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longcat · Open weights

LongCat 2.0 Thinking

Added Jul 21, 2026Open weights

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Use in the app

Meituan's LongCat 2.0 Thinking is the reasoning-enabled variant for harder coding, tool use, multi-step reasoning, and long-context agent workflows.

  • Reasoning
  • Tools
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.75
Output
$3.00
Cache read
$0.015

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K
Average output (7 days)
1.1K tokens
Longer than 73% of 468 models

Side by side

LongCat 2.0 Thinking compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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LongCat 2.0 ThinkinglongcatThis modelDeepSeek V4 Flash 0423DeepSeekRemove
ProviderlongcatDeepSeek
Discovery tierOpen weightsOpen weights
AddedJul 21, 2026Oct 7, 2026
Limits
Context window1M1.05MBest
Max output131.07K—
Average output1.1K tokens245 tokens
Pricing, USD per 1M tokens
Input$0.75$0.14Best
Output$3.00$0.28Best
Cache read$0.015Best$0.028
Performance
Throughput57.2 tok/s92.6 tok/sBest
Time to first token4.0 s2.5 sBest
Request success (24h)100.0%Best98.5%
Benchmark indexes
Intelligence Index19.1Best18.9
Coding Index45.3—
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputNoYes
Reasoning effort—none, low, high, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • LongCat 2.0 Thinking
  • DeepSeek V4 Flash 0423

Intelligence Index

  • LongCat 2.0 Thinking19.1
  • DeepSeek V4 Flash 042318.9

LongCat 2.0 Thinking: Better than 48% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • LongCat 2.0 Thinking: 45.3
    • DeepSeek V4 Flash 0423: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • LongCat 2.0 Thinking: 4.5%
    • DeepSeek V4 Flash 0423: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • LongCat 2.0 Thinking: 0.5%
    • DeepSeek V4 Flash 0423: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • LongCat 2.0 Thinking: 739 Elo
    • DeepSeek V4 Flash 0423: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • LongCat 2.0 Thinking: 869 Elo
    • DeepSeek V4 Flash 0423: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • LongCat 2.0 Thinking: 6.4%
    • DeepSeek V4 Flash 0423: —

  • AA-LCRv1.1

    Reasoning across long documents

    • LongCat 2.0 Thinking: 65.0%Top
    • DeepSeek V4 Flash 0423: 41.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • LongCat 2.0 Thinking: 33.7%Top
    • DeepSeek V4 Flash 0423: 7.8%

  • GPQA Diamond

    Graduate-level science questions

    • LongCat 2.0 Thinking: 78.0%Top
    • DeepSeek V4 Flash 0423: 71.6%

  • IFBench

    Following precise output instructions

    • LongCat 2.0 Thinking: —
    • DeepSeek V4 Flash 0423: 47.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • LongCat 2.0 Thinking: 0.0%
    • DeepSeek V4 Flash 0423: —

  • SciCode

    Scientific computing in Python

    • LongCat 2.0 Thinking: 36.3%
    • DeepSeek V4 Flash 0423: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • LongCat 2.0 Thinking: —
    • DeepSeek V4 Flash 0423: 34.1%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • LongCat 2.0 Thinking: —
    • DeepSeek V4 Flash 0423: 94.4%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • LongCat 2.0 Thinking: 65.0%Top
    • DeepSeek V4 Flash 0423: 41.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
57.2 tok/s
Faster than 40% of 258 models
Time to first token
4.0 s
Quicker than 44% of 258 models
Prompt cache hits
20.8%
About $0.60 per 1M input tokens with caching
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "longcat-2.0:thinking",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="longcat-2.0:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

longcat

Other longcat models, starting with the same family.