GLM-5.2 vs Inkling-Small
GLM-5.2 leads the LLM Stats Score 46.5 to 39.3. Inkling-Small is 2.8x cheaper per token.
Zhipu AI · Thinking Machines Lab · Updated for 2026
Which is better?
GLM-5.2 leads the overall LLM Stats Score 46.5 to 39.3, ranking #22 overall.
In the 8 individual benchmarks reported for both models, GLM-5.2 wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, Inkling-Small is roughly 2.8x 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 46.5 and ranks #22 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 6 of 8 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
Choose Inkling-Small
- cost matters — it's about 2.8x cheaper per token
- 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 · 24 for Inkling-Small
GLM-5.2 outperforms in 6 benchmarks (AIME 2026, CritPT, GPQA, Humanity's Last Exam, SWE-Bench Pro, Terminal-Bench 2.1), while Inkling-Small is better at 2 benchmarks (MCP Atlas, Toolathlon).
GLM-5.2 shows notably better performance in the majority of 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 ($0.95/1M tokens) is 3.2x more expensive than Inkling-Small ($0.30/1M tokens).
For output processing, GLM-5.2 ($3.00/1M tokens) is 2.5x more expensive than Inkling-Small ($1.20/1M tokens).
In conclusion, GLM-5.2 is more expensive than Inkling-Small.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.2 has 477.0B more parameters than Inkling-Small, making it 172.8% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Inkling-Small's 256,000 tokens. Inkling-Small can generate longer responses up to 256,000 tokens, while GLM-5.2 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Inkling-Small supports multimodal inputs, whereas GLM-5.2 does not.
Inkling-Small can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Inkling-Small
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Inkling-Small 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-Small was released on 2026-07-30.
Inkling-Small is 1 month newer than GLM-5.2.
Jun 16, 2026
2 months ago
Jul 30, 2026
4 weeks 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-Small is available from Thinking Machines Lab.
GLM-5.2
Inkling-Small
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Inkling-Small side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Inkling-Small.