DeepSeek-V2.5 vs Phi-3.5-mini-instruct Comparison

Comparing DeepSeek-V2.5 and Phi-3.5-mini-instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek-V2.5 outperforms in 5 benchmarks (Arena Hard, GSM8k, HumanEval, MATH, MMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.

DeepSeek-V2.5 significantly outperforms across most benchmarks.

Tue Mar 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Phi-3.5-mini-instruct costs less

For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 1.4x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 2.8x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, DeepSeek-V2.5 is more expensive than Phi-3.5-mini-instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Mar 17 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
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Model Size

Parameter count comparison

232.2B diff

DeepSeek-V2.5 has 232.2B more parameters than Phi-3.5-mini-instruct, making it 6110.5% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
236.0B
DeepSeek-V2.5
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

Phi-3.5-mini-instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Phi-3.5-mini-instruct can generate longer responses up to 128,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Tue Mar 17 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Phi-3.5-mini-instruct uses MIT.

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

DeepSeek-V2.5

deepseek

Open weights

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Phi-3.5-mini-instruct was released on 2024-08-23.

Phi-3.5-mini-instruct is 4 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

Phi-3.5-mini-instruct

Aug 23, 2024

1.6 years ago

3mo newer

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

Provider Availability

DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Phi-3.5-mini-instruct is available from Azure. The availability of providers can affect quality of the model and reliability.

DeepSeek-V2.5

deepseek logo
DeepSeek
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.70/1MOutput Price:Output: $1.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher Arena Hard score (76.2% vs 37.0%)
Higher GSM8k score (95.1% vs 86.2%)
Higher HumanEval score (89.0% vs 62.8%)
Higher MATH score (74.7% vs 48.5%)
Higher MMLU score (80.4% vs 69.0%)
Microsoft
Larger context window (128,000 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Microsoft
Phi-3.5-mini-instruct