GLM-5.3-Flash vs Mercury 3
GLM-5.3-Flash leads the LLM Stats Score 48.8 to 36.1. Mercury 3 is 1.5x cheaper per token.
Zhipu AI · Inception · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 48.8 to 36.1, ranking #28 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, Mercury 3 is roughly 1.5x 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 48.8 and ranks #28 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
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Mercury 3
- cost matters — it's about 1.5x cheaper per token
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
15 reported for GLM-5.3-Flash · 12 for Mercury 3
GLM-5.3-Flash outperforms in 1 benchmarks (GDPval-AA), while Mercury 3 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 1.5x more expensive than Mercury 3 ($0.10/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 1.4x more expensive than Mercury 3 ($0.35/1M tokens).
In conclusion, GLM-5.3-Flash is more expensive than Mercury 3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Mercury 3's 1,000,000 tokens. GLM-5.3-Flash can generate longer responses up to 1,048,576 tokens, while Mercury 3 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-5.3-Flash supports multimodal inputs, whereas Mercury 3 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3-Flash
Mercury 3
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Mercury 3 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Mercury 3 was released on 2026-10-19.
Mercury 3 is 2 months newer than GLM-5.3-Flash.
Aug 26, 2026
1 months ago
Oct 19, 2026
1 days 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-5.3-Flash is available from DeepInfra, FriendliAI, Novita, ZAI. Mercury 3 is available from Inception.
GLM-5.3-Flash
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Mercury 3.