The AI industry is divided between two powerful philosophies – Open-source democratization and proprietary innovation. OLMo 2(Open Language Model 2), developed by AllenAI, represents the pinnacle of transparent AI development with full public access to its architecture and training data. In contrast, Claude 3.5 Sonnet, Anthropic’s flagship model, prioritizes commercial-grade coding capabilities and multimodal reasoning behind closed doors.
This article dives into their technical architectures, use cases, and practical workflows, complete with code examples and dataset references. Whether you’re building a startup chatbot or scaling enterprise solutions, this guide will help you make an informed choice.
In this article, you will:
This article was published as a part of theData Science Blogathon.
OLMo 2 is an entirely open-source autoregressive language model, trained on an enormous dataset comprising 5 trillion tokens. It is released with full disclosure of its weights, training data, and source code empowering researchers and developers to reproduce results, experiment with the training process, and build upon its innovative architecture.
OLMo 2 incorporates several key architectural modifications designed to enhance both performance and training stability.
Try OLMo 2 model live – here
These architectural and training strategies combine to create a model that is not only high-performing but also robust and adaptable which is a true asset for academic research and practical applications alike.
In contrast to the open philosophy of OLMo2, Claude3.5 Sonnet is a closed‑source model optimized for specialized tasks, particularly in coding and ensuring ethically sound outputs. Its design reflects a careful balance between performance and responsible deployment.
By focusing on coding applications and ensuring ethical reliability, Claude3.5 Sonnet addresses niche requirements in industries that demand both technical precision and moral accountability.
Try Claude3.5 Sonnet model live-here.
|
OLMo 2 | Claude 3.5Sonnet | |||||||||||||||
| Model Access | Full weights available on Hugging Face | API-only access | |||||||||||||||
| Fine-Tuning | Customizable via PyTorch | Limited to prompt engineering | |||||||||||||||
| Inference Speed | 12 tokens/sec (A100 GPU) | 30 tokens/sec (API) | |||||||||||||||
| Cost | Free (self-hosted) | $15/million tokens |
| Price type | OLMo 2 (Cost per million tokens) | Claude 3.5 Sonnet(Cost per million tokens) |
|---|---|---|
| Input tokens | Free* (compute costs vary) | .00 |
| Output tokens | Free* (compute costs vary) | .00 |
OLMo2 is approximately four times more cost-effective for output-heavy tasks, making it ideal for budget-conscious projects. Note that since OLMo2 is an open‑source model, there is no fixed per‑token licensing fee, its cost depends on your self‑hosting compute resources. In contrast, Anthropic’s API rates set Claude 3.5 Sonnet’s pricing.
Visit the official Ollama repository or website to download the installer –here.
Once you have Ollama, install the necessary Python package
pip install ollama
Download the Olmo 2 Model.This command fetches the Olmo 2 model (7-billion-parameter version)
ollama run olmo2:7b
Create a Python file and execute the following sample code to interact with the model and retrieve its responses.
import ollama
def generate_with_olmo(prompt, n_predict=1000):
"""
Generate text using Ollama's Olmo 2 model (streaming version),
controlling the number of tokens with n_predict.
"""
full_text = []
try:
for chunk in ollama.generate(
model='olmo2:7b',
prompt=prompt,
options={"n_predict": n_predict},
stream=True
):
full_text.append(chunk["response"])
return "".join(full_text)
except Exception as e:
return f"Error with Ollama API: {str(e)}"
if __name__ == "__main__":
output = generate_with_olmo("Explain the concept of quantum computing in simple terms.")
print("Olmo 2 Response:", output)
Head over to the Anthropic console page. Select Get API keys.
Click on Create Key and name your key. Click on Add.
Note: Don’t forget to save that API key somewhere you won’t be able to see it again.
Install the Anthropic Library
pip install anthropic
Create a Python file and execute the following sample code to interact with the model and retrieve its responses.
import anthropic
from anthropic import Anthropic
# Create an instance of the Anthropic API client
client = Anthropic(api_key='your-api-key')
def generate_with_claude(prompt, max_tokens=1000):
"""
Generate text using Claude 3.5 API
"""
try:
message = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=max_tokens,
messages=[
{
"role": "user",
"content": prompt
}
]
)
return message.content
except Exception as e:
return f"Error with Claude API: {str(e)}"
if __name__ == "__main__":
output = generate_with_claude("Explain the concept of quantum computing in simple terms.")
print("Claude 3.5 Sonnet Response:", output)
UsingOlmo2 and Claude 3.5 Sonnetmodels for the below tasks.
Prompt: “Give me code to Compute the nth Fibonacci Number.”
def fibonacci_optimized(n):
if n <= 0:
return "Input must be a positive integer."
fib_0, fib_1 = 0, 1
for i in range(2, n+1):
fib_i = fib_0 + fib_1
fib_0, fib_1 = fib_1, fib_i
return fib_i
# Example usage:
print(fibonacci_optimized(10)) # Output: 55def fibonacci_recursive(n):
"""
Compute nth Fibonacci number using recursion.
Warning: Not efficient for large numbers due to exponential time complexity.
Time Complexity: O(2^n)
Space Complexity: O(n) due to recursion stack
"""
if n <= 0:
return 0
if n == 1:
return 1
return fibonacci_recursive(n - 1) + fibonacci_recursive(n - 2)
def fibonacci_iterative(n):
"""
Compute nth Fibonacci number using iteration.
Much more efficient than recursive approach.
Time Complexity: O(n)
Space Complexity: O(1)
"""
if n <= 0:
return 0
if n == 1:
return 1
prev, curr = 0, 1
for _ in range(2, n + 1):
prev, curr = curr, prev + curr
return curr
def fibonacci_matrix(n):
"""
Compute nth Fibonacci number using matrix exponentiation.
Most efficient for very large numbers.
Time Complexity: O(log n)
Space Complexity: O(1)
"""
if n <= 0:
return 0
if n == 1:
return 1
def matrix_multiply(a, b):
return [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]]
]
def matrix_power(matrix, power):
if power == 0:
return [[1, 0], [0, 1]]
if power == 1:
return matrix
# Recursive squaring for efficient computation
half = matrix_power(matrix, power // 2)
if power % 2 == 0:
return matrix_multiply(half, half)
else:
return matrix_multiply(matrix_multiply(half, half), matrix)
base_matrix = [[1, 1], [1, 0]]
result_matrix = matrix_power(base_matrix, n - 1)
return result_matrix[0][0]
# Example usage and testing
def test_fibonacci():
test_cases = [0, 1, 2, 5, 10]
for n in test_cases:
result1 = fibonacci_recursive(n)
result2 = fibonacci_iterative(n)
result3 = fibonacci_matrix(n)
print(f"n = {n}:")
print(f" Recursive: {result1}")
print(f" Iterative: {result2}")
print(f" Matrix: {result3}")
print(f" All methods match: {result1 == result2 == result3}\n")
if __name__ == "__main__":
test_fibonacci()OLMo 2 provides an iterative approach that is efficient but lacks flexibility, offering only one method. Claude Sonnet 3.5, on the other hand, presents three different implementations: recursive (inefficient but educational), iterative (optimal for general use), and matrix exponentiation (best for large inputs). Claude’s response is significantly more comprehensive, covering multiple use cases and including a test suite to verify correctness.
“Prompt: Generate a Python script that uses Matplotlib and Seaborn to produce a vibrant scatter plot showing the relationship between two variables. The plot should include clear axis labels, a descriptive title, and distinct colors to differentiate the data points.“
You can find the code responses–here.
OLMo 2’s response correctly generates a scatter plot but lacks visual enhancements beyond basic differentiation of groups. Claude Sonnet 3.5 goes further by integrating size variation, a regression trend line, and correlation annotation, resulting in a more informative and visually appealing plot. Claude’s response demonstrates a better grasp of advanced visualization techniques and statistical insights.
Prompt: “Convert this Java method into Python code while maintaining equivalent functionality:
pip install ollama
ollama run olmo2:7b
import ollama
def generate_with_olmo(prompt, n_predict=1000):
"""
Generate text using Ollama's Olmo 2 model (streaming version),
controlling the number of tokens with n_predict.
"""
full_text = []
try:
for chunk in ollama.generate(
model='olmo2:7b',
prompt=prompt,
options={"n_predict": n_predict},
stream=True
):
full_text.append(chunk["response"])
return "".join(full_text)
except Exception as e:
return f"Error with Ollama API: {str(e)}"
if __name__ == "__main__":
output = generate_with_olmo("Explain the concept of quantum computing in simple terms.")
print("Olmo 2 Response:", output)Both OLMo 2 and Claude Sonnet 3.5 provide identical solutions, accurately translating the Java method to Python. Since the function is straightforward, there is no room for differentiation, making both responses equally effective.
Prompt: “Optimize the following Python function to reduce time complexity.
pip install anthropic
import anthropic
from anthropic import Anthropic
# Create an instance of the Anthropic API client
client = Anthropic(api_key='your-api-key')
def generate_with_claude(prompt, max_tokens=1000):
"""
Generate text using Claude 3.5 API
"""
try:
message = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=max_tokens,
messages=[
{
"role": "user",
"content": prompt
}
]
)
return message.content
except Exception as e:
return f"Error with Claude API: {str(e)}"
if __name__ == "__main__":
output = generate_with_claude("Explain the concept of quantum computing in simple terms.")
print("Claude 3.5 Sonnet Response:", output)def fibonacci_optimized(n):
if n <= 0:
return "Input must be a positive integer."
fib_0, fib_1 = 0, 1
for i in range(2, n+1):
fib_i = fib_0 + fib_1
fib_0, fib_1 = fib_1, fib_i
return fib_i
# Example usage:
print(fibonacci_optimized(10)) # Output: 55OLMo 2 improves the function by using a set to track seen elements but retains a list for storing duplicates, leading to potential redundancy. Claude Sonnet 3.5 optimizes further by storing duplicates in a set and converting it back to a list at the end, improving efficiency and avoiding unnecessary operations. Claude’s approach is cleaner, ensuring better time complexity while maintaining correctness.
Prompt: “Below is a Python script that calculates the factorial of a number, but it contains bugs. Identify and correct the errors to ensure it returns the correct factorial for any positive integer:
pip install ollama
ollama run olmo2:7b
import ollama
def generate_with_olmo(prompt, n_predict=1000):
"""
Generate text using Ollama's Olmo 2 model (streaming version),
controlling the number of tokens with n_predict.
"""
full_text = []
try:
for chunk in ollama.generate(
model='olmo2:7b',
prompt=prompt,
options={"n_predict": n_predict},
stream=True
):
full_text.append(chunk["response"])
return "".join(full_text)
except Exception as e:
return f"Error with Ollama API: {str(e)}"
if __name__ == "__main__":
output = generate_with_olmo("Explain the concept of quantum computing in simple terms.")
print("Olmo 2 Response:", output)OLMo 2 correctly fixes the factorial function’s recursion step but lacks input validation. Claude Sonnet 3.5 not only corrects the recursion but also includes input validation to handle negative numbers and non-integer inputs, making it more robust. Claude’s solution is more thorough and suitable for real-world applications.
OLMo 2 democratizes advanced NLP through full transparency and cost efficiency (ideal for academic research and budget-conscious prototyping), Claude 3.5 Sonnet delivers enterprise-grade precision with multimodal coding prowess and ethical safeguards. The choice isn’t binary, forward-thinking organizations will strategically deploy OLMo 2 for transparent, customizable workflows and reserve Claude 3.5 Sonnet for mission-critical coding tasks requiring constitutional alignment. As AI matures, this symbiotic relationship between open-source foundations and commercial polish will define the next era of intelligent systems. I hope you found this OLMo 2 vs. Claude 3.5 Sonnet guide helpful, let me know in the comment section below.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
Ans. In narrow domains (e.g., legal documents), yes. For general-purpose tasks, Claude’s 140B parameters retain an edge.
Q2. How do the models handle non-English languages?Ans. Claude 3.5 Sonnet supports 50 languages natively. OLMo 2 focuses primarily on English but can be fine-tuned for multilingual tasks.
Q3. Is OLMo 2 available commercially?Ans. Yes, via Hugging Face and AWS Bedrock.
Q4. Which model is better for startups?Ans. OLMo 2 for cost-sensitive projects; Claude 3.5 Sonnet for coding-heavy tasks.
Q5. Which model is better for AI safety research?Ans. OLMo 2’s full transparency makes it superior for safety auditing and mechanistic interpretability work.
The above is the detailed content of OLMo 2 vs. Claude 3.5 Sonnet: Which is Better?. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
