Gemini 2.5 Pro vs Claude 3.7 Sonnet: Which is Better for Coding Tasks?

Coding is among the top uses of LLMs as per a Harvard 2025 report. Engineers and developers around the world are now using AI to debug their code, test it, validate it, or write scripts for it. In fact, with the way current LLMs are performing at generating code, soon they will be almost like a pair programmer for anyone who wishes to solve their coding problems. So far, Claude 3.7 Sonnet has held the title of being the best coding LLM to date. But recently, Google gave an update to their latest Gemini 2.5 Pro, and if benchmarks are to be believed, it beats Claude! So in this blog, we will put this claim to test. We will give same prompts to Gemini 2.5 Pro and Claude 3.7 Sonnet on various code-related tasks to see which LLM is the coding king.  

Gemini 2.5 Pro vs Claude 3.7 Sonnet

Before we start with our model experimentation, let’s have a quick revision of these models. 

What is Gemini 2.5 Pro?

Gemini 2.5 Pro is the long-context reasoner that DeepMind calls its premier multimodal AI model, being one under the Gemini 1.5 family, and fine-tuned to perform highly in text, code, and vision tasks. This model can reason over any kind of text of up to one million tokens in its context window: whole books, huge documents, or very long conversations precision and coherence. All of this makes it extremely useful for applications in the enterprise, scientific research, and mass content generation. 

What truly sets Gemini 2.5 Pro apart is its native multimodality: it is the only other model that can understand and reason across different data types fairly easily-interpreting images, text, and soon, audio. It powers sophisticated features in Workspace and Gemini apps and developer tools through the Gemini API, with tight integration into the Google ecosystem.

What is Claude 3.7 Sonnet?

The newest mid-tier model in the Claude 3 family is Claude 3.7 Sonnet, intermediating between the smaller Haiku and flagship Opus models. Being “mid-tier” in nature, Claude 3.7 Sonnet attains or sometimes exceeds the performance of GPT-4 in some benchmarks like structured reasoning, coding assistance, and business analysis. It is very responsive and cheap, well-suited for developers and businesses who want advanced AI capabilities without the cost of top-end models.

A big selling point for Claude 3.7 Sonnet is the emphasis on ethical alignment and reliability that can be traced back to the Constitutional AI principles of Anthropic. Multimedia input support (text + image), long documents handling, summarization, Q&A, and ideation are all areas where it shines. Regardless of whether it is accessed via Claude.ai, the Claude API, or embedded into enterprise workflows, Sonnet 3.7 offers a nice trade-off between performance, safety, and speed, making it perfect for teams that need trustworthy AI at scale.

Gemini 2.5 Pro vs Claude 3.7 Sonnet: Benchmark Comparison

Gemini 2.5 Pro, regarded with general knowledge and mathematical reasoning benchmarks, while the Claude 3.7 Sonnet is a consistent victor when coding specific benchmarks come into the picture. Claude also scores well on measures of truthfulness, thus implying that Anthropic genuinely puts effort into lessening hallucinations.

Benchmark	Winner
MMLU (general knowledge)	Gemini 2.5 Pro
HumanEval (Python coding)	Claude 3.7 Sonnet
GSM8K (math reasoning)	Gemini 2.5 Pro
MBPP (programming problems)	Claude 3.7 Sonnet
TruthfulQA	Claude 3.7 Sonnet

For context handling, Gemini’s huge one-million token window coupled with its Google ecosystem, is an advantage when dealing with extremely large codebases, while Claude tends to respond faster with normal coding tasks.

Gemini 2.5 Pro vs Claude 3.7 Sonnet: Hands-On Comparison

Task 1: JavaScript Endless Runner game

Prompt: “Create a pixel-art endless runner in p5.js where a robotic cat dashes through a neon cyberpunk cityscape, dodging drones and jumping over broken circuits. I want to run this locally.”

Gemini 2.5 Pro Output

Claude 3.7 Sonnet Output

Response Review:

Result: Gemini 2.5 Pro: 0 | Claude 3.7 Sonnet: 1

Task 2: Procedural Dungeon Generator in Pygame

Prompt: “Build a basic procedural dungeon generator in Python using pygame. The dungeon should consist of randomly placed rooms and corridors, and the player (a pixel hero) should be able to move from room to room. Include basic collision with walls.”

Gemini 2.5 Pro Output:

Claude 3.7 Sonnet Output:

Response Review:

Result: Gemini 2.5 Pro: 1 | Claude 3.7 Sonnet: 1

Task 3: Wildcard Pattern Matching Coding Problem

Prompt: “Give the solution to this problem in C++. Given an input string (s) and a pattern (p), implement wildcard pattern matching with support for “?’ and” where:

Give the solution to this problem in C++. Given an input string (s) and a pattern (p), implement wildcard pattern matching with support for “?’ and” where:
– ‘?’ Matches any single character.
– ” Matches any sequence of characters (including the empty sequence).
– The matching should cover the entire input string (not partial).
Example 1:
Input: s = “aa”, p = “a”
Output: false
Explanation: “a” does not match the entire string “aa”.
Example 2:
Input: s = “aa”, p = “*
Output: true
Explanation: ” matches any sequence.
Example 3:
Input: s = “cb”, p = “?a”
Output: false
Explanation: ‘?’ matches ‘c’, but the second letter is ‘a’, which does not match ‘b’.
Constraints:
0 <= s.length, p.length <= 2000
s contains only lowercase English letters.
p contains only lowercase English letters, ‘?’ or **.“

Gemini 2.5 Pro Output:

Claude 3.7 Sonnet Output:

Response Review:

Gemini 2.5 Pro	Claude 3.7 Sonnet
Gemini 2.5 Pro shows its ability to excel in handling edge cases here. Its logic is clearer with better handling of wildcards, and it provides clarity in variable names as well. It proves to be more reliable as compared to Claude 3.7 Sonnet. It is suitable for real-world applications.	Claude 3.7 Sonnet uses dynamic programming for pattern matching, but it struggles with complex patterns like multiple ‘*’ wildcards which causes errors in some cases like ‘mississippi’.

Result: Gemini 2.5 Pro: 1 | Claude 3.7 Sonnet: 0

Task 4: Shooter Game using Pygame

Prompt: “I need you to program a retro-style 2D side-scroller shooter game in Python using Pygame. The player would assume control of a spaceship whose lasers destroy incoming alien ships. Score tracking would be implemented, as well as some basic explosion animations.”

Gemini 2.5 Pro Output:

Claude 3.7 Sonnet Output:

Response Review:

Gemini 2.5 Pro	Claude 3.7 Sonnet
It was presented as a minimal but functional implementation. The spaceship would move and shoot, yet alien collision detection was buggy. Scores are inconsistently updated. No explosion effects were added.	This would prove to be a fully functioning and polished game, with smooth movement, intuitive laser collisions, and score tracking, augmented with satisfying explosion animations. Controls felt smooth and visually appealing.

Result: Gemini 2.5 Pro: 0 | Claude 3.7 Sonnet: 1

Task 5: Data Visualisation Application

Prompt: “Create an interactive data visualization application in Python with Streamlit that loads CSVs of global CO₂ emissions, plots line charts by country, allows users to filter on year range, and plots the top emitters in a bar chart.”

Gemini 2.5 Pro Output:

Claude 3.7 Sonnet Output:

Response Review:

Gemini 2.5 Pro	Claude 3.7 Sonnet
Creating a clean interactive dashboard with filtering and charts. Charts are labeled well; Streamlit components, e.g., sliders and dropdowns, worked great together.	Claude 3.7 Sonnet also delivered the dashboard that worked, but was lacking interactivity in filtering. The bar chart remained static, and some charts were missing legends.

Result: Gemini 2.5 Pro: 1 | Claude 3.7 Sonnet: 0

Comparison Summary

Task	Winner
JavaScript endless runner game	Claude 3.7 Sonnet
Procedural Dungeon Generator Pygame	Both
Wildcard pattern matching coding problem	Gemini 2.5 Pro
Shooter game using Pygame	Claude 3.7 Sonnet
Data Visualisation Dashboard Application	Gemini 2.5 Pro

Gemini 2.5 Pro vs Claude 3.7 Sonnet: Choose the Best Model

After experimenting and testing both models on different coding tasks, the “Best” choice depends on your specific needs.

You can choose Gemini 2.5 Pro when:

• You require the one-million token context window
• You’re integrating with Google Products
• Working with algorithms and Data visualization

You can choose Claude 3.7 Sonnet when:

• Your top priority is code reliability
• Development of games or interactive applications is needed
• The efficiency of the API cost is of greater importance

Both models justify their subscription pricing of $20 per month for professional developers. Losing time to debug, generate code, or just solve problems will wipe out any revenue earned. Whenever I need to code for the day, I tend to go with Claude 3.7 Sonnet because it generates interactive applications code better but when it comes to big datasets or documentation, Gemini’s context window might be the best for me.

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Conclusion

The task comparison between Gemini 2.5 Pro and Claude 3.7 Sonnet revealed that there’s no clear overall winner, resulting in a tie between them as each model has distinct strengths and weaknesses for different coding tasks. While these models continue to evolve, they are becoming a must-have for every developer, not to replace human programmers but rather to multiply their productivity and capabilities manyfold. This decision between Gemini 2.5 Pro and Claude 3.7 Sonnet should be dictated only by what your project requires, not by what is considered “better”.

Let me know your thoughts in the comment section below.