Model Comparison
Llama 4 Scout vs LongCat-Flash-Thinking-2601Which is better in 2026?
LongCat-Flash-Thinking-2601 significantly outperforms across most benchmarks. Llama 4 Scout is 3.9x cheaper per token.
Verdict: Llama 4 Scout vs LongCat-Flash-Thinking-2601 — which is better?
Llama 4 Scout (by Meta) 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.
Llama 4 Scout outperforms in 0 benchmarks, while LongCat-Flash-Thinking-2601 is better at 2 benchmarks (GPQA, LiveCodeBench). LongCat-Flash-Thinking-2601 significantly outperforms across most benchmarks.
On price, Llama 4 Scout is roughly 3.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 4 Scout also accepts a larger context window (10,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose Llama 4 Scout if…
- cost matters — it's about 3.9x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
Choose LongCat-Flash-Thinking-2601 if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- you want the most recent training data — it shipped Jan 2026
Performance Benchmarks
Comparative analysis across standard metrics
Llama 4 Scout outperforms in 0 benchmarks, while LongCat-Flash-Thinking-2601 is better at 2 benchmarks (GPQA, LiveCodeBench).
LongCat-Flash-Thinking-2601 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 4 Scout ($0.08/1M tokens) is 3.8x cheaper than LongCat-Flash-Thinking-2601 ($0.30/1M tokens).
For output processing, Llama 4 Scout ($0.30/1M tokens) is 4.0x cheaper than LongCat-Flash-Thinking-2601 ($1.20/1M tokens).
In conclusion, LongCat-Flash-Thinking-2601 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
LongCat-Flash-Thinking-2601 has 451.0B more parameters than Llama 4 Scout, making it 413.8% larger.
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to LongCat-Flash-Thinking-2601's 128,000 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while LongCat-Flash-Thinking-2601 is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Scout supports multimodal inputs, whereas LongCat-Flash-Thinking-2601 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
Llama 4 Scout
LongCat-Flash-Thinking-2601
License
Usage and distribution terms
Llama 4 Scout is licensed under Llama 4 Community License Agreement, while LongCat-Flash-Thinking-2601 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 4 Community License Agreement
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Llama 4 Scout was released on 2025-04-05, while LongCat-Flash-Thinking-2601 was released on 2026-01-14.
LongCat-Flash-Thinking-2601 is 9 months newer than Llama 4 Scout.
Apr 5, 2025
1.2 years ago
Jan 14, 2026
5 months ago
9mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together. LongCat-Flash-Thinking-2601 is available from Meituan.
Llama 4 Scout
LongCat-Flash-Thinking-2601
Outputs Comparison
Key Takeaways
Detailed Comparison
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FAQ
Common questions about Llama 4 Scout vs LongCat-Flash-Thinking-2601.