LongCat-2.0 and GPT-5.5: Documented Architecture, Access, and Cost Differences

Architecture and Transparency

LongCat-2.0 is fully documented in a public technical report with detailed descriptions of the ScMoE architecture, Zero-Computation Experts, LSA sparse attention, N-gram Embedding, and MOPD multi-teacher distillation. All training details — 35T+ tokens, 50,000 domestic ASICs, DORA RL across 10,000+ environments — are publicly disclosed. GPT-5.5 is a proprietary frontier model; OpenAI has not disclosed its parameter count, architecture details, or training hardware. This structural difference means LongCat-2.0 can be independently audited, replicated, and self-hosted, while GPT-5.5 can only be accessed through OpenAI's API.

Pricing and Cost Structure

LongCat-2.0 is substantially cheaper: $0.30 per million input tokens and $1.20 per million output tokens via OpenRouter — approximately 16× cheaper on input and 25× cheaper on output compared to GPT-5.5's $5.00 and $30.00 respectively (standard API pricing). LongCat-2.0 also provides free cache hits, while GPT-5.5 charges $0.50 per million tokens for cached input. For agentic workloads where the same codebase or document context is repeatedly processed, LongCat-2.0's free caching creates an even larger cost advantage.

However, GPT-5.5 offers Batch API pricing at half the standard rate ($2.50/$15.00) for non-time-sensitive workloads, and Flex pricing for variable-demand scenarios. LongCat-2.0's OpenRouter pricing is fixed and does not currently offer volume discounts through that channel. Self-hosting LongCat-2.0 eliminates per-token costs entirely but requires significant GPU infrastructure (16× H20 GPUs minimum for FP8 inference).

Deployment and Access

LongCat-2.0 offers three access paths: (1) the LongCat API platform with OpenAI and Anthropic-compatible endpoints, (2) OpenRouter with pay-per-token pricing and unified API routing, and (3) self-hosted deployment via SGLang, vLLM, or Hugging Face Transformers using the open-source MIT-licensed weights. This flexibility allows developers to choose between convenience and cost optimization.

GPT-5.5 is available exclusively through OpenAI's API (Chat Completions and Responses endpoints) and through ChatGPT (Plus/Pro/Enterprise subscriptions). Self-hosting is not possible. OpenAI provides significantly more mature tooling — the Responses API supports web search, file search, code interpreter, computer use, MCP, and structured outputs — while LongCat-2.0's tool ecosystem relies on third-party harness platforms like Hermes, Claude Code, and OpenClaw.

License and Ownership

LongCat-2.0 is released under the MIT License — one of the most permissive open-source licenses, allowing commercial use, modification, redistribution, and distillation without restrictions. Organizations can fine-tune the model on proprietary data, deploy it on private infrastructure, and distribute derivative works.

GPT-5.5 is a proprietary closed model. Users access it through OpenAI's API under commercial terms of service. Fine-tuning is not supported. The model cannot be inspected, modified, or self-hosted. This distinction is critical for regulated industries (finance, healthcare, defense) where data sovereignty and model auditability are requirements.

Ecosystem and Tools

GPT-5.5 operates in the mature OpenAI ecosystem with native support for structured outputs, function calling, streaming, web search, file search, code interpreter, computer use, and MCP servers — all via a single Responses API. The model is deeply integrated into ChatGPT, Codex, and the OpenAI developer platform with comprehensive documentation and SDK support.

LongCat-2.0 integrates through third-party developer tools: Hermes (agent workspace, #1 by monthly call volume), Claude Code (#2), and OpenClaw (#3). The OpenAI and Anthropic-compatible API endpoints allow it to function as a drop-in replacement in many existing applications without code changes. However, it lacks GPT-5.5's native multi-tool orchestration and does not support multimodal input.