GLM-5.2 vs Inkling
GLM-5.2 leads the LLM Stats Score 44.9 to 37.1. GLM-5.2 is 1.5x cheaper per token.
Zhipu AI · Thinking Machines Lab · Updated for 2026
Which is better?
GLM-5.2 leads the overall LLM Stats Score 44.9 to 37.1, ranking #40 overall.
In the 3 individual benchmarks reported for both models, GLM-5.2 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.2 is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.2 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-5.2
- overall performance matters — it scores 44.9 and ranks #40 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 1.5x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose Inkling
- 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 · 16 for Inkling
GLM-5.2 outperforms in 3 benchmarks (AIME 2026, MCP Atlas, Terminal-Bench 2.1), while Inkling is better at 0 benchmarks.
GLM-5.2 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 1.3x cheaper than Inkling ($0.95/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 1.7x cheaper than Inkling ($4.05/1M tokens).
In conclusion, Inkling is more expensive than GLM-5.2.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Inkling has 222.0B more parameters than GLM-5.2, making it 29.5% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Inkling's 524,288 tokens. GLM-5.2 can generate longer responses up to 1,048,576 tokens, while Inkling is limited to 524,288 tokens.
Input capabilities
Documented input modalities across available providers
Inkling supports multimodal inputs, whereas GLM-5.2 does not.
Inkling can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Inkling
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Inkling uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-5.2 was released on 2026-06-16, while Inkling was released on 2026-07-21.
Inkling is 1 month newer than GLM-5.2.
Jun 16, 2026
3 months ago
Jul 21, 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. Inkling is available from DeepInfra.
GLM-5.2
Inkling
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Inkling side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Inkling.