This year has been full of exciting releases in the GenAI space, from Claude Sonnet 3.5 to OpenAI’s o1 to Meta’s Llama 3.3 and many more. With so much happening, it’s quite difficult to pick a model that doesn’t have a better alternative. The recent launch of DeepSeek V3 has shaken the GenAI world with its impressive capabilities, and it’s said to be the best open-source model available today. So, I decided to put it to the test against Claude 3.5 Sonnet. In this article, I’ll test these models with the same prompts to see which provides a better response! So, lets begin our DeepSeek V3 vs Claude Sonnet 3.5 battle!
Prompt: You are in a rush to work. You pour yourself a cup of black coffee, but it is too hot. You intend to add a fixed amount of cold milk to it, but you know that even after that, the coffee will need to cool down for a few minutes before you can drink it.
In which case does the coffee cool down more:
1) Add milk right away, then wait a few minutes before drinking.
2) Wait a few minutes, then add milk just before drinking.
DeepSeek V3:
Claude Sonnet 3.5:
Observation:
DeepSeek V3 suggests adding cold milk right away, arguing that the coffee’s temperature drops immediately and then continues to cool. By contrast, Claude Sonnet 3.5 says it’s better to let the black coffee cool first—because a hotter liquid loses heat faster—then add milk right before drinking for an extra temperature drop. Most physics-based explanations favor Claude Sonnet 3.5. When coffee remains hotter initially, it loses more heat through faster cooling due to the larger temperature difference with the surroundings. Adding milk at the end then provides a final cool-down, resulting in a lower overall temperature at drinking time.
Verdict:
DeepSeek V3 ❌ | Claude Sonnet 3.5 ✅
Pormpt: Create a logical flow chart for multi-agent design pattern. It involves the following steps:
1. Break the Task: Define the overall goal and split it into smaller subtasks, identifying how they depend on each other.
2. Assign Roles: Assign each subtask to the best-suited agent, outlining responsibilities and deliverables. 3. Plan Communication: Decide if agents communicate directly or through a supervisor, using consistent formats.
4. Execute: Run agents in parallel or sequence, tracking their outputs for completeness and accuracy.
5. Validate & Integrate: Combine all agent results into one final answer, checking for errors or inconsistencies.
6. Refine: Gather feedback to improve roles or processes and update instructions or workflows as needed.
DeepSeek V3 Output:
+-----------------------------------------+
| Start |
+-----------------------------------------+
|
v
+-----------------------------------------+
| 1. Break the Task |
| - Define the overall goal |
| - Split the goal into smaller subtasks |
| - Identify dependencies between subtasks|
+-----------------------------------------+
|
v
+-----------------------------------------+
| 2. Assign Roles |
| - Assign each subtask to the best-suited|
| agent |
| - Outline responsibilities & deliverables|
+-----------------------------------------+
|
v
+-----------------------------------------+
| 3. Plan Communication |
| - Decide if agents communicate directly |
| or through a supervisor |
| - Use consistent communication formats |
+-----------------------------------------+
|
v
+-----------------------------------------+
| 4. Execute |
| - Run agents in parallel or sequence |
| - Track outputs for completeness & |
| accuracy |
+-----------------------------------------+
|
v
+-----------------------------------------+
| 5. Validate & Integrate |
| - Combine all agent results into one |
| final answer |
| - Check for errors or inconsistencies |
+-----------------------------------------+
|
v
+-----------------------------------------+
| 6. Refine |
| - Gather feedback to improve roles or |
| processes |
| - Update instructions or workflows as |
| needed |
+-----------------------------------------+
|
v
+-----------------------------------------+
| End |
+-----------------------------------------+
Claude Sonnet 3.5 Output:
Observation:
Claude Sonnet 3.5 is better because it shows decision points and feedback loops. Multi-agent systems often need re-checking and refining steps. If you only need a simple list, DeepSeek V3 might suffice. For real workflows requiring iterative improvements and branching, Claude Sonnet 3.5 provides a clearer, more complete structure.
Verdict:
DeepSeek V3 ❌ | Claude Sonnet 3.5 ✅
Prompt: Analyze the content below and list down the number of grammatical mistakes in the text.
Despite the ephemeral nature of existance, many remain obdurate in their illusions. Indubitably, ephemeral shifts in cosmic nuance underscores the labyrinthine complexity confronting even the most erudite minds. Untill we coalesce our collective energies to surmount these onerous intricacies, humanity shall remain enthralled by perplexities beyond measure. Yet, are we truly predestinated to remain mired in existential conflagrations. As an anthromorphic species, perhaps our greatest folly is hubristic complacence, manifesting when clarity beckons. The onus rests upon introspection, for recognition of intangible constraints fosters liberation. The presipice of knowledge entreats us onward, forging an uncertain but indispensable path. Take heed.
DeepSeek V3 Output:
Here’s a list of errors in the paragraph:
List of Errors:
[“existance”, “underscores”, “Untill”, “predestinated”, “anthromorphic”, “complacence”, “presipice”]Claude Sonnet 3.5 Output:
Here are all the errors found in the paragraph, listed in order of appearance:
The paragraph contains 8 distinct errors, primarily consisting of spelling mistakes, subject-verb agreement issues, and punctuation errors.
Observation:
Claude Sonnet 3.5’s response is better because it catches more issues. It not only points out spelling and subject–verb mistakes, like DeepSeek V3 does, but also notes a missing question mark and suggests a stylistic improvement for “Take heed.” That broader coverage makes it a more thorough analysis overall.
Verdict:
DeepSeek V3 ❌ | Claude Sonnet 3.5 ✅
Prompt: What is the win probability for each team in this game?
DeepSeek V3 Output:
Claude Sonnet 3.5 Output:
Observation:
Claude Sonnet 3.5 provides a more accurate, contextually consistent reading of the scoreboard and run chase requirements, leading to a more realistic probability estimate. It factors in overs remaining and the required run rate. DeepSeek V3 misinterprets the match situation, so its conclusion is less reliable. Consequently, Claude Sonnet 3.5’s response is better because it aligns with the correct match details and standard cricket chase logic.
Verdict:
DeepSeek V3 ❌ | Claude Sonnet 3.5 ✅
Prompt: A weightless string connects two identical metal balls. The middle of the string is over the edge of a table. You let go of both balls at the same time. What will happen sooner: ball 1 flying off the table or ball 2 hitting its side?
DeepSeek V3 Output:
Understand the Setup
Forces Acting on the Balls
Which Happens Sooner?
Claude Sonnet 3.5 Output:
Initial Setup
When Balls are Released
The Physics
The Result
Obervation:
Claude Sonnet 3.5 is more correct. Under ideal (frictionless) conditions, each ball travels the same horizontal distance with the same horizontal acceleration, so Ball 1 flies off exactly when Ball 2 hits the side. DeepSeek V3 incorrectly prioritizes the downward pull of gravity on Ball 2, but the key factor is the horizontal pull from the string, which is symmetrical for both balls. Consequently, they reach the table’s edge/side simultaneously in a pure-physics scenario.
Verdict:
DeepSeek V3 ❌ | Claude Sonnet 3.5 ✅
Task | Winner |
---|---|
Task 1: Solve a Puzzle | Claude Sonnet 3.5 |
Task 2: Create a Flow Chart | Claude Sonnet 3.5 |
Task 3: Find Grammar Mistakes | Claude Sonnet 3.5 |
Task 4: Calculate Winning Probability | Claude Sonnet 3.5 |
Task 5: Physics Problem | Claude Sonnet 3.5 |
Also Read:
The tasks in this article provide a glimpse into the capabilities of DeepSeek V3 vs Claude 3.5 Sonnet, but they are only a small part of what these models can do. Avoid judging them solely based on these results. Instead, explore and use each model according to your specific needs and requirements.
Have you tried DeepSeek V3 or Claude 3.5 Sonnet? Share your experiences and insights in the comments below!