The Developer's LLM Toolbox: Comparing Claude, DeepSeek, Gemini, and More for Coding

In the current landscape of AI-powered development, the consensus is clear: there is no one-size-fits-all solution for coding assistance. Instead, developers are curating a sophisticated toolbox of Large Language Models (LLMs), deploying different AIs for the specific tasks at which they excel. This strategic approach moves beyond relying on a single default model and focuses on maximizing efficiency and code quality.

The Multi-Model Toolbox

A common theme is the use of a primary model supplemented by one or two others as fallbacks or for specialized jobs. This workflow allows developers to overcome the unique weaknesses of any single model.

Here’s a breakdown of how different models are being utilized:

DeepSeek: Frequently praised for its ability to generate novel solutions and quickly churn out prototypes. Users highlight the importance of enabling its "thinking & reasoning & search" modes to unlock its full potential.
Claude (Code, Opus, Sonnet): The Claude family of models is a popular choice for various tasks. Claude Code is often used within editors like Cursor for generating project overviews, writing READMEs, and performing quick, simple code modifications. For more complex, full-feature implementations, developers turn to Claude Opus 4, but note that it performs best when given a massive amount of context, almost like a detailed specification. Claude 4 Sonnet is singled out for its superior ability to use tools and directly modify lines of code.
QWEN Coder: This model is noted as being similar to DeepSeek in capability but with a distinct advantage when working with visual data or image datasets.
GPT Series (4.1, 5): Still considered a powerful and reliable all-arounder. It's often the default choice when a task doesn't clearly align with the specialized strengths of other models.
Google Gemini (2.5 Pro): While some find its output verbose, Gemini is rapidly improving and has found its niche as a powerful "second opinion" or a fallback for difficult problems that other models fail to solve. One user theorizes its BERT-based architecture makes it particularly effective for tasks requiring back-and-forth context referencing.

Open-Source and Local Setups

Beyond the major commercial offerings, developers are also experimenting with powerful local and open-source combinations:

Devstral + Openhands: Mentioned as a dependable "workhorse" combination.
Qwen3 Coder (30b): A powerful local model that developers are fine-tuning for their workflows.
LM Studio with GPT 20b: The latest release of LM Studio is highlighted for its improved tool-calling capabilities, with one user running a 20-billion parameter model with a 120k context window, suggesting it could soon become a go-to for many.

This trend of using a diverse set of AI tools demonstrates a maturing relationship between developers and LLMs. The focus has shifted from simply asking one AI for everything to strategically delegating tasks to the best model for the job.