DeepSeek-V3.2 vs MAI-Code-1.1-Flash
DeepSeek-V3.2 and MAI-Code-1.1-Flash are closely matched at 33.2 and 28.0 on the LLM Stats Score. DeepSeek-V3.2 is 1.6x cheaper per token.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek-V3.2 and MAI-Code-1.1-Flash are closely matched on the overall LLM Stats Score at 33.2 and 28.0.
In the 1 individual benchmarks reported for both models, DeepSeek-V3.2 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3.2 is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MAI-Code-1.1-Flash also accepts a larger context window (256,000 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-V3.2
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.6x cheaper per token
- you need open weights you can self-host or fine-tune
Choose MAI-Code-1.1-Flash
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Individual benchmarks
17 reported for DeepSeek-V3.2 · 2 for MAI-Code-1.1-Flash
DeepSeek-V3.2 outperforms in 1 benchmarks (SWE-Bench Verified), while MAI-Code-1.1-Flash is better at 0 benchmarks.
DeepSeek-V3.2 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-V3.2 ($0.26/1M tokens) is 1.3x more expensive than MAI-Code-1.1-Flash ($0.20/1M tokens).
For output processing, DeepSeek-V3.2 ($0.38/1M tokens) is 3.2x cheaper than MAI-Code-1.1-Flash ($1.20/1M tokens).
In conclusion, MAI-Code-1.1-Flash is more expensive than DeepSeek-V3.2.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 has 547.0B more parameters than MAI-Code-1.1-Flash, making it 396.4% larger.
Context Window
Maximum input and output token capacity
MAI-Code-1.1-Flash accepts 256,000 input tokens compared to DeepSeek-V3.2's 163,840 tokens. Only DeepSeek-V3.2 specifies output context (163,840 tokens).
Input capabilities
Documented input modalities across available providers
MAI-Code-1.1-Flash supports multimodal inputs, whereas DeepSeek-V3.2 does not.
MAI-Code-1.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2
MAI-Code-1.1-Flash
License
Usage and distribution terms
DeepSeek-V3.2 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-V3.2 was released on 2025-12-01, while MAI-Code-1.1-Flash was released on 2026-08-11.
MAI-Code-1.1-Flash is 8 months newer than DeepSeek-V3.2.
Dec 1, 2025
9 months ago
Aug 11, 2026
1 months ago
8mo 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-V3.2 is available from DeepInfra, Novita, Fireworks. MAI-Code-1.1-Flash is available from GitHub Copilot.
DeepSeek-V3.2
MAI-Code-1.1-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 and MAI-Code-1.1-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 vs MAI-Code-1.1-Flash.