GLM-5.3-Flash vs Kimi K2.7 Code
GLM-5.3-Flash leads the LLM Stats Score 51.6 to 39.6. GLM-5.3-Flash is 6.0x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 39.6, ranking #11 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 6.0x 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 51.6 and ranks #11 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 6.0x 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 K2.7 Code
- you want predictable pricing at $0.74/M input and $3.50/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for GLM-5.3-Flash · 9 for Kimi K2.7 Code
GLM-5.3-Flash outperforms in 1 benchmarks (DeepSWE 1.1), while Kimi K2.7 Code 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 4.9x cheaper than Kimi K2.7 Code ($0.74/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 7.0x cheaper than Kimi K2.7 Code ($3.50/1M tokens).
In conclusion, Kimi K2.7 Code is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.7 Code 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 K2.7 Code's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Kimi K2.7 Code support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Kimi K2.7 Code
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Kimi K2.7 Code uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Kimi K2.7 Code was released on 2026-06-12.
GLM-5.3-Flash is 3 months newer than Kimi K2.7 Code.
Aug 26, 2026
2 days ago
2mo newerJun 12, 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
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
GLM-5.3-Flash
Kimi K2.7 Code
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Kimi K2.7 Code side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Kimi K2.7 Code.