DeepSeek-V4.1-Flash vs Phi-3.5-MoE-instruct
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 1.9.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 1.9, ranking #12 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Sep 2026
Choose Phi-3.5-MoE-instruct
- you are already invested in the Microsoft ecosystem
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 31 for Phi-3.5-MoE-instruct
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Phi-3.5-MoE-instruct is better at 0 benchmarks.
DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 703.2B more parameters than Phi-3.5-MoE-instruct, making it 1172.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Phi-3.5-MoE-instruct does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
Phi-3.5-MoE-instruct
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Phi-3.5-MoE-instruct was released on 2024-08-23.
DeepSeek-V4.1-Flash is 25 months newer than Phi-3.5-MoE-instruct.
Sep 10, 2026
0 days ago
2.0yr newerAug 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.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Phi-3.5-MoE-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Phi-3.5-MoE-instruct.