o4-mini-deep-research
OpenAI 推出的 o4-mini-deep-research 人工智能模型，具备 200K 上下文窗口，输入价格为 $2/百万 Token，输出价格为 $8/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
Technical Specifications
- Provider
- OpenAI
- Category
- reasoning
- Release Date
- 2025-06
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- 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)
- o4-mini-deep-research
- Supported SDK & API Protocol
- OpenAI Responses API & Chat Completions v1
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($2/1M in) across full 200K window
- Prompt Cache Write & TTL Policy
- Automatic prefix caching at $0.5/1M (no extra cache-write fee)
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Native Web Search tool + Hosted Code Interpreter & File Search RAG
- Fine-Tuning & Distillation Support
- Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- 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 OpenAI Cloud API
- System Card & Safety Framework
- OpenAI Preparedness Framework Audited & System Card Published
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
o4-mini-deep-research Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~51 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 o4-mini-deep-research:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 48.5%
- GPQA Diamond
- 68%
- MMLU-Pro
- 80.5%
- HumanEval
- 90.8%
- Humanity's Last Exam
- 11.8%
- Terminal-Bench
- 33.5%
- SciCode
- 35%
- LiveCodeBench
- 60%
- AIME
- 85%
- Long-Context Reasoning
- 76%
- Chatbot Arena Elo
- 1318pts
Strengths
- 由 OpenAI 研发的高精度 Compact agentic reasoning transformer 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $2/1M，输出 $8/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Limitations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Editorial Assessment
o4-mini-deep-research 是 OpenAI 旗下极具代表性的模型，在性能、速度与 API 成本之间实现了卓越平衡。
Best for: 企业级 AI 自动化、代码研发、智能体（Agent）工作流以及 OpenAI 生态深度集成
Compare o4-mini-deep-research
Frequently Asked Questions about o4-mini-deep-research
How much does the o4-mini-deep-research API cost per 1M tokens?+
o4-mini-deep-research by OpenAI is priced at $2 per 1M input tokens and $8 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.5 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the o4-mini-deep-research 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 o4-mini-deep-research's context window?+
o4-mini-deep-research features a 200K tokens context window, which equals approximately 150,000 English words or ~400 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 100,000 tokens.
Can I run o4-mini-deep-research locally on my own GPU (Open Weights)?+
No, o4-mini-deep-research is a proprietary closed-weights model hosted by OpenAI. It cannot be downloaded for local GPU execution and must be accessed via OpenAI's official API or cloud platform.
Does OpenAI train on data sent to the o4-mini-deep-research API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (OpenAI Terms). Full commercial ownership of generated outputs. Supported deployment channels include: OpenAI Platform API, Microsoft Azure OpenAI Foundry.
How does o4-mini-deep-research perform on coding and reasoning benchmarks?+
o4-mini-deep-research achieves a composite Intelligence Index of 48/100, scoring 48.5% on SWE-bench Verified, 68% on GPQA Diamond, 80.5% on MMLU-Pro, and 1318 pts on Chatbot Arena Elo.
What is o4-mini-deep-research best used for?+
o4-mini-deep-research is best suited for: 企业级 AI 自动化、代码研发、智能体（Agent）工作流以及 OpenAI 生态深度集成. Assessment: o4-mini-deep-research 是 OpenAI 旗下极具代表性的模型，在性能、速度与 API 成本之间实现了卓越平衡。
OpenAI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to OpenAI's flagship reasoning tier:
GPT-6 Astra
GPT-6.1 Sol
GPT-6 Sol
GPT-6 Luna
GPT-5.6 Sol
GPT-5.6 Terra
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to o4-mini-deep-research:
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.
数据来源: OpenAI official pricing & model documentation (verified October 2026)↗