Model Comparison

DeepSeek-R1-0528 vs Phi-3.5-MoE-instruct

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-R1-0528 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Phi-3.5-MoE-instruct is better at 0 benchmarks.

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Tue May 12 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

611.0B diff

DeepSeek-R1-0528 has 611.0B more parameters than Phi-3.5-MoE-instruct, making it 1018.3% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
671.0B
DeepSeek-R1-0528
60.0B
Phi-3.5-MoE-instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Tue May 12 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-R1-0528

MIT

Open weights

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Phi-3.5-MoE-instruct was released on 2024-08-23.

DeepSeek-R1-0528 is 9 months newer than Phi-3.5-MoE-instruct.

DeepSeek-R1-0528

May 28, 2025

11 months ago

9mo newer
Phi-3.5-MoE-instruct

Aug 23, 2024

1.7 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (81.0% vs 36.8%)
Higher MMLU-Pro score (85.0% vs 45.3%)
Microsoft

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Microsoft
Phi-3.5-MoE-instruct

FAQ

Common questions about DeepSeek-R1-0528 vs Phi-3.5-MoE-instruct.

Which is better, DeepSeek-R1-0528 or Phi-3.5-MoE-instruct?

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 is made by DeepSeek and Phi-3.5-MoE-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-R1-0528 compare to Phi-3.5-MoE-instruct in benchmarks?

DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.

What are the context window sizes for DeepSeek-R1-0528 and Phi-3.5-MoE-instruct?

DeepSeek-R1-0528 supports 131K tokens and Phi-3.5-MoE-instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes DeepSeek-R1-0528 and Phi-3.5-MoE-instruct?

DeepSeek-R1-0528 is developed by DeepSeek and Phi-3.5-MoE-instruct is developed by Microsoft.