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DeepSeek-V4.1-Flash-1017 vs Phi-3.5-MoE-instruct

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 1.6.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 1.6, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Phi-3.5-MoE-instruct

  • you are already invested in the Microsoft ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Phi-3.5-MoE-instruct
Microsoft
Core performance indexes
LLM Stats Score
51.0
#15
1.6
#342
Reasoning
48.6
#18
1.1
#335
Coding
39.1
#15
-3.9
#277
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
Phi-3.5-MoE-instruct
Math
31.8#67
4.3#296
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 31 for Phi-3.5-MoE-instruct

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Phi-3.5-MoE-instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

703.2B diff

DeepSeek-V4.1-Flash-1017 has 703.2B more parameters than Phi-3.5-MoE-instruct, making it 1172.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
60.0B
Phi-3.5-MoE-instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Wed Oct 21 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Phi-3.5-MoE-instruct does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Phi-3.5-MoE-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Phi-3.5-MoE-instruct was released on 2024-08-23.

DeepSeek-V4.1-Flash-1017 is 26 months newer than Phi-3.5-MoE-instruct.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

0 days ago

2.2yr newer
Phi-3.5-MoE-instruct

Aug 23, 2024

2.2 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Phi-3.5-MoE-instruct side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Phi-3.5-MoE-instruct
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Phi-3.5-MoE-instruct.

Which is better, DeepSeek-V4.1-Flash-1017 or Phi-3.5-MoE-instruct?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 1.6. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Phi-3.5-MoE-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-V4.1-Flash-1017 compare to Phi-3.5-MoE-instruct in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Phi-3.5-MoE-instruct?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Phi-3.5-MoE-instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4.1-Flash-1017 and Phi-3.5-MoE-instruct?

Key differences include LLM Stats Score (51.0 vs 1.6), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Phi-3.5-MoE-instruct?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Phi-3.5-MoE-instruct is developed by Microsoft.

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