Kwaipilot の KAT-Coder-Pro V2.5 は、自律型ソフトウェア開発、複数ファイルのリファクタリング、高度なデバッグ向けに設計されたAIモデルです。256K のコンテキストウィンドウ、毎秒 80 トークンの生成速度、知能指数 85/100（1Mトークンあたり入力$0.5・出力$2）を実現しています。
Technical Specifications
- Provider
- Kwaipilot
- Category
- flagship
- Release Date
- 2026-07
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- API Pay-as-you-go
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- kat-coder-pro-v2-5
- Supported SDK & API Protocol
- Kwaipilot REST API (OpenAI-Compatible)
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.5/1M in) across full 256K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.05/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- API Prompt Engineering & RAG Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Kwaipilot Cloud API
- System Card & Safety Framework
- Kwaipilot Production Safety & Alignment Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
KAT-Coder-Pro V2.5 Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~215 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to KAT-Coder-Pro V2.5:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 58%
- GPQA Diamond
- 67%
- MMLU-Pro
- 81%
- HumanEval
- 93%
- Chatbot Arena Elo
- 1360pts
Strengths
- 256K のコンテキストウィンドウと最大 16K トークンの出力容量（SWE-bench Verified 58% · GPQA Diamond 67%）
- 毎秒 80 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 510ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.5・出力$2）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Limitations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Editorial Assessment
知能指数 85/100、256K コンテキスト、毎秒 80 トークンの速度を兼ね備えた KAT-Coder-Pro V2.5 は、Kwaipilot のラインナップにおいて非常に実用性の高い主力モデルです。
Best for: 自律型ソフトウェア開発、複数ファイルのリファクタリング、高度なデバッグ、エンタープライズRAG基盤、スケーラブルな本番API統合に最適。
Compare KAT-Coder-Pro V2.5
Frequently Asked Questions about KAT-Coder-Pro V2.5
How much does the KAT-Coder-Pro V2.5 API cost per 1M tokens?+
KAT-Coder-Pro V2.5 by Kwaipilot is priced at $0.5 per 1M input tokens and $2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.05 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the KAT-Coder-Pro V2.5 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in KAT-Coder-Pro V2.5's context window?+
KAT-Coder-Pro V2.5 features a 256K tokens context window, which equals approximately 192,000 English words or ~512 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 16,384 tokens.
Can I run KAT-Coder-Pro V2.5 locally on my own GPU (Open Weights)?+
No, KAT-Coder-Pro V2.5 is a proprietary closed-weights model hosted by Kwaipilot. It cannot be downloaded for local GPU execution and must be accessed via Kwaipilot's official API or cloud platform.
Does Kwaipilot train on data sent to the KAT-Coder-Pro V2.5 API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Kwaipilot Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Kwaipilot Cloud API & Enterprise Endpoints.
How does KAT-Coder-Pro V2.5 perform on coding and reasoning benchmarks?+
KAT-Coder-Pro V2.5 achieves a composite Intelligence Index of 85/100, scoring 58% on SWE-bench Verified, 67% on GPQA Diamond, 81% on MMLU-Pro, and 1360 pts on Chatbot Arena Elo.
What is KAT-Coder-Pro V2.5 best used for?+
KAT-Coder-Pro V2.5 is best suited for: 自律型ソフトウェア開発、複数ファイルのリファクタリング、高度なデバッグ、エンタープライズRAG基盤、スケーラブルな本番API統合に最適。. Assessment: 知能指数 85/100、256K コンテキスト、毎秒 80 トークンの速度を兼ね備えた KAT-Coder-Pro V2.5 は、Kwaipilot のラインナップにおいて非常に実用性の高い主力モデルです。
Kwaipilot Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Kwaipilot's flagship reasoning tier:
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to KAT-Coder-Pro V2.5:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.