DeepSeek-V2.5 vs Phi-3.5-mini-instruct
DeepSeek-V2.5 leads the LLM Stats Score 8.8 to -3.3. Phi-3.5-mini-instruct is 1.8x cheaper per token.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek-V2.5 leads the overall LLM Stats Score 8.8 to -3.3, ranking #259 overall.
In the 5 individual benchmarks reported for both models, DeepSeek-V2.5 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Phi-3.5-mini-instruct also accepts a larger context window (128,000 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-V2.5
- overall performance matters — it scores 8.8 and ranks #259 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
Choose Phi-3.5-mini-instruct
- cost matters — it's about 1.8x cheaper per token
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Aug 2024
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 31 for Phi-3.5-mini-instruct
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.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
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
Model Size
Parameter count comparison
DeepSeek-V2.5 has 232.2B more parameters than Phi-3.5-mini-instruct, making it 6110.5% larger.
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.
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
Open weights
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.
May 8, 2024
2.3 years ago
Aug 23, 2024
2.0 years ago
3mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Phi-3.5-mini-instruct is available from Azure.
DeepSeek-V2.5
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Phi-3.5-mini-instruct.