GLM-5.3-Flash vs Inkling-Small
GLM-5.3-Flash leads the LLM Stats Score 51.6 to 39.3. GLM-5.3-Flash is 2.2x cheaper per token.
Zhipu AI · Thinking Machines Lab · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 39.3, ranking #11 overall.
In the 6 individual benchmarks reported for both models, GLM-5.3-Flash wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 2.2x 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 6 of 6 exact shared results
- cost matters — it's about 2.2x 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 Inkling-Small
- you want predictable pricing at $0.30/M input and $1.20/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 · 24 for Inkling-Small
GLM-5.3-Flash outperforms in 6 benchmarks (Artificial Analysis, CharXiv-R, GDPval-AA, Humanity's Last Exam, Terminal-Bench 2.1, Toolathlon), while Inkling-Small 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 2.0x cheaper than Inkling-Small ($0.30/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 2.4x cheaper than Inkling-Small ($1.20/1M tokens).
In conclusion, Inkling-Small is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 44.0B more parameters than Inkling-Small, making it 15.9% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash 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-5.3-Flash is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Inkling-Small support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Inkling-Small
License
Usage and distribution terms
GLM-5.3-Flash 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-5.3-Flash was released on 2026-08-26, while Inkling-Small was released on 2026-07-30.
GLM-5.3-Flash is 1 month newer than Inkling-Small.
Aug 26, 2026
2 days ago
3w newerJul 30, 2026
4 weeks 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. Inkling-Small is available from Thinking Machines Lab.
GLM-5.3-Flash
Inkling-Small
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Inkling-Small side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Inkling-Small.