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🔥Claude Sonnet 4 vs. Gemini 2.5 Pro Coding Comparison ✅

Shrijal Acharya on June 11, 2025

You'll find many AI model comparison articles that mainly test the models on a few selected questions, but not many articles show how the models re...

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Shekhar Rajput • Jun 11

I recently read it somewhere, that Antropic now claims that they don't really know how AI works nowadays. Imagine a company that big doing this claim.

But, even with that, when it comes to AI in general, I prefer Anthropic models than this cheap Google and Sam's one.

Shekhar Rajput • Jun 11

Overall, based on your observation, which one do you pick for coding? Keep pricing apart.

Shrijal Acharya • Jun 11

Claude Sonnet 4 would be it.

Shrijal Acharya • Jun 11

Gemini 2.5 is not something you can simply ignore. But, I agree Anthropic is just too goated for AI models. 💥

Shrijal Acharya • Jun 11

Folks, let me know your thoughts on this comparison. Do you prefer real-world coding tests or smaller ones?

Lara Stewart - DevOps Cloud Engineer • Jun 11

I prefer this method. This is how it should be done to test how they perform in the real world. It's a good read. 🤍

Have you had a chance to check out my recent post on starting with DevOps?

Start with DevOps in 3 simple steps 🐳

Lara Stewart - DevOps Cloud Engineer ・ Jun 7

#productivity #devops #certification #programming

Shrijal Acharya • Jun 13

Great read, @larastewart_engdev ✌️

Firuza Chess • Jun 11 • Edited

Anthropic’s Claude Sonnet 4 is a refined continuation of the Sonnet lineage—tuned for strong general capabilities, consistent coding, and nuanced reasoning—while remaining cost-effective and available on free tiers (theverge.com). It features the same “thinking summaries” and hybrid “extended thinking” mode introduced in Claude Opus 4, and Anthropic claims that it is about 65 % less prone to shortcutting and better at retaining long-term context (theverge.com). Meanwhile, Gemini 2.5 Pro represents Google DeepMind’s latest major leap, offering a mammoth 1 million‑token context window, a new “Deep Think” reasoning mode, and standout benchmark performance—especially in multi-step reasoning, math, science, and coding (tomsguide.com). Side‑by‑side user reports echo this: many note Gemini outperforms Claude on big coding tasks, thanks to its deep context and precision—but some still prefer Claude for cleaner reasoning trails or narrative flexibility (reddit.com).

In summary, Claude 4 Sonnet is a smart, reliable, and relatively lightweight companion—great for generalist use and precise reasoning—while Gemini 2.5 Pro pushes the envelope on context capacity, reasoning depth, and technical tasks, though occasionally at the cost of verbosity or over‑extension. Choosing between them depends on whether you prioritize nimble, instruction‑following consistency (Claude) or heavyweight reasoning and tool‑capable prowess (Gemini).

Shrijal Acharya • Jun 12

Thanks!

Aayush Pokharel • Jun 11

Nice one, sathi! ❤️

Shrijal Acharya • Jun 11

Thanks!

Aavash Parajuli • Jun 11

Good comparison. Always a nice read for model comparisons from you. Shrijal 💯

Shrijal Acharya • Jun 11

Thanks! 🙌

Sebastian • Jun 16

Let's be clear about what this article actually is. This isn't a "Claude Sonnet 4 vs. Gemini 2.5 Pro" comparison. It's a poorly structured and biased comparison of two completely different development tools: Anthropic's local command-line tool, "Claude Code", and Google's web-based agent, "Jules".
Because the author tests the wrapper tools instead of the models themselves in a controlled environment, the entire premise is flawed and the conclusions about which model is better are meaningless.
The comparison's credibility collapses further from there:

Broken Protocol: When the Jules agent (running Gemini) failed during the "Focus Mode" feature test, the author didn't log it as a failure. Instead, he broke his own methodology, switched to a completely different tool (Google AI Studio), and fed it context manually. This is a critical failure in testing; you cannot change the rules for one contestant mid-test.
Blatant Conflict of Interest: The final test involves building an AI agent using Composio. The author is explicitly writing "for Composio", meaning he's using this article to test and showcase his own company's product. This isn't a neutral benchmark; it's a conflict of interest masquerading as analysis.
Arbitrary Evaluation: The criteria for winning are subjective and inconsistent. A working feature from Gemini (after its tool failed) is declared a "win", while Claude's "great" code with a logic bug is treated as a lesser success. The goalposts are moved to fit whatever narrative is being pushed in that section. Criticism without a solution is just bashing, so here is a blueprint for a comparison that would actually be useful:
Establish Neutral Ground: Test the models, not their proprietary tools. Use a single, neutral IDE like VS Code with a third-party extension that can connect to both the Claude and Gemini APIs. This makes the environment and workflow identical.
Standardize Identical Tasks: The prompts and tasks must be 100% identical for both models. No asking one to perform a tool-specific function like init while the other performs a general task like writing a README.md.
Define Success Before Testing: Create a rigid, public scorecard before running any tests. This should include weighted metrics for functional correctness, adherence to all prompt constraints, and code quality. This prevents subjective, after-the-fact judging.
Ensure Transparency: If you are testing an integration with your own product, label it clearly as a "Case Study," not a neutral head-to-head comparison. An actual comparison requires rigor. This article offers none. It's a flawed, biased anecdote, not a credible analysis.