Model Comparison

DeepSeek-V3.1 vs Phi-3.5-MoE-instruct

DeepSeek-V3.1 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3.1 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Phi-3.5-MoE-instruct is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Wed Apr 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Microsoft
Phi-3.5-MoE-instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

611.0B diff

DeepSeek-V3.1 has 611.0B more parameters than Phi-3.5-MoE-instruct, making it 1018.3% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
671.0B
DeepSeek-V3.1
60.0B
Phi-3.5-MoE-instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Wed Apr 15 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-V3.1

MIT

Open weights

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Phi-3.5-MoE-instruct was released on 2024-08-23.

DeepSeek-V3.1 is 5 months newer than Phi-3.5-MoE-instruct.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

4mo newer
Phi-3.5-MoE-instruct

Aug 23, 2024

1.6 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

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Higher GPQA score (74.9% vs 36.8%)
Higher MMLU-Pro score (83.7% vs 45.3%)
Microsoft

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Microsoft
Phi-3.5-MoE-instruct

FAQ

Common questions about DeepSeek-V3.1 vs Phi-3.5-MoE-instruct

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 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.
DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.
DeepSeek-V3.1 supports 164K 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.
DeepSeek-V3.1 is developed by DeepSeek and Phi-3.5-MoE-instruct is developed by Microsoft.