Model Comparison

DeepSeek-V3 vs Phi-3.5-mini-instruct

DeepSeek-V3 significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is 4.8x cheaper per token.

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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-mini-instruct is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Mon Apr 06 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-V3 ($0.27/1M tokens) is 2.7x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, DeepSeek-V3 ($1.10/1M tokens) is 11.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, DeepSeek-V3 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
Mon Apr 06 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

667.2B diff

DeepSeek-V3 has 667.2B more parameters than Phi-3.5-mini-instruct, making it 17557.9% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
671.0B
DeepSeek-V3
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3 accepts 131,072 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. DeepSeek-V3 can generate longer responses up to 131,072 tokens, while Phi-3.5-mini-instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Mon Apr 06 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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-V3

MIT + Model License (Commercial use allowed)

Open weights

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek-V3

Dec 25, 2024

1.3 years ago

4mo newer
Phi-3.5-mini-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

Provider Availability

DeepSeek-V3 is available from DeepSeek. Phi-3.5-mini-instruct is available from Azure.

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M

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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (59.1% vs 30.4%)
Higher MMLU score (88.5% vs 69.0%)
Higher MMLU-Pro score (75.9% vs 47.4%)
Microsoft
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

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

FAQ

Common questions about DeepSeek-V3 vs Phi-3.5-mini-instruct

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Phi-3.5-mini-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.
Phi-3.5-mini-instruct is 2.7x cheaper for input tokens. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure.
DeepSeek-V3 supports 131K tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 128K), input pricing ($0.27 vs $0.10/M), licensing (MIT + Model License (Commercial use allowed) vs MIT). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Phi-3.5-mini-instruct is developed by Microsoft.