GLM-5.3-Flash vs Kimi K2-Thinking-0905
GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is 3.6x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
GLM-5.3-Flash outperforms in 1 benchmarks (Humanity's Last Exam), while Kimi K2-Thinking-0905 is better at 0 benchmarks. GLM-5.3-Flash significantly outperforms across most benchmarks.
On price, GLM-5.3-Flash is roughly 3.6x 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 benchmark, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 3.6x 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
Choose Kimi K2-Thinking-0905
- you want predictable pricing at $0.47/M input and $2.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash outperforms in 1 benchmarks (Humanity's Last Exam), while Kimi K2-Thinking-0905 is better at 0 benchmarks.
GLM-5.3-Flash significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 3.1x cheaper than Kimi K2-Thinking-0905 ($0.47/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 4.0x cheaper than Kimi K2-Thinking-0905 ($2.00/1M tokens).
In conclusion, Kimi K2-Thinking-0905 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2-Thinking-0905 has 680.0B more parameters than GLM-5.3-Flash, making it 212.5% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Kimi K2-Thinking-0905's 262,144 tokens. Kimi K2-Thinking-0905 can generate longer responses up to 262,144 tokens, while GLM-5.3-Flash is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3-Flash
Kimi K2-Thinking-0905
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Kimi K2-Thinking-0905 was released on 2025-09-05.
GLM-5.3-Flash is 12 months newer than Kimi K2-Thinking-0905.
Aug 26, 2026
1 days ago
11mo newerSep 5, 2025
11 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
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Kimi K2-Thinking-0905 is available from DeepInfra, Novita, Fireworks.
GLM-5.3-Flash
Kimi K2-Thinking-0905
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Kimi K2-Thinking-0905 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Kimi K2-Thinking-0905.