DeepSeek-R1-0528 vs LLaMA 4 Maverick: A detailed comparison of reasoning, coding, speed, and pricing to help developers, researchers, and businesses choose the best open-source LLM.
Introduction
The battle for open-weight AI supremacy is heating up, with DeepSeek-R1-0528 (by DeepSeek AI) and LLaMA 4 Maverick (by Meta) emerging as two of the most powerful models in 2025. Both offer cutting-edge reasoning, coding, and long-context capabilities, but they cater to different needs.
This 1,500+ word comparison breaks down:
β Architecture & training innovations
β Benchmark performance (MMLU, LiveCodeBench, AIME 2025)
β Real-world coding & reasoning tests
β Pricing & hardware requirements
β Developer feedback & use cases
Who should read this? AI engineers, startup founders, and researchers deciding between these models for coding, research, or enterprise AI applications.
π Quick Comparison Table: DeepSeek-R1-0528 vs LLaMA 4 Maverick
| Feature | DeepSeek-R1-0528 | LLaMA 4 Maverick |
|---|---|---|
| Developer | DeepSeek AI | Meta AI |
| Architecture | Mixture-of-Experts (MoE) | MoE (128 experts) |
| Total Parameters | 671B (37B active) | 400B (17B active) |
| Context Window | 128K tokens | 1M tokens |
| Strengths | Math & coding (87.5% AIME) | Multimodal & long-context |
| Pricing (Input/Output per M tokens) | $0.55/$2.19 | $0.17/$0.85 (Novita AI) |
| Best For | STEM, theorem proving, code debugging | Legal docs, research papers, multimodal tasks |
Model Overviews: DeepSeek-R1-0528 vs LLaMA 4 Maverick
1. DeepSeek-R1-0528 β The Reasoning Powerhouse
- Focus: Specialized in mathematical reasoning, code generation, and logic-heavy tasks.
- Key Innovations:
- Two-stage RL training for improved reasoning patterns 5.
- 92% deeper reasoning (23K tokens per AIME question vs. 12K in previous versions) 10.
- 87.5% accuracy on AIME 2025, rivaling GPT-4.5 and Gemini 2.5 Pro 6.
- Licensing: MIT License (commercial-friendly) 10.
2. LLaMA 4 Maverick β The Multimodal Long-Context Expert
- Focus: 1M token context, native multimodal (text + images), and multilingual support.
- Key Innovations:
- Early fusion training (text + images simultaneously) for better coherence 14.
- 10x larger context than competitors (handles entire codebases or novels) 14.
- 73.4% on MMMU (multimodal benchmark), outperforming Gemini 2.0 Flash 1.
- Licensing: Custom commercial license (some restrictions apply) 11.
Benchmark Performance: DeepSeek-R1-0528 vs LLaMA 4 Maverick
1. Mathematical Reasoning (AIME 2025)
| Model | Accuracy |
|---|---|
| DeepSeek-R1-0528 | 87.5% |
| LLaMA 4 Maverick | ~69.8% (GPQA Diamond) |
β DeepSeek dominates in math, while LLaMA 4 is stronger in general knowledge (MMLU-Pro: 80.5%) 11.
2. Coding (LiveCodeBench & SWE Verified)
| Model | LiveCodeBench (Pass@1) | SWE Verified (Resolved) |
|---|---|---|
| DeepSeek-R1-0528 | 73.3% | 57.6% |
| LLaMA 4 Maverick | 43.4% | Not tested |
β DeepSeek is the clear winner for coding, while LLaMA 4 is better for documentation & high-level logic 11.
3. Long-Context Understanding (MTOB & DocVQA)
| Model | MTOB (Half Book) | DocVQA (Text) |
|---|---|---|
| DeepSeek-R1-0528 | Not tested | No support |
| LLaMA 4 Maverick | 54.0% | 94.4% |
β LLaMA 4 crushes long-context tasks, making it ideal for legal/financial analysis 1.
Use Case Breakdown: DeepSeek-R1-0528 vs LLaMA 4 Maverick
1. Coding & Debugging
- DeepSeek-R1-0528:
- Best for: Complex algorithm design, theorem proving, and code optimization.
- Real-world test: Fixed a Next.js cache bug that LLaMA 4 struggled with 11.
- LLaMA 4 Maverick:
- Best for: Code documentation and high-level architecture planning.
2. Research & Document Analysis
- LLaMA 4 Maverick:
- Processes 10M token documents (entire research papers or legal contracts) 14.
- Outperforms in DocVQA (94.4%) for extracting insights from PDFs 1.
- DeepSeek-R1-0528:
- Better for mathematical proofs and scientific reasoning (GPQA: 81.0%) 5.
3. Multimodal Tasks (Images + Text)
- Only LLaMA 4 supports images, scoring 73.4% on MMMU (vs. DeepSeekβs text-only limitation) 1.
π° Pricing & Hardware Requirements
| Model | Input Cost (per M tokens) | Output Cost (per M tokens) | GPU Memory Needed |
|---|---|---|---|
| DeepSeek-R1-0528 | $0.55 | $2.19 | ~1.5TB (24Γ H100) |
| LLaMA 4 Maverick | $0.17 (Novita AI) | $0.85 | 18.8TB (240Γ H100) |
β LLaMA 4 is cheaper but requires 10x more GPU memory for ultra-long contexts 11.
π Final Verdict: Who Should Choose What?
Pick DeepSeek-R1-0528 If You Need:
β Elite mathematical reasoning (AIME 87.5%).
β Superior coding performance (LiveCodeBench 73.3%).
β Cost-efficient inference (lower GPU requirements).
Pick LLaMA 4 Maverick If You Need:
β 1M token context (entire books, legal docs).
β Multimodal support (images + text).
β Multilingual & general knowledge tasks.
In comparesion of DeepSeek-R1-0528 vs LLaMA 4 Maverick, For most developers, DeepSeek-R1-0528 is the better choice for coding & STEM, whileΒ LLaMA 4 Maverick dominates in long-context and multimodal applicationsΒ 311.
β FAQ
1. Can DeepSeek-R1-0528 process images?
β Noβitβs text-only, while LLaMA 4 supports images & video 1.
2. Which model is faster for real-time chat?
β‘ LLaMA 4 Maverick (145 tokens/sec on Fireworks AI) vs. DeepSeekβs 21.2 tokens/sec 14.
3. Is DeepSeek-R1-0528 open-source?
β Yes (MIT License), while LLaMA 4 has custom commercial terms 1011.
π Explore More LLM Comparisons on RankLLMs.com
Also Read: Deepseek-r1-0528 vs Grok 3
Final Thought: The “best” model depends on your use case. For coding & math, DeepSeek-R1-0528 wins. For long docs & images, LLaMA 4 Maverick is unbeatable. Choose wisely! π
Sources:
- [1] DocsBot AI – LLaMA 4 vs. DeepSeek-R1
- [3] RankLLMs – DeepSeek-R1 vs. LLaMA 4
- [4] NVIDIA Docs – DeepSeek-R1-0528
- [8] Medium – DeepSeek-R1-0528 Update
- [9] Novita AI – LLaMA 4 vs. DeepSeek-V3
- [10] Fireworks AI – Optimizing LLaMA 4
Note: All benchmarks & pricing reflect July 2025 data.
