Model Comparison
GLM-5 vs LongCat-Flash-Thinking-2601Which is better in 2026?
GLM-5 significantly outperforms across most benchmarks. LongCat-Flash-Thinking-2601 is 3.0x cheaper per token.
Verdict: GLM-5 vs LongCat-Flash-Thinking-2601 — which is better?
GLM-5 (by Zhipu AI) and LongCat-Flash-Thinking-2601 (by Meituan) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
GLM-5 outperforms in 2 benchmarks (BrowseComp, SWE-Bench Verified), while LongCat-Flash-Thinking-2601 is better at 0 benchmarks. GLM-5 significantly outperforms across most benchmarks.
On price, LongCat-Flash-Thinking-2601 is roughly 3.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5 also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-5 if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- you process long inputs — it offers a 200,000 token context window
- you want the most recent training data — it shipped Feb 2026
Choose LongCat-Flash-Thinking-2601 if…
- cost matters — it's about 3.0x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 outperforms in 2 benchmarks (BrowseComp, SWE-Bench Verified), while LongCat-Flash-Thinking-2601 is better at 0 benchmarks.
GLM-5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5 ($1.00/1M tokens) is 3.3x more expensive than LongCat-Flash-Thinking-2601 ($0.30/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 2.7x more expensive than LongCat-Flash-Thinking-2601 ($1.20/1M tokens).
In conclusion, GLM-5 is more expensive than LongCat-Flash-Thinking-2601.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 184.0B more parameters than LongCat-Flash-Thinking-2601, making it 32.9% larger.
Context Window
Maximum input and output token capacity
GLM-5 accepts 200,000 input tokens compared to LongCat-Flash-Thinking-2601's 128,000 tokens. Both models can generate responses up to 128,000 tokens.
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while LongCat-Flash-Thinking-2601 was released on 2026-01-14.
GLM-5 is 1 month newer than LongCat-Flash-Thinking-2601.
Feb 11, 2026
4 months ago
4w newerJan 14, 2026
5 months 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 is available from FriendliAI, ZAI. LongCat-Flash-Thinking-2601 is available from Meituan.
GLM-5
LongCat-Flash-Thinking-2601
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
Detailed Comparison
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FAQ
Common questions about GLM-5 vs LongCat-Flash-Thinking-2601.