The AI world just doesn’t slow down. Last week, Google released 15 new products at their annual I/O event and OpenAI bought Jony Ive’s company for $6bn+. But it was Anthropic that stole the spotlight with its largest release in a while.
Anthropic released two new models, Claude Opus 4 and Claude Sonnet 4, along with important prompt migration information and four new API features.
This launch signals Anthropic’s focus on developers and coding agents, and less on the battle of the chatbot. We dove into the details, including somewhat alarming safety precautions that were mentioned in the system card, as well as some side-by-side tests to compare the front-end skills of these models.
Claude Opus 4 & Sonnet 4 overview
If you only remember one thing about these models, it should be that they were trained to be great at coding. The words “code” or “coding” appeared 41 times in the release announcement from Anthropic. We’ll get more into the coding performance of these models later on, for now, here’s some of the basic information and key metrics about Claude Opus 4 and Sonnet 4.
Claude Opus 4
- 200k token context window
- 32k token max output
- $15 / 1M input tokens • $75 / 1M output tokens
- Knowledge cutoff: March 2025
Anthropic noted that Opus 4 is designed to be able to work on long-horizon tasks, citing that it can work up to seven hours independently. But is Opus 4 cheaper than Opus 3? Does it have a larger context window? No to both, but it is a far superior model.
The lack of increase in context window and price is contrary to the trend of other major model releases.
Claude Sonnet 4
- 200k token context window
- 64k token max output
- $3 / 1M input tokens • $15 / 1M output tokens
- Knowledge cutoff: March 2025
Sonnet 4 also has the same context window and pricing as its predecessor Sonnet 3.7.
Both models support:
- Parallel tool execution
- Extending thinking with tool use
- Memory improvements
- Reduction in occurrences where the model uses shortcuts or loopholes to complete tasks (65% fewer occurrences than Sonnet 3.7)
New API features for building AI agents
In addition to the new models, Anthropic has introduced four new API features, all of which are aimed at making it easier for developers to build AI agents.
1. Code Execution Tool
With the new Code Execution Tool, Claude can now run Python code in a sandboxed environment. This means that rather than just generating code, Claude can generate graphs, clean up datasets, and analyze data all from a single API call.
2. MCP Connector
Previously, connecting LLMs to tools via the Model Context Protocol (MCP) was a little challenging. You needed to write custom code to handle the connection, tool discovery, and execution, error handling and more. Now, through the MCP Connector, all you need is to pass the remote server URL.
The Anthropic API handles all the connection management, tool discovery, and error handling for you. This makes it even easier to build agents that can access third-party tools.
3. Files API
The Files API, which OpenAI also offers, allows developers to upload documents once and then refer to them across conversations by just passing an ID. So rather than including the documents in messages over and over again, you can just pass the file ID and you are good to go.
This is especially helpful for workflows where you have a large set of documents (large knowledge bases, code repositories, documentation, etc).
4. Extended Prompt Caching
You can now choose between the standard 5-minute time to live (TTL) for prompt caching or choose the new, extended 1-hour TTL. The 1-hour TTL comes at an additional cost, but chances are that you’ll be saving money by leveraging cached tokens rather than being charged input token costs.
This extended window is really helpful for agents that maintain context over extended periods of time. It doesn’t unlock new functionality, but it can greatly reduce costs which makes delivering value to users at scale much more approachable.
Performance benchmarks
As with any model launch, Anthropic published head-to-head results showing how Opus 4 and Sonnet 4 stack up against prior Claude versions and other leading models.
Claude 4 benchmarks
- Opus 4 and Sonnet 4 both jump roughly +10 pts on agentic coding (SWE-bench Verified) compared to Sonnet 3.7
Software engineering accuracy
- Interestingly, Opus 4 and Sonnet 4 perform on par with OpenAI’s Codex-1—designed specifically as a code-focused model—despite being general-purpose models.
These charts highlight two things:
- Significant coding improvements in Claude 4—Opus 4 now matches or exceeds the best open-source and proprietary models on core engineering benchmarks.
- Parallel test-time compute yields an extra ~7 – 8 points on these tasks. This shows that running multiple LLM prompts in parallel can boost performance, which can be helpful when having agents handle tasks for you.
Claude 4 compared to other models
- On a more generalized benchmark, the new Anthropic models don’t crack the top 5
- Even on coding-specific benchmarks, Opus 4 and Sonnet 4 lag behind
- There’s one huge outlier on this map: Opus 4
- Sonnet 4 and Sonnet 3.7 are also still expensive, costing more than some models that score higher on intelligence benchmarks.
Front-end tests
Since Claude is notably everyone’s favorite model for front-end work, I ran batch tests in PromptHub using the same prompt (accessible here) across a few models and compared the generated websites.
Prompt Engineering best practices for Claude 4 models
Similar to OpenAI’s guidance when they launched GPT-4.1, Anthropic released some prompt engineering guidance for the Claude 4 models.
1. Be more explicit
Clearly defining what you want from Claude will help guide its responses and ensure the model delivers exactly what you're looking for.
- Less Effective: "Fix this code."
- More Effective: "Refactor this Python code to improve readability and efficiency, ensuring it adheres to PEP 8 standards."
2. Provide context
Explaining the rationale behind your instructions helps Claude better understand your request. The Claude 4 models are more literal overall.
- Less Effective: "Summarize this article."
- More Effective: "Summarize this article on climate change, highlighting the key findings and their implications for policymakers."
3. Use examples
Few-shot prompting helps Claude better understand what you want by seeing it, rather than you telling it.
- Less Effective: "Translate this text into French."
- More Effective: "Translate this English text into French. For example, 'Hello' becomes 'Bonjour', 'Goodbye' becomes 'Au revoir', and 'Please' becomes 'S'il vous plaît'."
4. Tell Claude what to do, not what not to do
OpenAI noted this as well in their prompt engineering guide for GPT-4.1. It’s safe to say this is now a best practice.
- Less Effective: "Don’t use markdown."
- More Effective: "Your output should be in paragraphs."
5. Leverage Claude’s thinking capabilities
Claude 4 is designed to use tools while reasoning, but you can guide it further by prompting it to reflect on its actions before returning the output.
- Example: “After receiving tool results, carefully reflect on their quality and determine optimal next steps before proceeding. Use your thinking to plan and iterate based on this new information, and then take the best next action.”
6. Parallel tool calling
Claude 4 is trained to call tools in parallel, but you can nudge it further to increase the likelihood and overall performance of this new (insanely powerful) capability.
- Example: “For maximum efficiency, whenever you need to perform multiple independent operations, invoke all relevant tools simultaneously rather than sequentially.”
Safety & behavior considerations
Did a whole video on the buzz around safety and autonomy considerations from the Claude 4 System Card!
The TL;DR:
If you give the model instructions to act boldly when it sees immoral behavior and give it access to tools, don't be surprised if it emails the authorities when it finds actions that are breaking the law. This is not an issue that is isolated to Claude 4 or Anthropic models!
Wrapping up
Claude 4 marks the day Anthropic went all-in on coding and developers! Really excited to continue to test these models out and leverage the new API-related features.
