GLM-5.2 vs Kimi K3
Kimi K3 leads the LLM Stats Score 52.7 to 45.5. GLM-5.2 is 4.9x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
Kimi K3 leads the overall LLM Stats Score 52.7 to 45.5, ranking #8 overall.
In the 11 individual benchmarks reported for both models, Kimi K3 wins 11; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.2 is roughly 4.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-5.2
- cost matters — it's about 4.9x cheaper per token
Choose Kimi K3
- overall performance matters — it scores 52.7 and ranks #8 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 11 of 11 exact shared results
- you want the most recent training data — it shipped Jul 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for GLM-5.2 · 31 for Kimi K3
GLM-5.2 outperforms in 0 benchmarks, while Kimi K3 is better at 11 benchmarks (DeepSWE, DeepSWE 1.1, FrontierSWE, GPQA, Humanity's Last Exam, MCP Atlas, PostTrainBench, Program Bench, SWE-Marathon, Terminal-Bench 2.1, Toolathlon).
Kimi K3 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-5.2 ($0.75/1M tokens) is 3.8x cheaper than Kimi K3 ($2.85/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 5.9x cheaper than Kimi K3 ($14.25/1M tokens).
In conclusion, Kimi K3 is more expensive than GLM-5.2.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K3 has 2047.0B more parameters than GLM-5.2, making it 271.8% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 1,048,576 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K3 supports multimodal inputs, whereas GLM-5.2 does not.
Kimi K3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Kimi K3
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Kimi K3 uses Kimi K3 License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Kimi K3 License
Open weights
Release Timeline
When each model was launched
GLM-5.2 was released on 2026-06-16, while Kimi K3 was released on 2026-07-16.
Kimi K3 is 1 month newer than GLM-5.2.
Jun 16, 2026
3 months ago
Jul 16, 2026
2 months ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. Kimi K3 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
GLM-5.2
Kimi K3
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Kimi K3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Kimi K3.