Beam 1.0 vs LongCat-Flash-Thinking
Beam 1.0 leads the LLM Stats Score 44.6 to 28.2. LongCat-Flash-Thinking is 1.1x cheaper per token.
Reflection AI · Meituan · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to 28.2, ranking #46 overall.
In the 3 individual benchmarks reported for both models, Beam 1.0 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, LongCat-Flash-Thinking is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Beam 1.0 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 Beam 1.0
- overall performance matters — it scores 44.6 and ranks #46 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose LongCat-Flash-Thinking
- cost matters — it's about 1.1x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for Beam 1.0 · 14 for LongCat-Flash-Thinking
Beam 1.0 outperforms in 3 benchmarks (GPQA, LiveCodeBench, SWE-Bench Verified), while LongCat-Flash-Thinking is better at 0 benchmarks.
Beam 1.0 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, Beam 1.0 ($0.40/1M tokens) is 1.3x more expensive than LongCat-Flash-Thinking ($0.30/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) costs the same as LongCat-Flash-Thinking ($1.20/1M tokens).
In conclusion, Beam 1.0 is more expensive than LongCat-Flash-Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
LongCat-Flash-Thinking has 58.7B more parameters than Beam 1.0, making it 11.7% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to LongCat-Flash-Thinking's 128,000 tokens. Beam 1.0 can generate longer responses up to 131,072 tokens, while LongCat-Flash-Thinking is limited to 128,000 tokens.
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while LongCat-Flash-Thinking uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while LongCat-Flash-Thinking was released on 2025-09-22.
Beam 1.0 is 13 months newer than LongCat-Flash-Thinking.
Oct 16, 2026
3 days ago
1.1yr newerSep 22, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while LongCat-Flash-Thinking's cutoff date is not specified.
We can confirm Beam 1.0's training data extends to 2026-06-30, but cannot make a direct comparison without LongCat-Flash-Thinking's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. LongCat-Flash-Thinking is available from Meituan.
Beam 1.0
LongCat-Flash-Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and LongCat-Flash-Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs LongCat-Flash-Thinking.