GPT OSS 120B High vs LongCat-Flash-Thinking-2601
LongCat-Flash-Thinking-2601 leads the LLM Stats Score 35.4 to 25.4. GPT OSS 120B High is 2.6x cheaper per token.
OpenAI · Meituan · Updated for 2026
Which is better?
LongCat-Flash-Thinking-2601 leads the overall LLM Stats Score 35.4 to 25.4, ranking #99 overall.
The models split the 2 individual benchmarks reported for both models evenly.
On price, GPT OSS 120B High is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT OSS 120B High also accepts a larger context window (131,072 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 GPT OSS 120B High
- cost matters — it's about 2.6x cheaper per token
- you process long inputs — it offers a 131,072 token context window
Choose LongCat-Flash-Thinking-2601
- overall performance matters — it scores 35.4 and ranks #99 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you want the most recent training data — it shipped Jan 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GPT OSS 120B High · 11 for LongCat-Flash-Thinking-2601
GPT OSS 120B High outperforms in 1 benchmarks (GPQA), while LongCat-Flash-Thinking-2601 is better at 1 benchmark (AIME 2025).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT OSS 120B High ($0.10/1M tokens) is 3.0x cheaper than LongCat-Flash-Thinking-2601 ($0.30/1M tokens).
For output processing, GPT OSS 120B High ($0.50/1M tokens) is 2.4x cheaper than LongCat-Flash-Thinking-2601 ($1.20/1M tokens).
In conclusion, LongCat-Flash-Thinking-2601 is more expensive than GPT OSS 120B High.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
LongCat-Flash-Thinking-2601 has 443.2B more parameters than GPT OSS 120B High, making it 379.5% larger.
Context Window
Maximum input and output token capacity
GPT OSS 120B High accepts 131,072 input tokens compared to LongCat-Flash-Thinking-2601's 128,000 tokens. GPT OSS 120B High can generate longer responses up to 131,072 tokens, while LongCat-Flash-Thinking-2601 is limited to 128,000 tokens.
License
Usage and distribution terms
GPT OSS 120B High is licensed under Apache 2.0, while LongCat-Flash-Thinking-2601 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
GPT OSS 120B High was released on 2025-08-05, while LongCat-Flash-Thinking-2601 was released on 2026-01-14.
LongCat-Flash-Thinking-2601 is 5 months newer than GPT OSS 120B High.
Aug 5, 2025
1.2 years ago
Jan 14, 2026
8 months ago
5mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT OSS 120B High is available from OpenAI, Fireworks. LongCat-Flash-Thinking-2601 is available from Meituan.
GPT OSS 120B High
LongCat-Flash-Thinking-2601
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT OSS 120B High and LongCat-Flash-Thinking-2601 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT OSS 120B High vs LongCat-Flash-Thinking-2601.