GLM-5.3-Flash vs MAI-Code-1.1-Flash
GLM-5.3-Flash leads the LLM Stats Score 50.7 to 28.3. GLM-5.3-Flash is 1.9x cheaper per token.
Zhipu AI · Microsoft · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 50.7 to 28.3, ranking #16 overall.
In the 1 individual benchmarks reported for both models, GLM-5.3-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash 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 GLM-5.3-Flash
- overall performance matters — it scores 50.7 and ranks #16 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
- cost matters — it's about 1.9x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose MAI-Code-1.1-Flash
- you want predictable pricing at $0.20/M input and $1.20/M output
At a glance
The differences that matter most.
Individual benchmarks
15 reported for GLM-5.3-Flash · 2 for MAI-Code-1.1-Flash
GLM-5.3-Flash outperforms in 1 benchmarks (Terminal-Bench 2.1), while MAI-Code-1.1-Flash is better at 0 benchmarks.
GLM-5.3-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, GLM-5.3-Flash ($0.15/1M tokens) is 1.3x cheaper than MAI-Code-1.1-Flash ($0.20/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 2.4x cheaper than MAI-Code-1.1-Flash ($1.20/1M tokens).
In conclusion, MAI-Code-1.1-Flash is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 182.0B more parameters than MAI-Code-1.1-Flash, making it 131.9% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to MAI-Code-1.1-Flash's 256,000 tokens. Only GLM-5.3-Flash specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and MAI-Code-1.1-Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
MAI-Code-1.1-Flash
License
Usage and distribution terms
GLM-5.3-Flash 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
GLM-5.3-Flash was released on 2026-08-26, while MAI-Code-1.1-Flash was released on 2026-08-11.
GLM-5.3-Flash is 1 month newer than MAI-Code-1.1-Flash.
Aug 26, 2026
1 weeks ago
2w newerAug 11, 2026
3 weeks 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
GLM-5.3-Flash is available from DeepInfra, FriendliAI, Novita, ZAI. MAI-Code-1.1-Flash is available from GitHub Copilot.
GLM-5.3-Flash
MAI-Code-1.1-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and MAI-Code-1.1-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs MAI-Code-1.1-Flash.