Model Comparison

DeepSeek-V3 vs Phi-3.5-MoE-instruct

DeepSeek-V3 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Phi-3.5-MoE-instruct is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

611.0B diff

DeepSeek-V3 has 611.0B more parameters than Phi-3.5-MoE-instruct, making it 1018.3% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
671.0B
DeepSeek-V3
60.0B
Phi-3.5-MoE-instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Sat May 02 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Phi-3.5-MoE-instruct uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Phi-3.5-MoE-instruct was released on 2024-08-23.

DeepSeek-V3 is 4 months newer than Phi-3.5-MoE-instruct.

DeepSeek-V3

Dec 25, 2024

1.4 years ago

4mo 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 (59.1% vs 36.8%)
Higher MMLU score (88.5% vs 78.9%)
Higher MMLU-Pro score (75.9% vs 45.3%)
Microsoft

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

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Microsoft
Phi-3.5-MoE-instruct

FAQ

Common questions about DeepSeek-V3 vs Phi-3.5-MoE-instruct.

Which is better, DeepSeek-V3 or Phi-3.5-MoE-instruct?

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 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-V3 compare to Phi-3.5-MoE-instruct in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. 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-V3 and Phi-3.5-MoE-instruct?

DeepSeek-V3 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.

What are the main differences between DeepSeek-V3 and Phi-3.5-MoE-instruct?

Key differences include licensing (MIT + Model License (Commercial use allowed) vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Phi-3.5-MoE-instruct?

DeepSeek-V3 is developed by DeepSeek and Phi-3.5-MoE-instruct is developed by Microsoft.