
| Parameter Count | 13 billion (Inflection-2 is a 175 billion parameter model, potentially 400 billion) | Models range in size from GPT-1 at 512 tokens, to the Llama models going from Llama 2 at 4,096 to Llama 3 at 8,192 all the way to 3.1 at 128,000. |
| Inference Speed (Tokens/Second) | Inflection-2 is more cost-effective and faster in serving. | Llama 3.3 70B has been benchmarked at 276 tokens per second on Groq. Groq claims speeds of over 1,200 tokens/sec with Llama 3 8B. GroqChat can generate 1200 tokens per second using Llama 3-8b with 8196 context length. |
| Finetuning Capabilities | Proprietary finetuning system using reinforcement learning from employee feedback. | Finetuning can adapt a model to specific tasks and datasets. Fine-tuning can improve a model's code generation performance. |
| Multilingual Support (Number of Languages) | Yes | Some models offer multilingual support, with Whisper Large v3 supporting 99+ languages. |
| Code Generation Performance (Pass@k) | Inflection-2.5 demonstrated significant improvement in a test that comprised coding tasks. | Models can be fine-tuned to improve code generation performance. |
| Reasoning Ability (e.g., MMLU score) | Inflection-2.5 outperforms its predecessor on the MMLU benchmark and performs at the 85th percentile of human test-takers on the Physics GRE. | MMLU (Massive Multitask Language Understanding) is a benchmark used to evaluate a model's reasoning capabilities across various subjects. |
| Hallucination Rate | Not available | Models can sometimes generate incorrect or nonsensical information. |
| API Availability & Pricing | Yes, a commercial API is available. The pricing is $2.50 per 1 million input tokens and $10 per 1 million output tokens. | Groq offers an inference API. Groq offers Llama 3.3 70B Versatile 128k at an input price of $0.59 per million tokens and an output price of $0.79 per million tokens. Groq is giving away five billion tokens per day for free. |
| Safety Measures & Bias Mitigation | Designed to be a safer alternative, avoids harmful, abusive, or illegal topics. Employs 'empathetic fine-tuning'. Pi was launched to prevent bias. | Safety measures are implemented to prevent models from generating harmful or inappropriate content. Safety measures include modification of packaging and labeling, and substituting chemicals with a lower toxicity profile. |
| Energy Efficiency (Inference Cost) | Not available | Energy consumption is a significant factor in AI. |
| Price | Not available | Groq offers Llama 3.3 70B Versatile 128k at an input price of $0.59 per million tokens and an output price of $0.79 per million tokens. Groq is giving away five billion tokens per day for free. |
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