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# Claude vs GPT vs DeepSeek for Coding: A Transparent Test Plan

**[Medium](https://daily.dev/sources/medium_js)** · 3 min read · 0 upvotes · 0 comments

## Summary

A methodology piece arguing that choosing the best coding LLM should not rely on public benchmark leaderboards, but on a small, repeatable, self-controlled task suite built from real backlog work. It recommends defining a specific decision statement, holding environment variables constant across models, scoring runs on correctness, human rework, latency, and cost, and publishing the full method alongside any ranking. The piece is written by someone working on RouterPlex, a model-routing gateway, which is mentioned as a tool for running the same suite across providers.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://routerrplex.medium.com/claude-vs-gpt-vs-deepseek-for-coding-a-transparent-test-plan-159109b195db>

## Questions this post answers

### How should I set up a fair comparison test between Claude, GPT, and DeepSeek for coding tasks?

Start by writing a one-sentence decision statement defining what you're choosing a model for, such as TypeScript bug fixes that pass tests within five minutes for under $0.25 per task. Then build 12-20 tasks from your real backlog across categories like bug fixes, refactors, and diff reviews, keeping repository revision, prompts, tool permissions, and timeouts identical across models so you compare models rather than workflows.

_Teams comparing coding models can track methodology write-ups like this one on daily.dev before picking a default._

### What metrics should I record when benchmarking an AI coding model?

Record four fields per run: correctness (did the test pass or did review catch the real issue), human rework (minutes a developer spent fixing the result), latency (time from request to usable answer), and cost (input, output, and retry costs combined). For coding agents, also track whether unrelated files were touched, since that signals risk even when tests pass.

_Developers weighing model trade-offs on cost and reliability can follow this kind of evaluation guidance on daily.dev._

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- [Grok 4.5 vs Claude Fable 5 vs GPT-5.6 Sol: How I’m Testing Them Across 100 Real Tasks](https://daily.dev/posts/grok-4-5-vs-claude-fable-5-vs-gpt-5-6-sol-how-i-m-testing-them-across-100-real-tasks-hb5pgi2wo) · Medium · 0 upvotes · 0 comments
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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-coding](https://daily.dev/tags/ai-coding), [#claude](https://daily.dev/tags/claude), [#gpt](https://daily.dev/tags/gpt), [#deepseek](https://daily.dev/tags/deepseek)

[View this post on daily.dev](https://daily.dev/posts/claude-vs-gpt-vs-deepseek-for-coding-a-transparent-test-plan-we6go4dfl)

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