First Experience Coding with DeepSeek-R1-0528
TL;DR
- DeepSeek-R1-0528: Latest open source reasoning model with MIT license
- Major breakthrough: Significantly improved performance over previous version (87.5% vs 70% on AIME 2025)
- Architecture: 671B total parameters, ~37B active per token via Mixture-of-Experts
- Major limitation: 15-30s latency via OpenRouter API vs ~1s for other models
- Best for: Complex reasoning, architectural planning, vendor independence
- Poor for: Real-time coding, rapid iteration, interactive development
- Bottom line: Impressive reasoning capabilities, but latency challenges practical use
The Promise vs. My 8-Hour Reality Check
From @deepseek_ai: DeepSeek-R1-0528 is now available! This latest reasoning model shows substantial improvements across benchmarks while maintaining MIT licensing for complete open-source access.
Source: https://x.com/deepseek_ai/status/1928061589107900779
My response: Hold my coffee while I test this "breakthrough"...
SPOILER: It's brilliant... if you can wait 30 seconds for every response. And it keeps increasing as your context grows
I was 47 minutes into debugging a Rust async runtime when DeepSeek-R1-0528 (via my favorite coding agent) finally responded with the perfect solution. By then, I'd already fixed the bug myself, grabbed coffee, and started questioning my life choices.
Here's what 8 hours of testing taught me about the latest "open source breakthrough."
Reality Check: Hype vs. My Actual Experience
DeepSeek's announcement promises groundbreaking performance with practical accessibility. After intensive testing, here's how those claims stack up:
| DeepSeek's Claim | My Reality | Verdict |
|---|---|---|
| "Matches GPT/Claude performance" | Often exceeds it on reasoning | TRUE |
| "MIT licensed open source" | Completely open, no restrictions | TRUE |
| "Substantial improvements" | Major benchmark gains confirmed | TRUE |
The breakthrough is real. The daily usability is... challenging.
Before diving into why those response times matter so much, let's understand what makes this model technically impressive enough that I kept coming back despite the frustration.
The Tech Behind the Magic (And Why It's So Slow)
Key Architecture Stats
- 671B total parameters (685B with extras)
- ~37B active per token via Mixture-of-Experts routing
- 128K context window
- MIT license (completely open source)
- Cost: $0.50 input / $2.18 output per 1M tokens
Why the Innovation Matters
R1-0528 achieves GPT-4 level reasoning at ~5.5% parameter activation cost through:
- Reinforcement Learning Training: Pure RL without supervised fine-tuning initially
- Chain-of-Thought Architecture: Multi-step reasoning for every response
- Expert Routing: Different specialists activate for different coding patterns
Why It's Painfully Slow
Every response requires:
- Thinking tokens: Internal reasoning in
<think>...</think>blocks (hundreds-thousands of tokens) - Expert selection: Dynamic routing across 671B parameters
- Multi-step verification: Problem analysis → solution → verification
When R1-0528 generates a 2000-token reasoning trace for a 100-token answer, you pay computational cost for all 2100 tokens.
The Benchmarks Don't Lie (But They Don't Code Either)
The performance improvements are legitimate:
Key Wins
| Benchmark | Previous | R1-0528 | Improvement |
|---|---|---|---|
| AIME 2025 | 70.0% | 87.5% | +17.5% |
| Coding (LiveCodeBench) | 63.5% | 73.3% | +9.8% |
| Codeforces Rating | 1530 | 1930 | +400 points |
| SWE Verified (Resolved) | 49.2% | 57.6% | Notable progress |
| Aider-Polyglot | 53.3% | 71.6% | Major improvement |
But here's the thing: Benchmarks run with infinite patience. Real development doesn't.
The Latency Reality
| Model Type | Response Time | Developer Experience |
|---|---|---|
| Claude/GPT-4 | 0.8-1.0s | Smooth iteration |
| DeepSeek-R1-0528 | 15-30s | Productivity killer |
When R1-0528 Actually Shines
Despite my latency complaints, there are genuine scenarios where waiting pays off:
Perfect Use Cases
- Large codebase analysis (20,000+ lines) - leverages 128K context beautifully
- Architectural planning - deep reasoning justifies wait time
- Precise instruction following - delivers exactly what you ask for
- Vendor independence - MIT license enables self-hosting
Frustrating Use Cases
- Real-time debugging - by the time it responds, you've fixed it
- Rapid prototyping - kills the iterative flow
- Learning/exploration - waiting breaks the learning momentum
Reasoning Transparency
The "thinking" process is genuinely impressive:
- Problem analysis and approach planning
- Edge case consideration
- Solution verification
- Output polishing
Different experts activate for different patterns (API design vs systems programming vs unsafe code).
My Honest Take: Historic Achievement, Practical Challenges
The Historic Achievement
- First truly competitive open reasoning model
- MIT license = complete vendor independence
- Proves open source can match closed systems
The Daily Reality
Remember that 47-minute debugging session? It perfectly captures the R1-0528 experience: technically brilliant, practically challenging.
The question isn't whether R1-0528 is impressive - it absolutely is.
The question is whether you can build your workflow around waiting for genius to arrive.
Community Discussion
Drop your experiences below:
- Have you tested R1-0528 for coding? What's your patience threshold?
- Found ways to work around the latency?
The Bottom Line
DeepSeek's announcement wasn't wrong about capabilities - the benchmark improvements are real, reasoning quality is impressive, and the MIT license is genuinely game-changing.
For architectural planning where you can afford to wait? Absolutely worth it.
For rapid iteration? Not quite there yet.