📺 Watch the full video on YouTube

I Tested LongCat 2.0 And It's Insane

🐈 LongCat 2.0: The 1.6-Trillion Parameter MoE Agentic Frontier Model

LongCat 2.0 is an open-source, frontier-scale Mixture-of-Experts (MoE) language model boasting 1.6 trillion total parameters (with an average of 48 billion activated per token). Designed specifically for agentic coding, long-context repository understanding, and complex multi-hop reasoning, LongCat 2.0 is trained and served entirely on large-scale clusters of domestic AI ASIC superpods.

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🚀 Key Innovations

1. 1M-Token Context via LongCat Sparse Attention (LSA)

Traditional sparse attention mechanisms face performance bottlenecks during ultra-long input processing. LongCat 2.0 introduces LongCat Sparse Attention (LSA), offering three orthogonal efficiency improvements over standard sparse attention:

• Streaming-aware Indexing (SI): Reshapes token selection budgets to combine hardware-aligned contiguous access with dynamic random selection, ensuring high effective HBM bandwidth.
• Cross-Layer Indexing (CLI): Amortizes indexing overhead by sharing a single indexing pass across consecutive layers (and speculative Multi-Token Prediction steps) via cross-layer distillation.
• Hierarchical Indexing (HI): Shrinks the candidate indexer search space using a coarse-to-fine, two-stage scoring scheme for ultra-long context tasks.

2. 135B Parameter N-gram Embedding

LongCat 2.0 integrates an N-gram Embedding module configured to $N=5$, adding 135B parameters in sparse dimensions orthogonal to MoE. By expanding the embedding space by $\sim100\times$, it captures rich local context and accelerates generation by reducing large-batch decoding memory I/O.

3. Frontier Training on AI ASIC Superpods

• Scale: Pre-trained on over 50,000 AI ASICs across $35+$ trillion tokens without a single rollback or irrecoverable loss spike.
• 6D Parallelism & EMBP: Leverages TP, CP, EP, DP, PP, and a dedicated N-gram Embedding Parallelism (EMBP) to resolve per-device memory limits.
• Muon Optimizer: Deployed at scale with targeted optimizations for TP parallelism and symmetric matrix multiplication.

4. Specialized Expert Post-Training (MOPD)

Using Multi-expert Online Policy Distillation (MOPD), the post-training pipeline blends capabilities from three dedicated expert groups:

• Agent Experts: Optimized for tool invocation, parameter parsing, and self-correcting agent loops.
• Reasoning Experts: Tailored for advanced mathematics, STEM, and deep multi-hop logic.
• Interaction Experts: Designed for instruction following, alignment, and hallucination reduction.

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⚡ API Quick Start

LongCat 2.0 is fully compatible with mainstream API formats (OpenAI and Anthropic), allowing developers to drop it in place of existing setups.

• API Base URL (OpenAI Format): https://api.longcat.chat/openai
• API Base URL (Anthropic Format): https://api.longcat.chat/anthropic
• Supported Model Name: LongCat-2.0
• Max Input Context: 1,000,000 tokens
• Max Output Context: 131,072 tokens (128K)

OpenAI Python SDK Example

import openai

client = openai.OpenAI(
    base_url="https://api.longcat.chat/openai/v1",
    api_key="YOUR_LONGCAT_API_KEY"
)

response = client.chat.completions.create(
    model="LongCat-2.0",
    messages=[
        {"role": "user", "content": "Analyze our codebase for memory leaks."}
    ],
    temperature=0.2
)

print(response.choices[0].message.content)

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📊 Benchmark Evaluations

All evaluations are measured in-house under a unified harness unless marked with an asterisk (* for external reported metrics).

Benchmark	LongCat-2.0	Gemini 3.1 Pro	GPT-5.5	Claude Opus 4.6	Claude Opus 4.7	Claude Opus 4.8
Terminal-Bench 2.1	70.8	70.7*	73.8*	—	71.7*	78.9*
SWE-bench Pro	59.5	54.2*	58.6*	57.3*	64.3*	69.2*
SWE-bench Multilingual	77.3	76.9*	—	77.8*	80.5*	84.8*
FORTE †	73.2	70.3	77.8	73.2	77.6	77.2
BrowseComp	79.9	85.9*	84.4*	84.0*	79.3*	84.3*
RWSearch	78.8	76.3	85.3	81.3	79.3	77.3
IFEval	90.0	96.1	95.0	92.2	88.7	86.0
Writing Bench	83.8	83.7	84.7	—	85.3	85.2
IMO-AnswerBench	81.8	90.0	79.5	75.3*	81.8	75.3
GPQA-diamond	88.9	94.3*	93.6*	91.3*	94.2*	92.4

† FORTE (Full-cycle Office Real-world Task Evaluation) evaluates agents on daily productivity across 15 professions. Values are normalized on a 0–100 scale.

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📺 YouTube Video Feature: Multi-Year SEC 10-K Audit

In our latest YouTube deep-dive, we tested LongCat 2.0's massive 1M-token context window by feeding it multiple consecutive years of Microsoft (MSFT) Annual Reports (SEC Form 10-K). Because these documents contain hundreds of thousands of tokens of dense financial text and tables, typical LLMs truncate or fail to cross-reference them. LongCat 2.0 ingested them all natively.

📝 The Test Prompt

Read through the annual reports for this company. Compare the numbers and text across all the years to find any hidden money problems, lawsuits, or bad business trends, and list out the biggest warning signs.

🧠 LongCat 2.0 Audit Output (Summary)

Based on a detailed, cross-year analysis of Microsoft’s FY23, FY24, and FY25 reports, LongCat 2.0 successfully cross-referenced key sections (including Part I, Item 1A - Risk Factors, and Consolidated Financial Statements) to extract the following critical warning signs:

⚠️ Warning Sign 1: Exploding Capital Expenditures (CapEx) for AI Infrastructure

• Observation: Microsoft’s capital expenditures are accelerating at an unprecedented rate, primarily driven by building data centers, leasing space, and purchasing high-end AI accelerators (GPUs/ASICs) to support AI training and inference.
• Hidden Risk: LongCat pointed out that while revenues (especially Azure cloud) are growing, the depreciation costs and operational expenses associated with this massive physical footprint are growing faster. If the enterprise monetization curve for generative AI features (like Copilot) flattens or experiences delays, Microsoft risks significant margin compression or asset impairment write-downs due to overinvestment.

⚠️ Warning Sign 2: Escalating Global Antitrust and Regulatory Scrutiny

• Observation: Comparative text analysis reveals a significant escalation in the severity of language used regarding antitrust and regulatory risks.
• Hidden Risk: The model flagged warnings regarding:
  • Regulatory scrutiny over the strategic partnership and investments with OpenAI.
  • Antitrust challenges regarding the integration/bundling of collaboration software (e.g., Teams) with productivity suites (e.g., Office 365).
  • The long-term operational integration overhead and regulatory concessions made to clear the Activision Blizzard acquisition.
• Implication: A single major regulatory block or fine could force a change in Microsoft’s software-bundling strategies, impacting core SaaS margins.

⚠️ Warning Sign 3: Operational Margin Pressure and Macroeconomic Sensitivity

• Observation: Comparing year-over-year balance sheets, LongCat identified that the cost of services is outpacing licensing revenues.
• Hidden Risk: Operating margins are facing pressure from the high variable compute cost of serving real-time AI models. Additionally, enterprise IT budgets remain highly sensitive to global inflation and currency fluctuations, which could slow down cloud migration velocity just as MSFT’s fixed infrastructure costs peak.

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🔗 Reference Links

• Official Blog: Introducing LongCat-2.0
• API Platform & Documentation: LongCat Docs
• Online Chat Interface: longcat.chat

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