01.AI의 Yi-Large 모델은 32K 토큰 컨텍스트 윈도우와 입력 $2.5/1M 토큰, 출력 $2.5/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
Technical Specifications
- Provider
- 01.AI
- Category
- flagship
- Release Date
- 2024-05
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- API Pay-as-you-go
- Reasoning Effort Control
- Fixed
- 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)
- yi-large
- Supported SDK & API Protocol
- 01.AI REST API (OpenAI-Compatible)
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in 01.AI lineup)
- 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 ($2.5/1M in) across full 33K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.25/1M 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 01.AI Cloud API
- System Card & Safety Framework
- 01.AI Production Safety & Alignment Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Yi-Large Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~83 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 Yi-Large:
Benchmark Scores & 6-Axis Capability Radar
- GPQA Diamond
- 52%
- MMLU-Pro
- 76.5%
- HumanEval
- 83.5%
- Chatbot Arena Elo
- 1290pts
Strengths
- 01.AI의 첨단 Dense Bilingual Foundation Transformer 아키텍처와 최대 32K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2.5/1M, 출력 $2.5/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Limitations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Editorial Assessment
Yi-Large은(는) 성능, 속도, 비용 효율성의 균형이 뛰어난 01.AI의 핵심 AI 모델입니다.
Best for: 기업 업무 자동화, 소프트웨어 개발, 자율형 AI 에이전트 및 01.AI 기반 프로덕션 파이프라인
Compare Yi-Large
Frequently Asked Questions about Yi-Large
How much does the Yi-Large API cost per 1M tokens?+
Yi-Large by 01.AI is priced at $2.5 per 1M input tokens and $2.5 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.25 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Yi-Large 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 Yi-Large's context window?+
Yi-Large features a 33K tokens context window, which equals approximately 24,576 English words or ~66 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Yi-Large locally on my own GPU (Open Weights)?+
No, Yi-Large is a proprietary closed-weights model hosted by 01.AI. It cannot be downloaded for local GPU execution and must be accessed via 01.AI's official API or cloud platform.
Does 01.AI train on data sent to the Yi-Large API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (01.AI Terms). Full commercial ownership of generated outputs. Supported deployment channels include: 01.AI Cloud API & Enterprise Endpoints.
How does Yi-Large perform on coding and reasoning benchmarks?+
Yi-Large achieves a composite Intelligence Index of 79/100, scoring N/A% on SWE-bench Verified, 52% on GPQA Diamond, 76.5% on MMLU-Pro, and 1290 pts on Chatbot Arena Elo.
What is Yi-Large best used for?+
Yi-Large is best suited for: 기업 업무 자동화, 소프트웨어 개발, 자율형 AI 에이전트 및 01.AI 기반 프로덕션 파이프라인. Assessment: Yi-Large은(는) 성능, 속도, 비용 효율성의 균형이 뛰어난 01.AI의 핵심 AI 모델입니다.
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Yi-Large:
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.
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.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
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.