DeepSeek R1
Use the new version of deepseek r1 0528 here.| Feature | DeepSeek R1-0528 | Why It Matters |
|---|---|---|
| Context window | 128 K tokens (real-world recall ≈ 32–64 K) | Summarise whole books or codebases |
| Model size | 671 B total / 37 B active (MoE) | Frontier reasoning on mid-range clusters |
| LiveCodeBench | 73.1 Pass@1 – #4 overall | Beats Grok 3 Mini & Qwen 3 |
| Token cost | $0.014 per 5 K-token chat | ~92 % cheaper than GPT-4o |
| Red-team fail rate | 91 % jailbreak, 93 % malware | Highest risk among top models |
Why This “Minor” Upgrade Shook the Leaderboards
DeepSeek’s engineers dropped R1-0528 onto Hugging Face on 28 May 2025 with no fanfare. Within three days it:- Overtook Grok 3 Mini on LiveCodeBench.
- Triggered Google and OpenAI to slash certain API prices.
- Re-ignited U.S. House hearings on CCP tech influence.
Under the Hood: 671 B Parameters on a Diet
Mixture of Experts (MoE)
Only 5 % of weights fire per token (37 B of 671 B), giving near-GPT-4 logic without GPT-4 bills.FP8 + Multi-Token Prediction
- FP8 math cuts VRAM 75 % versus FP32.
- 8-token decoding halves latency at long contexts.
Chain-of-Thought Training
Reinforcement Learning with Group Relative Policy Optimisation (GRPO) pushes the model to think in explicit steps—then prints those steps between<think> tags (great for debugging, disastrous for secrets).
Benchmark Showdown
Code Generation (LiveCodeBench v1.1)
| Rank | Model | Pass@1 |
|---|---|---|
| 1 | OpenAI o4 Mini (High) | 80.2 |
| 2 | OpenAI o3 (High) | 75.8 |
| 3 | OpenAI o4 Mini (Med) | 74.2 |
| 4 | DeepSeek R1-0528 | 73.1 |
| 5 | OpenAI o3 Mini (High) | 67.4 |
| 6 | xAI Grok 3 Mini (High) | 66.7 |
| 8 | Alibaba Qwen 3-235B | 65.9 |
Math & Logic
Original R1 hit 97.3 % on MATH-500. Early tests show 0528 edging closer to OpenAI o3-level reasoning.Red-Team Stress
| Attack Vector | Fail Rate* |
|---|---|
| Jailbreak prompts | 91 % |
| Malware generation | 93 % |
| Prompt injection | 86 % |
| Toxic output | 68 % |
Deploy-or-Avoid Guide
Access Paths
| Route | Context | Cost | Best For |
|---|---|---|---|
| OpenRouter API | 164 K* | $0.50 / M in, 2.18 out | Rapid prototyping |
| Fireworks AI | 32 K | Similar | Fine-tuned LoRA |
| Local GGUF 4-bit | 32–64 K | Hardware only | Data-sensitive orgs |
Hardening Checklist
- Strip
<think>before logs/UI. - Wrap replies with OpenAI Moderation or AWS Guardrails.
- Enforce rate-limits & max-tokens.
- Isolate secrets; never embed keys in prompts.
- Verify GGUF SHA-256; avoid rogue forks.
Cost Math
- 5 K-token dialog (in + out): $0.014 (R1-0528) vs $0.18 (GPT-4o).
- At 1M tokens/month you’d bank $332 savings.
Censorship, Data Flow & Geopolitics
- House CCP report: traffic routes via China Mobile; 85 % of democracy queries softened or blocked.
- NASA, U.S. Navy, Tennessee ban DeepSeek on official devices.
- Android app audited with hard-coded AES keys and SQL-injection flaws.
Where Does It Beat—and Lose to—GPT-4o?
| Task | Winner | Why |
|---|---|---|
| Long-doc summarisation | R1-0528 | 2–4× larger working memory |
| Raw coding Pass@1 cost | R1-0528 | 90 % cheaper |
| Hallucination control | GPT-4o | Better refusal heuristics |
| Safety compliance | GPT-4o | Lower malware, toxicity |
| Political neutrality | GPT-4o | No CCP alignment |
Roadmap: Eyes on DeepSeek R2
Rumoured Q3 2025 launch promises:- Multimodal I/O (text + image + audio).
- Generative Reward Modelling for self-feedback.
- Target: GPT-4o parity at half the compute.
Practical Use Cases Today
- Indie devs: generate boiler-plate React or Unity scripts fast.
- Academics: ingest full papers (≤ 60 K tokens) for meta-analysis.
- Legal ops: rapid first-pass contract review—locally hosted only.
- Game masters: rich lore consistency over giant campaign notes.
Expert Insight
“R1-0528 is the first free model that lets me paste a whole 400-page PDF and ask nuanced questions. But I sandbox it—its <think> tag once spilled an AWS key.”
—CTO, mid-size SaaS (name withheld)