GLM-5 vs LongCat-2.5
LongCat-2.5 leads the LLM Stats Score 50.0 to 36.6. LongCat-2.5 is 3.0x cheaper per token.
Zhipu AI · Meituan · Updated for 2026
Which is better?
LongCat-2.5 leads the overall LLM Stats Score 50.0 to 36.6, ranking #20 overall.
In the 2 individual benchmarks reported for both models, LongCat-2.5 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, LongCat-2.5 is roughly 3.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
LongCat-2.5 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
- you need open weights you can self-host or fine-tune
Choose LongCat-2.5
- overall performance matters — it scores 50.0 and ranks #20 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 3.0x 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 Nov 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
5 reported for GLM-5 · 23 for LongCat-2.5
GLM-5 outperforms in 0 benchmarks, while LongCat-2.5 is better at 2 benchmarks (BrowseComp, MCP Atlas).
LongCat-2.5 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 ($1.00/1M tokens) is 3.3x more expensive than LongCat-2.5 ($0.30/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 2.7x more expensive than LongCat-2.5 ($1.20/1M tokens).
In conclusion, GLM-5 is more expensive than LongCat-2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
LongCat-2.5 has 856.0B more parameters than GLM-5, making it 115.1% larger.
Context Window
Maximum input and output token capacity
LongCat-2.5 accepts 1,048,576 input tokens compared to GLM-5's 200,000 tokens. LongCat-2.5 can generate longer responses up to 131,072 tokens, while GLM-5 is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
LongCat-2.5 supports multimodal inputs, whereas GLM-5 does not.
LongCat-2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
LongCat-2.5
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while LongCat-2.5 was released on 2026-11-01.
LongCat-2.5 is 9 months newer than GLM-5.
Feb 11, 2026
8 months ago
Nov 1, 2026
0 days ago
8mo 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 is available from FriendliAI, ZAI. LongCat-2.5 is available from Meituan.
GLM-5
LongCat-2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5 and LongCat-2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5 vs LongCat-2.5.