[whatmodel.sh](../../README.md) / LongCat 2.0 (1.6T-A48B) # LongCat 2.0 (1.6T-A48B) Meituan · released 2026-06 · 1600B params (48B active) · mit **score 71.7** ±1.9 · #63 of 732 · from 7 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks LongCat 2.0 (1.6T-A48B) among every model on that task or test, worst to best.
task            vs the others            rank  counts  tests
agent tasks     ▁▁▃▆█▄▅▅●▄▂▁      #72 of 313*     24%      6
coding                         not ranked yet       —      2
chat & writing  ▁▁▁▃▆▄▆█●▇▇▃     #137 of 402*    5.3%      1
math            ▁▁▃▅█▆▄█●▂▁▁      #54 of 431*     15%      1
web search      ▁▁▁▅▄●▃▄·▁·▁       #40 of 84*     13%      2
agent tasks · #72 of 313 · 24% of its score Trails [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [Qwen3.5 397B-A17B](qwen3-5-397b-a17b.md), [GLM-5.1](glm-5-1.md) here, though they rank lower overall.
test                                result  vs the others in its run  difficulty            counts  why
RWSearch (maker)                     78.8%  ██●█··█····█ beats 2/5    medium · avg 45%         13%  typical
EEGAgentBench overall (tool-based…   44.5%  ▂·▁▁··●▇█▂·▁ beats 9/25   hard · avg 28%           11%  blunt · at its level
SWE-bench Multilingual (maker)       77.3%  ▁··▁▃▁▅█●▂▂▁ beats 51/65  hard · avg 18%             —  not counted yet: no other model in the …
Terminal-Bench 2.1 (maker)           70.8%  ▁·▁·▁▁▄▃▆●▅█ beats 39/63  very hard · avg 7.2%       —  not counted yet: no other model in the …
BrowseComp (maker)                   79.9%  ▂·▃▁▂▂▆▅█●▂▄ beats 38/46  very hard · avg 11%        —  not counted yet: no other model in the …
SWE-bench Pro (maker)                59.5%  ▄▂▂▂▄▂▆●·▂·▂ beats 14/16                             —  not counted yet: no other model in the …
coding · 0.0% of its score
test                            result  vs the others in its run  difficulty      counts  why
SWE-bench Multilingual (maker)   77.3%  ▁··▁▃▁▅█●▂▂▁ beats 51/65  hard · avg 18%       —  not counted yet: no other model in the …
SWE-bench Pro (maker)            59.5%  ▄▂▂▂▄▂▆●·▂·▂ beats 14/16                       —  not counted yet: no other model in the …
chat & writing · #137 of 402 · 5.3% of its score Trails [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [Nemotron-Cascade-8B](nemotron-cascade-8b.md) here, though they rank lower overall.
test                                result  vs the others in its run  difficulty      counts  why
IFEval (aggregation unspecified) …   90.0%  █·█●·█···█·█ beats 2/5    easy · avg 72%    5.3%  typical
math · #54 of 431 · 15% of its score Trails [A.X K2 (688B-A33B)](ax-k2.md), [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [GPT-5](gpt-5.md) here, though they rank lower overall.
test                     result  vs the others in its run  difficulty      counts  why
IMO-AnswerBench (maker)   81.8%  ▁▁·▁▁▁▄▆●▄▂▃ beats 28/44  hard · avg 25%     15%  typical
web search · #40 of 84 · 13% of its score Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Gemini 3.5 Flash](gemini-3-5-flash.md), [Qwen3.7 Max](qwen3-7-max.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test                result  vs the others in its run  difficulty           counts  why
RWSearch (maker)     78.8%  ██●█··█····█ beats 2/5    medium · avg 45%        13%  typical
BrowseComp (maker)   79.9%  ▂·▃▁▂▂▆▅█●▂▄ beats 38/46  very hard · avg 11%       —  not counted yet: no other model in the …
other tests (3)
test                      result  vs the others in its run    difficulty        counts  why
GPQA Diamond (maker)       88.9%  ▁▁▁▁▂▄▅▅██●▄ beats 265/322  hard · avg 39%       28%  sharp · small run
FORTE (AGI-Eval) (maker)   73.2%  ▄···●·····▄█ beats 2/5      medium · avg 38%     14%  at its level
WritingBench (maker)       83.8%  ▁·▁▂▁··●█▂▂▁ beats 8/20     medium · avg 54%     13%  typical
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)
source                                    results  share of the score
meituan-longcat/LongCat-2.0 model card …        6   88% ▇▇▇▇▇▇▇▇▇▇▇
EEGAgentBench: Benchmarking LLM Agents …        1   11% ▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 71.7
Licences: meituan-longcat/LongCat-2.0 model card ([mit](../../licenses/meituan-longcat--LongCat-2.0.txt)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
Not counted (4)
source                  test                    score  why
meituan-longcat/LongC…  SWE-bench Pro           59.5%  too few models have it yet
meituan-longcat/LongC…  BrowseComp              79.9%  too few models have it yet
meituan-longcat/LongC…  SWE-bench Multilingual  77.3%  too few models have it yet
meituan-longcat/LongC…  Terminal-Bench 2.1      70.8%  too few models have it yet
## How to run it ### Through an API api (LongCat API Platform (Meituan)) · $1.30/M blended · $0.75 in / $2.95 out per 1M tokens · [longcat.chat](https://longcat.chat/platform/docs/pricing/longcat-2.0) [price source](https://longcat.chat/platform/docs/pricing/longcat-2.0): USD per 1M tokens, LongCat API Platform pay-as-you-go list price for LongCat-2.0 (uncached input / output). A limited-time discounted price ($0.30 / $1.20) was also shown and is not used; cached input $0.015/1M excluded; CNY price table not used. Currency: USD. Meituan LongCat official pricing page, observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/meituan/longcat-2.0.toml) ### Other ways to run it 1. Run it as published (fp8, needs ~2051.18 GB, NVIDIA only, from [docs.sglang.io/cookbook](https://docs.sglang.io/cookbook/autoregressive/Meituan/LongCat-2.0)) ```sh sglang serve --trust-remote-code --model-path meituan-longcat/LongCat-2.0-FP8 --tp 8 --ep 8 --max-running-requests 64 --mem-fraction-static 0.92 --chunked-prefill-size 2048 --dsa-prefill-backend fa3 --kv-cache-dtype bfloat16 --model-loader-extra-config '{"enable_multithread_load":true,"num_threads":12}' --host 0.0.0.0 --port 30000 ``` Model repository and documentation: [huggingface.co/meituan-longcat](https://huggingface.co/meituan-longcat/LongCat-2.0) [interactive version](https://whatmodel.sh/model/longcat-2-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)