Model Comparison

GLM-5 vs LongCat-Flash-Thinking

GLM-5 significantly outperforms across most benchmarks. LongCat-Flash-Thinking is 3.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-5 outperforms in 1 benchmarks (SWE-Bench Verified), while LongCat-Flash-Thinking is better at 0 benchmarks.

GLM-5 significantly outperforms across most benchmarks.

Wed Jun 03 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

LongCat-Flash-Thinking costs less

For input processing, GLM-5 ($1.00/1M tokens) is 3.3x more expensive than LongCat-Flash-Thinking ($0.30/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 2.7x more expensive than LongCat-Flash-Thinking ($1.20/1M tokens).

In conclusion, GLM-5 is more expensive than LongCat-Flash-Thinking.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers

Wed Jun 03 2026 • llm-stats.com

GLM-5

Input tokens$1.00

Output tokens$3.20

Best providerFriendliAI

LongCat-Flash-Thinking

Input tokens$0.30

Output tokens$1.20

Best providerMeituan

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Model Size

Parameter count comparison

184.0B diff

GLM-5 has 184.0B more parameters than LongCat-Flash-Thinking, making it 32.9% larger.

GLM-5

744.0Bparameters

LongCat-Flash-Thinking

560.0Bparameters

744.0B

GLM-5

560.0B

LongCat-Flash-Thinking

Context Window

Maximum input and output token capacity

GLM-5 accepts 200,000 input tokens compared to LongCat-Flash-Thinking's 128,000 tokens. Both models can generate responses up to 128,000 tokens.

GLM-5

Input200,000 tokens

Output128,000 tokens

LongCat-Flash-Thinking

Input128,000 tokens

Output128,000 tokens

Wed Jun 03 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-5

MIT

Open weights

LongCat-Flash-Thinking

MIT

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while LongCat-Flash-Thinking was released on 2025-09-22.

GLM-5 is 5 months newer than LongCat-Flash-Thinking.

GLM-5

Feb 11, 2026

3 months ago

4mo newer

LongCat-Flash-Thinking

Sep 22, 2025

8 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

GLM-5 is available from FriendliAI, ZAI. LongCat-Flash-Thinking is available from Meituan.

GLM-5

FriendliAI

Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Unknown Organization

Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

LongCat-Flash-Thinking

Meituan

Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

GLM-5

View details

Zhipu AI

Larger context window (200,000 tokens)

Higher SWE-Bench Verified score (77.8% vs 59.4%)

LongCat-Flash-Thinking

View details

Meituan

Less expensive input tokens

Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature	GLM-5	LongCat-Flash-Thinking

FAQ

Common questions about GLM-5 vs LongCat-Flash-Thinking.

Which is better, GLM-5 or LongCat-Flash-Thinking?

GLM-5 significantly outperforms across most benchmarks. GLM-5 is made by Zhipu AI and LongCat-Flash-Thinking is made by Meituan. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-5 compare to LongCat-Flash-Thinking in benchmarks?

GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%.

Is GLM-5 cheaper than LongCat-Flash-Thinking?

LongCat-Flash-Thinking is 3.3x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via friendli. LongCat-Flash-Thinking costs $0.30/M input and $1.20/M output via meituan.

What are the context window sizes for GLM-5 and LongCat-Flash-Thinking?

GLM-5 supports 200K tokens and LongCat-Flash-Thinking supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-5 and LongCat-Flash-Thinking?

Key differences include context window (200K vs 128K), input pricing ($1.00 vs $0.30/M). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and LongCat-Flash-Thinking?

GLM-5 is developed by Zhipu AI and LongCat-Flash-Thinking is developed by Meituan.