I've Been Rotating Between DeepSeek, Claude, and ChatGPT for 8 Months - Here's What Actually Works

DeepSeek R1 launched in January and it's genuinely terrifying how good it is at math. 96.8% success rate on MATH-500 benchmark while Claude and ChatGPT tap out around 75-78%.

The Good: Algorithm Beast Mode

This thing scored 2029 on Codeforces. That's literally top 4% of competitive programmers worldwide. I've watched it solve DP problems that had me scrolling through GeeksforGeeks for 4 hours like a desperate CS student.

Had this Dijkstra variant last month - shortest paths with dynamic edge weights that changed based on time of day. Spent 3 hours on Stack Overflow, tried implementing A* twice, gave up and threw it at DeepSeek. Not only did it solve it but explained why my A* heuristic was fucked and derived the mathematical proof for why this specific variant needs modified Dijkstra.

The Bad: Watching Paint Dry Has Nothing on This

DeepSeek takes 5-8 minutes for anything remotely complex. You literally sit there watching it think step-by-step like the world's slowest human. Great for learning, absolute hell when production is on fire.

Tried using it during a late-night bug hunt once. PostgreSQL was throwing FATAL: remaining connection slots are reserved for non-replication superuser connections, users were screaming, and I'm sitting there for 7 fucking minutes watching DeepSeek analyze connection pooling theory. Said fuck it and switched to Claude.

The Weird: Multiple Personality Disorder

DeepSeek randomly thinks it's Claude. Like mid-conversation solving a binary tree problem, it'll suddenly say "As Claude, I apologize for the confusion..."

First time this happened I genuinely thought I had multiple tabs open. Checked three times. Nope, just DeepSeek having an identity crisis. Now I just hit refresh whenever it thinks it's Anthropic's model. Happens 1-2 times per session if you're using it heavily, usually after 30+ minutes of back-and-forth.

Claude 3.5: Expensive but Actually Useful

Claude gets 51.3% on SWE-bench Verified, which tests real GitHub issues from actual open-source projects. Not leetcode bullshit - the messy debugging stuff you deal with every fucking day.

I've been bouncing between Claude and DeepSeek for months. Claude just handles legacy code better. When I'm debugging 3000 lines of jQuery 1.8.3 spaghetti from 2017 with zero documentation and variable names like x1, temp2, Claude somehow makes sense of it. DeepSeek wants to rewrite everything in TypeScript with proper architecture, which would take 3 weeks and break production.

Had this nightmare recently - React 16.8 app with some homegrown Redux knockoff that barely held together. Previous developer rage-quit mid-refactor, left no handover docs. Claude traced through the component state mutations and found the race condition causing random crashes. DeepSeek spent 45 minutes explaining why class components are deprecated and how hooks would solve this properly.

The 200K Context Window Actually Works

Claude remembers your entire codebase. I've pasted full React projects and it maintains context across components, understanding how everything connects. DeepSeek starts forgetting variable names after 30 minutes.

The API Will Destroy Your Credit Card

Claude costs $15 per million output tokens. Burned through $312 last month debugging a Kubernetes networking hell that involved 7 different microservices. Worth every penny when users can't log in, absolutely brutal for normal "why won't this CSS work" debugging.

ChatGPT-4o: The Reliable Backup

ChatGPT-4o is boring in the best way. 90% HumanEval score - not winning any contests, but it also doesn't randomly think it's someone else or take 7 minutes to respond.

It actually leads BigCodeBench at 32%, which tests complex multi-step tasks. Good for when you need something that works without surprises.

When I Actually Use It

ChatGPT is my fallback. When DeepSeek is taking forever and Claude is draining my API budget, ChatGPT just works. It's not brilliant at anything specific, but it handles most coding tasks without breaking.

Built a demo app last week for client presentation. Needed something clean and functional in 2 hours before the meeting. ChatGPT cranked out React 18 code with hooks that actually compiled and ran. No 7-minute thinking sessions, no identity crises, no surprise $67 API bill.

The Middle Ground Pricing

$10 per million tokens. Not cheap like DeepSeek ($2), not expensive like Claude ($15). The Goldilocks option when you need decent results without breaking the bank.

Forget the benchmarks. Here's what happens when you actually use these things to ship code.

Why I Use All Three (Not by Choice)

I rotate between DeepSeek for algorithms, Claude for legacy code, and ChatGPT for quick fixes. Sounds overkill but each one fails differently, so having backups saves your ass.

Real example: Node.js app was eating 2GB RAM and crashing with FATAL ERROR: Reached heap limit Allocation failed - JavaScript heap out of memory every 6 hours. DeepSeek spent 10 minutes explaining V8 garbage collection theory. Claude looked at my Express middleware and said "you're not closing those MongoDB connections in your error handlers, look at line 47 in auth.js." Took another 2 hours hunting through nested try-catch blocks, but at least I wasn't debugging completely blind.

DeepSeek R1: The Perfectionist That Takes Forever

DeepSeek is that genius coworker who gives perfect solutions but takes 20 minutes to explain basic concepts. When I needed a graph algorithm for route optimization, it produced textbook-perfect code with step-by-step mathematical reasoning.

The painful reality:

• 7+ minute wait times for anything complex (I literally time it with my phone stopwatch)
• Identity confusion hits 1-2 times per session - randomly says "As Claude, I need to clarify..."
• Forgets you're writing JavaScript and starts suggesting Python imports after 30 minutes
• API timeouts during peak hours (usually 2-4pm PST when everyone's debugging)

When the wait pays off: Algorithm design, mathematical optimization, competitive programming problems. Watched it derive an O(n log n) solution I would've never found on Stack Overflow.

Claude 3.5: Expensive but Gets Shit Done

Been using both for months. DeepSeek wins benchmarks but Claude handles messy real-world code better. When you're debugging 5000 lines of legacy React that someone wrote while high on caffeine, Claude actually understands the chaos.

Why it works for production:

• Actually reads your whole codebase with 200K context window
• Fewer "what the fuck did it just generate?" moments
• Understands project structure instead of rewriting everything
• Handles weird edge cases that break other models

The financial pain: Claude API costs are fucking brutal. Spent $312 last month fixing a Kubernetes service mesh disaster where nothing could talk to anything. Worth every penny during production emergencies, absolutely devastating for "why won't this CSS center properly" debugging.

ChatGPT-4o: Boring and Reliable

ChatGPT doesn't win benchmarks but it also doesn't randomly break your workflow. It's like that senior developer who writes boring, correct code that just works.

Why I keep using it:

• Consistent behavior - no identity crises or 10-minute thinking sessions
• Reasonable pricing at $10/million tokens
• Good at quick prototypes when you need something working in 20 minutes
• Can analyze screenshots and diagrams

Real example: Built a customer dashboard last week for client demo. 2 hours before the meeting. ChatGPT cranked out clean React 18 code with Material-UI that compiled first try and looked professional enough that the client asked for the GitHub link.

My Actual Daily Workflow

Morning algorithm work: DeepSeek (grab coffee while it thinks)
Production fires: Claude (expense it to the client)
Quick prototypes: ChatGPT (just works)
3AM debugging: Whatever isn't broken

Reality check: All three are fucking useless with undocumented legacy APIs. None of them magically understand your custom JWT auth system or that PostgreSQL schema from 2019 with 47 junction tables that the previous team lead designed while drunk.

What the Benchmarks Miss

• DeepSeek overthinks simple problems - spend 5 minutes reasoning through a missing semicolon
• Claude's context window doesn't help when your "codebase" is 50 different microservices
• ChatGPT's consistency means consistently mediocre at specialized tasks
• All of them make up function signatures for libraries they've never seen

The real trick is using the right tool for each job and having backups when your primary choice decides to shit the bed.

After 8 months of daily use: DeepSeek for algorithms, Claude for production, ChatGPT for everything else. Each has earned its place through brilliant successes and spectacular failures.

The future isn't finding the perfect coding AI - it's knowing which broken one to use when. You probably have questions about the messy reality, so let me answer what you're actually wondering.