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DeepSeek-V3 vs Phi-3.5-mini-instruct

DeepSeek-V3 leads the LLM Stats Score 15.8 to -3.7. Phi-3.5-mini-instruct is 4.8x cheaper per token.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek-V3 leads the overall LLM Stats Score 15.8 to -3.7, ranking #215 overall.

In the 3 individual benchmarks reported for both models, DeepSeek-V3 wins 3; this is a narrower head-to-head signal than the composite indexes.

On price, Phi-3.5-mini-instruct is roughly 4.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V3 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V3

  • overall performance matters — it scores 15.8 and ranks #215 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results
  • you process long inputs — it offers a 131,072 token context window
  • you want the most recent training data — it shipped Dec 2024

Choose Phi-3.5-mini-instruct

  • cost matters — it's about 4.8x cheaper per token

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
Phi-3.5-mini-instruct
Microsoft
Core performance indexes
LLM Stats Score
15.8
#215
-3.7
#333
Reasoning
14.9
#216
-4.6
#330
Coding
6.5
#183
-6.8
#252
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
Input price
$0.27 / M
$0.10 / M
Output price
$1.10 / M
$0.10 / M
Context window
131,072
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V3
Phi-3.5-mini-instruct
Math
18.2#174
-1.1#294
Finance
20.1#79
1.0#185
Legal
20.1#63
1.0#172
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V3 · 31 for Phi-3.5-mini-instruct

3 shared

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.

Thu Sep 03 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

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
Thu Sep 03 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
Thu Sep 03 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

1.7 years ago

4mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

2.0 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→

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Phi-3.5-mini-instruct
Open in Playground

FAQ

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

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

DeepSeek-V3 leads the LLM Stats Score 15.8 to -3.7. 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 capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3 compare to Phi-3.5-mini-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-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

Is DeepSeek-V3 cheaper than Phi-3.5-mini-instruct?

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.

What are the context window sizes for DeepSeek-V3 and Phi-3.5-mini-instruct?

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.

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

Key differences include LLM Stats Score (15.8 vs -3.7), 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.

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

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

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