Claude 3 sonnet vs opus when to choose which for your LLM?

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For startups developing LLM apps, choosing the right LLM to integrate can determine the ease and flexibility of meeting your users’ needs.

It is, therefore, an important and long-term decision that has to be made taking a comprehensive look at what’s available to us in the LLM landscape. For that we’ll be looking at Claude and their models, the leading LLM on the market right now.

Claude 3 is AI unicorn Anthropic’s large language model (LLM). Technically, there are 3 different models that fall under the Claude 3 umbrella: Opus, Sonnet and Haiku.

Anthropic’s way of allowing a choice of 3 models is actually a nice approach to convey the differing needs and resources of different startups. The naming of each model also indicates a hint towards the best use case for it:

Haiku, much like the short and fixed structure form of poetry, represents the model designed for simple tasks that require speed.

Sonnet, also like the poems but a little more concise and varied, is a model tailored for apps where a balance between speed and intelligence is preferred.

And Opus, defined as a large scale piece of artwork, is the most advanced model and best for startups needing the maximum intelligence and capability.

Like Goldilocks navigating her options between too hot and too cold, companies are increasingly evaluating different offerings, looking to choose not just any model, but the one that aligns “just right” with their specific needs and goals.

Despite the availability of three models, in this article we’ll be looking at the two most intelligent models in Opus and Sonnet to determine which of these is best for different LLM apps.

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Overview of Claude 3 Sonnet

Claude 3 Sonnet is the model referred to as the ‘work horse’ by Anthropic themselves and it’s because this model is meant to be the Goldilocks option that balances both speed and intelligence in performance but at a lower cost to Opus. Which is why this model is engineered for solid endurance in large scale deployments.

This means Sonnet is best suited for startups needing it to handle tasks that demand rapid responses in large volumes. Use cases for that could be knowledge retrieval or sales automation.

Key Features of Claude 3 Sonnet:

Claude 3 Sonnet is best at generating creative content like poems, stories, and dialogues because it's trained on a diverse dataset that includes literary works. This enables Sonnet to come out with text that is stylistic.

The model is also fine tuned to advanced human level language such as metaphors and similes. This makes this very useful for applications that require some sort of sophistication in terms of nuance in Language such as communication heavy use cases.

Sonnet is very good at remembering context from long texts. This becomes very useful when it’s important to maintain coherence in narratives and descriptions that need to stay logically consistent.

Use Cases for Claude 3 Sonnet:

More creative roles such as writers and marketers will find a lot of use with the Sonnet model given they need to generate more imaginative content. Its training on literary works helps this model produce more relevant responses where applications need creativity and nuanced language.

For example, it’s good for LLM apps needing to create literature and ideal for a target audience of writers who need a jumpstart on their creativity or just trying to tackle writer’s block.

This also makes it useful for script and dialogue writing. An LLM app for screenwriters who need help in crafting natural sounding language will find this model is the most in tune with the type of writing they need.

Marketing and branding is another use case for this model where creating compelling and effective marketing copy is more important than handling complex tasks and problem solving.

Overview of Claude 3 Opus

As we’ve briefly mentioned, the Opus model is the most intelligent between the two and stands as the best model in the evaluation benchmarks for AI systems.

The Opus model is the most advanced for multimodal data which means it can process and analyse text, images, charts and diagrams the best of the available models. When combined with the 99% accuracy it scored in the NIAH evaluation (test to verify ability to recall information) it positions itself as the model of choice for apps in fields like healthcare, engineering, and data analysis, where visual information and near perfect recall is of critical importance.

It’s more expensive than the Sonnet model because of its intelligence but the near human levels of comprehension and fluency can make it the ideal choice if you’re a startup that needs the highest level of performance and are willing to invest in the most powerful model for your LLM app.

Key Features of Claude 3 Opus:

Training data for this model contains diverse, rich sets of information and technical jargon, so in general, it becomes very capable of producing precise and accurate technical texts. This positions it well for generating technical content like manuals, reports or documentation.

Even more, it can churn through big data sources and still return a succinct summary that in itself is already so valuable for any startup out there to quickly distill information from large, extensive data sources.

Claude 3 Opus is incredibly good at pulling out relevant data from extreme databases and then boiling it down into clear, coherent, and comprehensive reports. In this way, the feature makes it quite fitting for LLM apps dealing with heavy research activities, journalism, and academia.

Benchmark tests also show that it can perform translation tasks with extremely high accuracy, making this model an easy choice for startups targeting global markets or any form of international collaboration.

Use Cases for Claude 3 Opus:

We've mentioned the Opus model's higher intelligence and that lends itself to use cases that require more detailed and accurate content. These capabilities will make it fit for LLM apps in technical writing, research, and multi-lingual communication.

Technical Documentation: Ideally suited for engineers, scientists, and technical writers who will be developing detailed and accurate documentation.

Research and Reporting: Researchers and analysts use this to help compile large data volumes and summarize them.

Customer Support and Knowledge Management: This can be applied to the customer support system to provide more in depth and proper responses to technical queries while taking into account more factors.

Content Localisation: this enables the translation and adaptation of a startup's content into foreign markets with this model scoring the highest accuracy for translation and consistency across different languages.

Training and Design

The training of Claude 3 models emphasizes being helpful, harmless, and honest. Anthropic’s approach to development and training is for their models to be “as trustworthy as they are capable”.

The Claude 3 models are trained using a blend of publicly available internet data as of August 2023, along with public data from data labelling services and synthetic data generated internally.

The training process involves several data cleaning and filtering methods, including deduplication and classification. The models are not trained on any user submitted prompt or output data.

Anthropic follows industry practices when obtaining data from public web pages, respecting robots.txt instructions and other signals indicating whether crawling is permitted. The crawling system also operates transparently, allowing website operators to identify Anthropic visits and signal their preferences.

Anthropic continues to advance safety and transparency initiatives, such as Constitutional AI and has refined the models to address privacy concerns introduced by new features. They have dedicated teams focused on addressing a wide range of risks and are currently classified as AI Safety Level 2 (ASL-2) under Anthropic's Responsible Scaling Policy.

Red teaming evaluations, conducted in accordance with commitments made to the White House and the 2023 US Executive Order, have also determined that the models currently pose a negligible catastrophic risk.

Comparison Metrics

For digestibility of the differences between the two models, here’s a comparative overview of Sonnet and Opus:

Performance and Utility:

Claude 3 Opus stands out as the most powerful model, designed for highly complex tasks, showcasing top-level performance, intelligence, fluency, and understanding.

Claude 3 Sonnet offers an ideal balance of intelligence and speed, making it a dependable choice for enterprise workloads that demand maximum utility at a lower price.

Context Window:

They share the same maximum output of 4096 tokens and a large context window of 200K, providing ample space for remembering context to provide complex inputs and outputs.

Latency:

Latency varies among the models, with Sonnet being slightly faster. This indicates that while Opus focuses on depth and complexity, Sonnet balances both speed and intelligence.

Pricing:

Claude 3 Opus is the most expensive, costing $15 per million tokens for input and $75 per million tokens for output, reflecting its high level capabilities.

Claude 3 Sonnet presents a more affordable option at $3 per million tokens for input and $15 per million tokens for output, suitable for startups seeking a balance between cost and performance.

Ease of integration:

For ease of integration, they are both available via API and even through Amazon Bedrock.

Claude 3 Sonnet can be easily integrated into platforms that require creative content generation, such as content management systems (CMS), educational tools, and marketing software.

Claude 3 Opus is designed for integration into more technically demanding environments, such as data analysis tools, customer support systems, and technical documentation platforms.

If you’re looking for a more detailed comparison that could also influence your decision-making, read our previous article comparing Claude 3 Sonnet to GPT-4 here.

Choosing the right LLM for your needs

For startups, the utility of Sonnet and Opus will vary based on specific needs and resource allocation.

Sonnet could find its niche in balancing cost with capability—it strikes the ideal balance between intelligence and speed. Sonnet offers a more nuanced approach for content creation or data analysis that requires a blend of text and visual information. It delivers strong performance at a lower cost compared to its peers, making it an all-rounder for large-scale LLM apps requiring high levels of endurance.

Sonnet differentiator: More affordable than other models with similar intelligence - better for scale.

Opus, with its higher price point and advanced capabilities, is geared towards startups comfortable enough to invest in the most sophisticated AI model for complex problem-solving or in-depth analysis in their LLM apps.

Differentiator: Higher intelligence and more accurate recall than any other model available - better for complexity.

Here’s a simple 3 step process to an informed LLM selection:

1. Define your needs: think about your LLM app’s needs for the long term - how complex will your tasks be, how much latency can you afford, how much complexity do you need in your responses.
2. Test each model: Run small tests to evaluate each model's performance under contained environments and determine which produces the best output for your users.
3. Evaluate cost effectiveness: after testing, weigh up the cost effectiveness of each model based on the accuracy of responses and your available resources.

To test the viability of each model, try integrating them both and use text analytics tools like Phospho to get full visibility into which one performs better for your LLM app users with a data driven approach. If you’re creating an LLM app and want to see which model your users prefer, sign up here!

Claude 3 Sonnet or Opus: Which one to choose?

As a general guideline on which to choose for your LLM app, consider the specific needs and use cases for your AI startup, look at which model is best suited for them based on speed and intelligence, and factor in the price per million tokens to get a more holistic idea of cost-effectiveness.

In short, Sonnet would be a better fit for startups and scale-ups looking for the most ‘bang for their buck’ in terms of a balance in intelligence and speed. However, for startups requiring far more complexity and the maximum capability of AI models, Opus would better facilitate that for your LLM app.

Understanding the differences between these models can help you leverage the full potential of Claude’s LLM for your startup. As these models continue to evolve in capabilities, our choices between them will involve more scope for further bespoke and specific use cases. It’s important to have a comprehensive understanding of them and we hope this article gave you some insight into making a more informed choice.

If you’re curious, sign up and start testing out Phospho on your LLM app here, or take our open-source package for a spin here from our GitHub repo. We welcome contributions from everyone.