Tutorial: Coding with Claude Opus 4.1

Table of contents

Key improvements over Claude Opus 4



Advanced Coding Skills: Claude Opus 4.1 shows a marked improvement in real-world coding tasks. It’s better at handling multi-file codebases and can refactor or update code spanning multiple modules in one session. This is possible thanks to enhanced memory and context handling, which allow the model to maintain awareness of “key contextual information” across a larger codebase for coherent edits.
Longer Autonomous Reasoning: The model exhibits stronger agentic abilities, meaning it can operate more autonomously and for longer durations without intervention. For example, Claude Opus 4.1 can run through a coding project for hours, planning steps and adjusting as needed. (Claude Opus 4 was demonstrated to work for up to 7 hours continuously during a code refactoring task, and 4.1 continues to push this boundary.) This makes it ideal for complex tasks like iterative debugging or lengthy code generation sessions that require sustained attention.
Improved Reasoning & Chain-of-Thought: Claude Opus 4.1 has upgrades in reasoning algorithms that help it solve problems more systematically. It’s better at logical reasoning in coding, for instance, understanding algorithmic complexity or tracking variable state, which leads to more accurate and efficient code output. The model also provides clearer explanations when asked (offering summarized reasoning rather than dumping an entire chain-of-thought), which helps in understanding its solutions. This can reduce the time you spend deciphering the model’s intent and speed up the development cycle.
Enhanced Safety and Guidance: Alongside performance gains, Claude Opus 4.1 comes with refined safety measures (as detailed in its system card). It’s designed to avoid insecure coding suggestions and can adhere to guidelines or coding standards you provide. For example, if you ask it to write a function with certain constraints or style, it’s more likely to follow those instructions precisely. This improvement reduces the need for manual corrections and makes the model a more reliable coding assistant.

Claude Opus 4.1 performance benchmarks and evaluations



Claude 4.1 AI working in tandem with W&B Weave for improved coding tasks and performance monitoring.

Step 1: Install and import the required libraries

pip install anthropic weave 

Step 2: Compose the prompt for Claude Opus 4.1

prompt = (
    "You are an expert Python programmer. "
    "Please write a Python function named factorial(n) that returns the factorial of n. "
    "Only provide the code for the function, without any additional explanation."
)

Step 3: Call Claude 4.1 to generate the code

import os
import weave; weave.init("claude-opus-4-1-tutorial")
from anthropic import Anthropic


# Step 4: Compose prompt
prompt = (
    "You are an expert Python programmer. "
    "Please write a Python function named factorial(n) that returns the factorial of n. "
    "Only provide the code for the function, without any additional explanation."
)

# Step 5: Call Claude 4.1 to generate the code
client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))

response = client.messages.create(
    model="claude-opus-4-1-20250805",
    max_tokens=300,
    messages=[{"role": "user", "content": prompt}]
)

generated_code = response.content
print("Claude Opus 4.1's output:\n", generated_code)

# Step 6: Extract, clean, save, and execute the generated code
code_text = generated_code[0].text if isinstance(generated_code, list) else generated_code
code_str = code_text.strip().strip("```python").strip("```")

with open("generated_factorial.py", "w") as f:
    f.write(code_str)

import generated_factorial

test_value = 5
expected = 120
result = generated_factorial.factorial(test_value)
print(f"factorial({test_value}) = {result}")


We stripped the code string of any markdown ``` markers just in case. In our expected output above, Claude didn’t include them, but this step makes our solution robust to such cases.
We wrote the cleaned code into generated_factorial.py. If Claude provided a complete function definition, that file now contains a def factorial(n): ... ready to use.
We import the new module generated_factorial. This executes the code in the file, defining the factorial function within that module’s namespace.
We then call generated_factorial.factorial(5) to compute 5!. We also set an expected value (120) to verify correctness.
We print the result in a clear format to see if it matches our expectation.

factorial(5) = 120



📝 Challenge: Try extending this workflow to a slightly more complex task. For example, ask Claude Opus 4.1 to write a function for a different algorithm (sorting a list, checking for prime numbers, etc.), or even generate two related functions (like an implementation and a helper function). Log multiple test cases to W&B. Then check the W&B dashboard to ensure each case passes. This will give you practice with iterating prompts and observing results. You can also attempt to intentionally introduce a bug (or catch one the model makes) and use W&B logs to pinpoint the issue and fix it in a follow-up prompt.

Observability with W&B Weave

Detailed Trace of Interactions: W&B Weave automatically records each prompt and response we logged. In the W&B run page, you’ll find a trace table showing the inputs and outputs. For example, you’ll see a row with the user prompt (“You are an expert Python programmer...”) and Claude’s answer (the code for factorial(n)). If your workflow had multiple steps (imagine an agent that asks Claude multiple questions or a chain of prompts), each would appear in sequence. Weave’s interface lets you click on each step to inspect full contents easily. This is immensely helpful for multi-step debugging – you can pinpoint which prompt led to an incorrect or unexpected output.
Logging Metadata and Metrics: In our simple run, we logged the test input and output. In larger projects, you might log more metrics: execution time, number of tokens used, memory usage, etc. W&B Weave can plot these over time or across runs. For instance, if you logged how long Claude Opus 4.1 took to generate a response, you could see if a more complex prompt causes a slowdown. If you log the token count of each response (Anthropic’s API may return usage info in the response object), you can monitor Claude’s consumption to manage costs. All this info appears in Weave’s dashboard where you can create custom visualizations. It’s like having an analytics panel for your AI’s performance.
Compare Model Versions and Prompts: Weave makes it easy to compare different runs. Suppose next week you try the same factorial prompt with a different model (maybe Claude’s smaller Sonnet 4 model, or GPT-4) and log that to W&B. You can use Weave to line up the runs side by side. This might show differences like Claude Opus 4.1 took fewer iterations to refine an answer, or provided a more optimal solution than another model. By comparing outputs in one place, you gain insights into each model’s strengths. You can also compare different prompt styles: for example, one with minimal instruction vs one where you explicitly ask for big-O analysis in comments – see how the output differs, all within the W&B interface.
Debugging with Rich Data: One of the powerful observability features is catching failure modes. If Claude makes a mistake (perhaps in a more complex scenario like generating code across multiple files or an API call), W&B Weave helps you identify it. Because we logged expected_output vs model_output, in our factorial example it was easy to see they matched. In a scenario where they don’t, you could log a flag indicating “test_passed = False”. Weave could then let you filter or highlight runs where tests failed. Imagine generating dozens of functions via an automated script and logging each one’s test results – you could quickly filter to just the failures and examine what went wrong, with full context of the prompt and code given. This beats manually sifting through console logs.
Team Collaboration and Reports: Since your W&B runs are saved to the cloud, you can share the Weave dashboard link with colleagues. They can view the prompts and outputs, comment on them, or even build a report (like a summary document) directly including parts of your runs. This is great for code review of AI-generated code. For example, a teammate might open the run, see the generated_code we logged, and review it as if it were a code snippet. You can discuss improvements or issues, and then refine the prompt accordingly. W&B Weave basically provides a shared space to analyze the AI’s contributions to your codebase.

Conclusion