DeepSeek-V4.1-Flash-1017 vs MAI-Code-1.1-Flash
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 27.4. MAI-Code-1.1-Flash is 1.2x cheaper per token.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 27.4, ranking #15 overall.
On price, MAI-Code-1.1-Flash is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 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-V4.1-Flash-1017
- overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
- you need open weights you can self-host or fine-tune
Choose MAI-Code-1.1-Flash
- cost matters — it's about 1.2x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 2 for MAI-Code-1.1-Flash
DeepSeek-V4.1-Flash-1017 and MAI-Code-1.1-Flashdon'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
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 1.5x more expensive than MAI-Code-1.1-Flash ($0.20/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) costs the same as MAI-Code-1.1-Flash ($1.20/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than MAI-Code-1.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 625.2B more parameters than MAI-Code-1.1-Flash, making it 453.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to MAI-Code-1.1-Flash's 256,000 tokens. Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and MAI-Code-1.1-Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
MAI-Code-1.1-Flash
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while MAI-Code-1.1-Flash uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while MAI-Code-1.1-Flash was released on 2026-08-11.
DeepSeek-V4.1-Flash-1017 is 2 months newer than MAI-Code-1.1-Flash.
Oct 17, 2026
2 days ago
2mo newerAug 11, 2026
2 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. MAI-Code-1.1-Flash is available from GitHub Copilot.
DeepSeek-V4.1-Flash-1017
MAI-Code-1.1-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and MAI-Code-1.1-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs MAI-Code-1.1-Flash.