Language models > Llama 4 vs. GroqSonic 3
Selecting the right LLM requires careful consideration of factors like context window, speed, cost, and specific application needs. This comparison highlights the strengths of Llama 4 and GroqSonic 3, enabling a more informed decision.
This comparison analyzes real-world performance, user feedback, and key differentiators to help you make an informed decision.
Quick Verdict
The choice hinges on your priorities: Llama 4 for flexible fine-tuning and multilingual breadth, or GroqSonic 3 for speed-optimized coding and real-time web access. Evaluate long-term costs associated with API usage and infrastructure.
- Llama 4 offers open-source fine-tuning capabilities and multilingual support, while GroqSonic 3 emphasizes speed and coding proficiency.
- Consider the trade-offs between Llama 4's larger context windows and GroqSonic 3's potential energy efficiency.
Who Should Choose Which?
Choose Llama 4 if:
Researchers and developers who need extensive fine-tuning capabilities, multilingual support, and access to open-source models.
Choose GroqSonic 3 if:
Organizations prioritizing speed, coding performance, and real-time web access, particularly those working on developer tools or applications requiring rapid response times.
Key features – Side-by-Side
| Attribute | Llama 4 | GroqSonic 3 |
|---|---|---|
| Context Window Length | — | 128,000 tokens (API version), up to 1 million tokens claimed |
| Training Data Size | — | 12.8 trillion tokens |
| Number of Parameters | — | 300 billion - 2.7 trillion (estimated) |
| Inference Speed | — | 276-284 tokens/second (Llama 3.3 70B) |
| API Pricing (per 1M tokens) | — | $0.59 input / $0.79 output (Llama 3.3 70B) |
Overall Comparison
Pros and Cons
Llama 4
- Enables open source fine-tuning
- Supports multiple languages
- Available on llama.com and Hugging Face
- Can be run on a single NVIDIA H100 DGX host for easy deployment, or with distributed inference for maximum efficiency
- Llama 4 Scout model is much more energy efficient than the dense model.
- Drastically reduce hallucinations through direct mapping of questions to verified citations.
- Code generation performance falls far short of top-tier proprietary rivals.
- Inferior performance in generating, debugging, and solving complex coding problems compared to leading proprietary models.
- Requires significant VRAM (55-60GB for 4-bit quantized version of Llama 4 Scout) and even more for FP16/128K configuration (145,016GB VRAM and 5,016 H100 GPUs).
GroqSonic 3
- Real-time web access
- Advanced problem-solving
- Large context window
- Faster processing speeds
- Higher accuracy in natural language tasks
- Excels in technical reasoning, math, science, and coding
- Can be integrated into existing workflows and applications
- Struggles with niche creative tasks like generating poetry
- Some users report that older models outperform it in real-world tasks
User Experiences and Feedback
Llama 4
- Intended for commercial and research use in multiple languages.
- Instruction tuned models are intended for assistant-like chat and visual reasoning tasks.
- Pretrained models can be adapted for natural language generation.
- Llama 4 Maverick is the best-in-class multimodal model, exceeding comparable models like GPT-4o and Gemini 2.0 on coding, reasoning, multilingual, long-context, and image benchmarks.
- No major complaints reported.
- Llama 4 Scout: ~$0.09 per million tokens.
GroqSonic 3
- Delivers faster processing speeds and higher accuracy in natural language tasks compared to similar models.
- Excels in coding tasks. It can assist developers by reviewing, optimizing, and debugging code, and generating code snippets based on specific requirements.
- Maintains consistent speed across all input token counts tested.
- Can be integrated into existing workflows and applications.
- Some users report that older models outperform it in real-world tasks.
- Limitations include struggles with niche creative tasks like generating poetry.
- Offers real-time web access, advanced problem-solving, and a large context window.
- Available through an API. Groq offers Llama 3.3 70B Versatile 128k at a price of $0.59 (input) and $0.79 (output) per 1M tokens.
Frequently Asked Questions
Which model is better for coding tasks?
GroqSonic 3 is specifically noted for excelling in coding tasks, while Llama 4's code generation performance is considered respectable but not top-tier.
Which model has a larger context window?
Llama 4 Scout has the largest context window at 10 million tokens, followed by Llama 4 Maverick at 1 million tokens. GroqSonic 3 has a context window of 128,000 tokens (API version), with some sources claiming 1 million tokens.
Sources & Citations
- Official product specifications
- Expert reviews from tech publications
- User feedback from online forums
Information gathered through AI-assisted web search and analysis. Last updated: October 2025
Methodology & Transparency
Our comparison methodology combines multiple data sources to provide comprehensive, unbiased analysis:
- Data Collection: We gather information from official specifications, user reviews, and independent testing
- AI-Assisted Analysis: Advanced AI helps process large amounts of data while maintaining accuracy
- Human Oversight: All comparisons are reviewed for accuracy and relevance
- Regular Updates: Content is refreshed to reflect new information and user feedback
- Bias Mitigation: We strive for objectivity by considering multiple perspectives and sources
Versusly.ai uses AI-assisted content generation combined with human oversight to deliver comprehensive comparisons. We are transparent about our process and continuously work to improve accuracy and usefulness.