MAI-Code-1.1-Flash vs Qwen3.5-27B
MAI-Code-1.1-Flash and Qwen3.5-27B are closely matched at 28.0 and 34.0 on the LLM Stats Score. MAI-Code-1.1-Flash is 1.8x cheaper per token.
Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MAI-Code-1.1-Flash and Qwen3.5-27B are closely matched on the overall LLM Stats Score at 28.0 and 34.0.
In the 1 individual benchmarks reported for both models, MAI-Code-1.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, MAI-Code-1.1-Flash is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.5-27B also accepts a larger context window (262,144 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 MAI-Code-1.1-Flash
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.8x cheaper per token
- you want the most recent training data — it shipped Aug 2026
Choose Qwen3.5-27B
- you process long inputs — it offers a 262,144 token context window
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
2 reported for MAI-Code-1.1-Flash · 81 for Qwen3.5-27B
MAI-Code-1.1-Flash outperforms in 1 benchmarks (SWE-Bench Verified), while Qwen3.5-27B is better at 0 benchmarks.
MAI-Code-1.1-Flash 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, MAI-Code-1.1-Flash ($0.20/1M tokens) is 1.3x cheaper than Qwen3.5-27B ($0.26/1M tokens).
For output processing, MAI-Code-1.1-Flash ($1.20/1M tokens) is 2.0x cheaper than Qwen3.5-27B ($2.40/1M tokens).
In conclusion, Qwen3.5-27B is more expensive than MAI-Code-1.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MAI-Code-1.1-Flash has 111.0B more parameters than Qwen3.5-27B, making it 411.1% larger.
Context Window
Maximum input and output token capacity
Qwen3.5-27B accepts 262,144 input tokens compared to MAI-Code-1.1-Flash's 256,000 tokens. Only Qwen3.5-27B specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both MAI-Code-1.1-Flash and Qwen3.5-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MAI-Code-1.1-Flash
Qwen3.5-27B
License
Usage and distribution terms
MAI-Code-1.1-Flash is licensed under a proprietary license, while Qwen3.5-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
MAI-Code-1.1-Flash was released on 2026-08-11, while Qwen3.5-27B was released on 2026-02-24.
MAI-Code-1.1-Flash is 6 months newer than Qwen3.5-27B.
Aug 11, 2026
1 months ago
5mo newerFeb 24, 2026
6 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
MAI-Code-1.1-Flash is available from GitHub Copilot. Qwen3.5-27B is available from DeepInfra, Novita.
MAI-Code-1.1-Flash
Qwen3.5-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against MAI-Code-1.1-Flash and Qwen3.5-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about MAI-Code-1.1-Flash vs Qwen3.5-27B.