OpenAI API is an AI agent platform that supports multi-Agent collaborative work, visual workflow orchestration and automated task execution, reducing manual operation costs of complex business processes.

OpenAIAPI

Core parameters and statistics of OpenAI API

OpenAI API is currently one of the most frequently called large model API services in the world, providing a multi-modal capability matrix from text dialogue, image generation, speech recognition to embedding vectors. Its core parameters draw clear gradients between different models, forming a full coverage strategy from flagship high-precision to lightweight and low-cost.

Features	GPT-4o	GPT-4o-mini	o4-mini	Description
Positioning	Multi-modal flagship	Highly cost-effective and lightweight	Inference acceleration model	Covering different accuracy and cost requirements
Context window	128K tokens	128K tokens	128K tokens	Full range of unified contexts
Maximum output	16K tokens	16K tokens	16K tokens	Standard output length
Knowledge deadline	~2025-06	~2025-06	~2025-06	Subject to official documents
Multi-modal input	Text + image + audio	Text + image	Text	The flagship model has the richest modalities
Structured output	Support	Support	Support	Full range of standardization

Parameter layered interpretation: The flagship model GPT-4o undertakes high-complexity tasks (multi-modal analysis, complex instruction following, long document reasoning), while GPT-4o-mini covers high-frequency and low-latency scenarios (customer service, content classification, simple question and answer) with a price difference close to 10-20 times. The o4-mini series is enhanced in mathematical reasoning and code generation, and is suitable for STEM-type tasks that require precise chain reasoning. The three form a triangular coverage of "flagship quality-economic throughput-dedicated for reasoning".

Performance and Throughput Reference: OpenAI officials have not continued to disclose precise TTFT (first word delay) and concurrency frequency control values. According to sampling data from third-party evaluations (such as Artificial Analysis), the TTFT of GPT-4o-mini under medium concurrency is about 200-500ms, and GPT-4o is about 500-1500ms. The o4-mini inference mode fluctuates greatly depending on the task complexity. In terms of frequency control, the default free tier has lower RPM and TPM limits, and the paid tier increases based on usage levels. The details are subject to official real-time documents and actual account usage.

User and market recognition of OpenAI API

The market position of OpenAI API cannot be bypassed - it is the benchmark product for the commercialization of large models and is the default benchmark for almost every native AI application in the initial evaluation.

C-side influence: ChatGPT (based on the same underlying model) has accumulated more than 500 million registered users by 2026, with weekly active users stable at more than 200 million. Although C-side users call the API through the ChatGPT interface rather than directly, the usage habits of ChatGPT directly promote developers’ understanding and adoption of the OpenAI API. The ChatGPT Plus/Pro subscription system fosters willingness to pay for enterprise-level API services.

Depth of B-side adoption: OpenAI API has been accessed by more than 1 million developers and enterprises around the world (data comes from officially disclosed market activities in 2025), covering vertical industries such as finance, law, medical care, e-commerce, education, and software research and development. Enterprise customers include leading technology and financial companies such as Morgan Stanley, Salesforce, and Stripe. The form of adoption has expanded from simple intelligent customer service Q&A to core business scenarios such as contract review, code generation, data analysis processes, and personalized recommendations.

Industry benchmark performance: The GPT-4o series has long maintained the first echelon of closed-source models on mainstream benchmarks such as MMLU, GPQA, and HumanEval. However, it faces strong competition from Claude Opus and Gemini Ultra on domain-specific benchmarks (such as legal professional examinations, medical licensing tests). Third-party evaluations (such as LMSYS Chatbot Arena) show that users' overall preference for GPT-4o is in the top range, but it is not always ahead in tasks such as creative writing and accurate citation of long documents.

Developer Ecosystem: OpenAI API has the most mature SDK system - it officially provides SDKs for mainstream languages ​​such as Python, Node.js, Go, Java, and .NET, and third-party bindings contributed by the community cover more language stacks. OpenAI's API design (especially the Chat Completions interface) has actually become an industry standard interface specification, and most competing products (DeepSeek, Claude, Gemini) have chosen to be compatible with this format to reduce developers' migration costs.

Cost Advantages of OpenAI API

The pricing of OpenAI API has evolved from "early high unit price" to "multi-level tiering". It currently forms a two-tier structure with GPT-4o-mini as the cost inflection point and the flagship model raising the quality ceiling.

C-side/individual users: Individual developers can register an account on the OpenAI platform to obtain a free trial amount (usually an initial bonus of $5-18, subject to the official website’s real-time policy). After the free quota is exhausted, you will switch to pay-as-you-go, and no long-term subscription is required. For light usage (hundreds of calls per day on average), the monthly cost is usually within a few dozen dollars. Applicable Boundaries: The free tier has strict frequency control limits (about 3-20 RPM) and is not suitable for production-level applications; it is recommended that personal projects start directly from the paid tier.

Developer/API call: full model price comparison

Model	Input (per million tokens)	Output (per million tokens)	Applicable scenarios
GPT-4o	$2.50	$10.00	Multi-modal analysis, complex instructions, high-quality content generation
GPT-4o-mini	$0.15	$0.60	Customer service, classification, summary, high-frequency and low-latency scenarios
o4-mini	$1.10	$4.40	Mathematical reasoning, code generation, structured logic tasks
GPT-4.1	$2.00	$8.00	Very long context, code generation, complex instructions (to 1M token)
GPT-4.1-mini	$0.40	$1.60	Economical long context scenario
GPT-4.1-nano	$0.10	$0.40	Ultra-lightweight long context processing

Price gradient meaning: From GPT-4o-mini to GPT-4o, the output price differs by about 16 times; from GPT-4.1-nano to GPT-4o, the output price differs by about 25 times. This price span allows developers to select models based on mission accuracy needs within the same API stack without having to switch vendors. For a medium-sized application with an average daily input of one million tokens, the monthly cost of fully using GPT-4o-mini is about $9 (output is $18), while the monthly cost of fully using GPT-4o is about $150 (output is $600) - a difference of up to 20-30 times.

Enterprise/Private Deployment: OpenAI officially does not provide open source model weights, so enterprises cannot self-host OpenAI models. Enterprise-level services are provided through Azure OpenAI Service or OpenAI's enterprise plan: data is not used for model training, dedicated instances (Provisioned Throughput) are supported, SLA guarantees and VPC isolation are provided. For enterprise pricing, you need to contact the business team for an on-demand quotation. Ladder discounts are usually negotiable if you promise to spend more than $100,000 per year. Hidden costs: Backwards compatibility of model versions - OpenAI will regularly depreciate old model versions, and enterprises need to reserve a testing and migration budget for model switching.

Main functions of OpenAI API

The functional system of OpenAI API is built around the Chat Completions interface, but extends to a complete toolbox covering multi-modality, real-time interaction, and automated orchestration.

• Multi-modal understanding (Vision): GPT-4o can directly analyze image content - chart interpretation, object recognition, handwritten text transcription, screenshot analysis. Suitable for invoice OCR, product image labeling UI screenshot conversion and other scenarios. Capability Boundary: Fine layout restoration (such as accurate restoration of table structure) still requires post-processing; for images with excessive rotation angles or low resolution, the recognition accuracy drops significantly.

• Structured Outputs: Specify JSON Schema through the response_format parameter, and the model output will strictly conform to the predefined structure. Compared with conventional Function Calling, structured output is more stringent in terms of type constraints and field integrity, and is suitable for scenarios that require deterministic data structures - such as information extraction pipeline API response formatting and database writing preparation.

• Function Calling / Tool Use: The model can automatically choose to call external tools (functions) to integrate the results into the reply. This allows a single API call to complete the entire process of "understanding the intent → calling the tool → processing the result". Typical applications: weather query, database query, air ticket price comparison, code execution. Implementation Tip: The stability of Function Calling highly depends on the clarity of the function description - fuzzy description will lead to the model selecting the wrong function or fabricating parameters.

• Real-time API: Low-latency voice-to-voice interaction based on WebSocket, with single round-trip latency typically in the millisecond level. Suitable for scenarios such as voice assistants, real-time translation, and customer service voice interaction. It is still a relatively new capability, and the documentation and workflow tool chains are still being improved.

• Batch API (batch processing): supports asynchronous submission of large batch requests, and unified callback after processing is completed. The batch processing price is about 50% of the real-time API, and is suitable for scenarios that are not sensitive to latency, such as data annotation, content batch review, and large-scale information extraction. Time Reference: OpenAI officially states that batch processing is usually completed within 24 hours, depending on the queue depth.

• Fine-tuning: allows developers to use private data to perform additional training on the basic model to optimize the performance of specific tasks. It is suitable for scenarios with dense domain terms, highly customized output formats, and brand tone. Supports full parameter fine-tuning and efficient parameter fine-tuning solutions such as LoRA. Cost Consideration: Fine-tuning training is billed by token, and hosted fine-tuning models are billed by running time; for fine-tuning a medium-sized (~100,000 samples) GPT-4o-mini, the training cost is about several hundred dollars, and the long-term hosting monthly fee is about hundreds to thousands of dollars.

• Assistants API: Provides functions such as conversation state management, knowledge base retrieval, code interpreter, etc., and packages GPT-4o into an "AI agent" form that can be embedded in applications. Developers only need to define instructions, mount knowledge files, and configure tools to get an intelligent assistant that can perform multi-step tasks.

Model and version evolution of OpenAI API

OpenAI's model iteration follows the dual-track strategy of "capability base + specialized branches". The basic capability model continues to evolve, and special models are developed in dimensions such as reasoning, multi-modality, and cost optimization.

GPT-4 Series: Multi-modal fusion and cost stratification (2023-03 to present)

• GPT-4 (2023-03-14): Multi-modal capabilities were introduced for the first time, reaching human levels in multiple professional examinations. It was the largest commercial model with the largest parameter scale and the strongest capabilities at the time.
• GPT-4 Turbo (2023-11-06): The context is expanded to 128K, the knowledge is updated to 2023-04, the price is about 50% lower than GPT-4, and JSON mode and function call optimization are introduced.
• GPT-4o (2024-05-13): Native multi-modal architecture, unified processing of text + image + audio, real-time dialogue delay reduced to the order of 200-300ms, it is a fundamental architectural reconstruction of the GPT-4 series.
• GPT-4o-mini (2024-07-18): A cost-effective multi-modal small model that covers most daily tasks at a very low cost and has become the first choice entry model for small and medium-sized teams.
• GPT-4.1 Series (2025-04-14): Code capabilities are significantly enhanced, the context is expanded to 1M token (GPT-4.1), mini/nano economic models are introduced, and engineering development scenarios are positioned.

o Series: Reasoning Specialization and Chain Thinking (2024-09 to present)

• o1-preview / o1-mini (2024-09-12): The first specialization series for reasoning, which significantly surpasses GPT-4o in tasks requiring long-range chain reasoning such as Olympiad mathematics and programming competitions. The reasoning time increases in proportion to the output length.
• o1 (2024-12-05): The official version of the inference series, which has improved speed, accuracy and multi-language support compared to the preview version.
• o3-mini (2025-01-31): Inference acceleration model, providing three levels of inference intensity (low/medium/high), providing a more flexible adjustment space between cost and accuracy.
• o4-mini (2025-04-11): Reasoning capabilities are further improved, and mathematics and programming benchmarks reach new heights, while maintaining low latency and cost.

Other model branches

• DALL-E 3 (2023-10): Vincent graph model, supports 1024x1024, 1792x1024 and other resolutions, currently provided through the Images API.
• Whisper (2022-09): An open source speech recognition model, the API version is continuously updated, supports 99+ languages, and the recognition rate in multi-lingual mixed scenarios continues to be optimized.
• TTS (2023-11): Text-to-speech API, providing 6 preset sounds and supporting two quality levels: standard and HD.
• Embeddings (2022-12): text-embedding-3-small/large series, released in January 2024, supports flexible output of 256-3072 dimensions, and is the de facto benchmark model for RAG semantic retrieval.

Model depreciation mechanism: OpenAI will regularly release model deprecation schedules. Older versions of models usually enter the "depreciation" status 3-6 months after the new version is released. This means for production systems: the model field in the API response will change at any time. Enterprise applications should avoid hard-coding the model name and instead confirm the model version through the response header or read-only field returned by the API.

Technical advantages of OpenAI API

The technical barriers of OpenAI API are not only reflected in the model capability itself, but also in the system engineering optimization covering the entire link of training, inference, and deployment.

Scale effect of training infrastructure: OpenAI has cooperated with Microsoft to build the world's largest AI training cluster. The computing power pool of hundreds of thousands of GPUs enables sufficient architecture search and hyperparameter tuning in a large-scale parameter space. Early estimates of the single training cost of training a GPT-4 model range from tens to hundreds of millions of dollars. This kind of computing power investment constitutes a fixed cost threshold that is difficult for latecomers to overcome.

Multi-level strategies for inference optimization: OpenAI uses a combination of technologies such as segmented KV cache, dynamic batching (Dynamic Batching), and speculative decoding (Speculative Decoding) at the inference level. Specific performance: GPT-4o-mini's single-token inference delay can be compressed to less than 10ms, while the flagship model avoids full calculation through the sparse attention mechanism in long context scenarios. The direct effect in engineering is that about 60-70% of the single API call price paid by users is used to cover the cost of inference computing power, and the rest covers training amortization and operating expenses, rather than "brand premium".

Uniformity of multi-modal fusion architecture: Unlike earlier "external alignment" solutions that processed text, images, and speech separately, GPT-4o adopts an end-to-end native multi-modal architecture, and all modalities are processed in parallel in the same Transformer backbone. This allows cross-modal information (e.g. text in diagrams, tone in speech) to be interacted directly within the model without the need for an external alignment step. This explains why GPT-4o performs better than the spliced ​​solution of "image encoder + text model" on the tasks of "looking at pictures and speaking" and "understanding diagrams".

API infrastructure maturity: OpenAI API provides globally distributed inference nodes, automatic fault tolerance, real-time cost monitoring (Dashboard + Usage API) and detailed token-level billing. Tier 5 (the highest level) accounts receive concurrency caps of over 20,000 RPM. This is in contrast to most competing products that are still at the stage where "API stability requires continuous monitoring". For production-grade applications, this infrastructure maturity translates directly into lower operational costs and fewer unplanned outages.

How to use OpenAI API

The entrances to the OpenAI API include the official Dashboard, direct API calls, and the Azure OpenAI Service enterprise channel.

How to use	Suitable for people	Features	Cost
API direct call	Developers and enterprises	Accessed through api.openai.com, standard RESTful interface	Pay-as-you-go by token
Azure OpenAI Service	Enterprise customers	Access via Azure cloud platform, supports dedicated instances and VPC	Pay-per-use + Reserved Instance billing
OpenAI Playground	Prototype verification	Web interactive interface, visual parameter adjustment	Need to bind a paid account
ChatGPT desktop/mobile	Individual users	Non-API, but shared model capabilities	Subscription $20-$200/month

API Quick Start Example: OpenAI API uses HTTP protocol. The following is a standard example of calling GPT-4o from Python (using the official openai Python SDK):

from openai import OpenAI

client = OpenAI(
    api_key="<YOUR_API_KEY>",
    # base_url defaults to https://api.openai.com/v1
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "developer", "content": "You are a professional Python code review assistant."},
        {"role": "user", "content": "Please review the following Python code to point out potential performance issues and security vulnerabilities."}
    ],
    temperature=0.3,
    max_tokens=4096,
    stream=False,
    response_format={"type": "text"}
)

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

API Key Obtaining Process: Visit platform.openai.com → Register/log in to your account → Select "API Keys" from the left menu → Create a new Secret Key → Copy and store securely (the complete Key cannot be viewed again after closing the pop-up window). Security Reminder: API Keys should be stored as contextual variables and should not be hard-coded in the code repository; it is recommended to create independent Keys for different applications and set usage limits (Usage Limits) to prevent accidental overspending.

REST API direct call (Curl):

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "Introducing the core advantages of OpenAI API"}],
    "temperature": 0.7,
    "max_tokens": 1024
  }'

Key parameter practice: temperature (0-2.0) controls randomness, 0.1-0.3 is recommended for coding tasks, and 0.7-0.9 is recommended for creative writing; max_tokens limits the maximum output length, and will stop even if the output is not completed after setting; stream=true enables SSE streaming output, significantly reducing the first word perceived delay; response_format={"type": "json_object"} is mandatory JSON format output (you need to ask for JSON output in the system prompt). For the complete parameter list, refer to the OpenAI official API documentation.

Product Pricing

The billing model of OpenAI API is unified as "pay-as-you-go by token + tiered discounts", with no fixed monthly fee. The unit price details of each model have been given in the previous chapter on cost advantages. Here we focus on the evaluation framework and practical suggestions of the three-tier cost structure.

C-side/individual developers: Get an initial credit when registering (currently usually $5-18, subject to the official real-time policy). After the free quota is exhausted, it will automatically switch to the pay-as-you-go mode without interrupting service. Monthly costs typically range from $0-50 for personal learning, prototyping, and low-traffic applications. Note: The free tier has strict frequency control (Tier 1 is about 3-20 RPM) and is not suitable for any form of online production.

Developer/API calling layer: Automatically obtain frequency control upgrade after usage reaches a certain threshold. Tier tiers are determined by cumulative spend – $5 liter Tier 2 (~200 RPM), $50 liter Tier 3 (~500 RPM), $250 liter Tier 4 (~2000 RPM), $1000 liter Tier 5 (~20000 RPM). In addition, Batch API (about 50% discount) and Provisioned Throughput (reserved throughput) are provided to optimize costs. Batch is suitable for large-scale non-real-time tasks, and reserved throughput is suitable for quasi-production environments that have stable requirements for latency and concurrency.

Enterprise/Private: OpenAI does not offer model self-hosting, enterprises can choose Azure OpenAI Service or OpenAI Enterprise Plan. The core selling points of the enterprise version include: data is not used for model training (Data Privacy), dedicated instances with controllable direct costs (Provisioned Throughput Units), compliance certifications (SOC 2, ISO 27001, HIPAA, etc.), VPC private network isolation, and dedicated business support. Enterprise pricing requires contacting sales team for quote, annual consumption commitment typically starts at $100,000. Hidden Cost: Model version depreciation risk (old version of the model needs to be migrated to the new version), and enterprises need to incorporate the model version upgrade budget into the annual technology plan.

Application scenarios

The implementation scenarios of OpenAI API span the four major fields of content production, software development, customer service and industry automation. The following four types of scenarios have been verified at scale:

• Content Generation and Editing: Multi-language article writing, batch product description generation, marketing copy A/B testing, and draft generation of social media content scheduling. Taking the cross-border e-commerce scenario as an example, GPT-4o can automatically generate 1,000-word description copy (covering English, Chinese, Japanese and other languages) based on product parameters. It only requires manual review and fine-tuning, and the daily output of a single person has increased from 20 to 80-120 articles. Implementation Tip: For scenarios that require high brand tone consistency, it is recommended to use Fine-tuning to fine-tune the model first. The output quality and large-scale stability can be significantly improved.

• Code generation and R&D efficiency improvement: code completion and generation (through API integrated IDE plug-in or CI/CD process), automatic unit test generation, code review-assisted SQL query generation, vulnerability scanning assistance. Deduction: With the assistance of AI, junior developers’ coding time for simple CRUD functions was shortened from 30-60 minutes to 5-15 minutes, and the test coverage increased from 40% to more than 70%. However, judgments that require a global perspective such as architecture design and security audits still rely on manual labor, and the model may ignore non-functional requirements (performance boundaries, fault-tolerant design) and cross-module business consistency issues.

• Intelligent customer service and dialogue system: A multi-round dialogue customer service system built based on Assistants API or Chat Completions, supporting knowledge base retrieval (through File Search tool) and dynamic intent recognition. Quantitative reference: Taking the e-commerce customer service scenario as an example, GPT-4o-mini can automatically handle about 70-80% of standard inquiries (delivery inquiries, return and exchange policies, payment issues), and the remaining 20-30% of complex exceptions (dispute mediation, after-sales that require manual judgment) are transferred to manual labor, and the customer service response time is shortened from an average of 15 minutes to 1 Within minutes, but the "handover experience" between robots and humans (the completeness of the conversation history transmission, the fatigue of repeated descriptions by users) is still a shortcoming in the experience.

• Information Extraction and Data Analysis: Extract structured information from unstructured documents - quantitative data extraction of key terms of contracts, invoice fields, resume screening, and market research reports. In Batch API mode, millions of documents can be processed within hours. Capability Boundary: For handwriting in scans, non-standard table layouts (merged cells, frameless tables) and PDFs with dense strong domain terms, the accuracy will drop by 15-30%. It is recommended to cooperate with OCR preprocessing and domain vocabulary injection.

Adaptation boundary with competing products: OpenAI API is the most balanced in terms of "average experience" for general tasks - most tasks only require a single model to obtain a result of 80 points, without the need for multi-model stitching. However, careful evaluation is required in the following scenarios: plain text high-concurrency scenarios that are extremely sensitive to output costs (competing products such as DeepSeek are 10-100 times lower in price); scenarios that require open source self-hosting to meet data sovereignty (no open source model is available); scenarios that have strict requirements on real-time conversation delay (their own competing products have regional acceleration node differences).

Applicable people

OpenAI API is applicable to far more than AI developers. Through model layering and SDK coverage, it has penetrated into the daily workflow of different technical backgrounds and industry roles:

• AI application developers and independent entrepreneurs: This is the core user group of OpenAI API. Embed LLM capabilities into products via APIs—from simple chatbots to complex multi-agent collaboration systems. The low cost of GPT-4o-mini allows MVP-validated API costs to be kept under $50 per month. Not suitable for boundaries: If your product has geographical restrictions on data privacy compliance (for example, the EU GDPR requires data not to exit the country), or requires an end-to-end latency of less than 100ms (real-time game NPC dialogue), Azure OpenAI Service or a self-hosted open source solution may be more suitable.

• Product Managers and Operations Staff: Generate copywriting A/B testing techniques, competitive product analysis summaries, and user feedback classifications in batches through the Playground or API interface. GPT-4o's structured output capabilities allow operators to bypass the engineering team and process data in batches directly through preset templates. Prerequisites: You need to master the basic API calling concepts (Token, Prompt, Temperature), and you can complete parameter adjustment and testing through the Playground interface without mastering a programming language.

• Data Scientists and Analysts: Use LLM as a pipeline tool for data preprocessing and information extraction. Typical workflow: Use GPT-4o-mini for NER entity extraction → Use Embeddings API for semantic clustering → Use GPT-4o to generate analysis report summary. Unsuitable Boundary: OpenAI API is not suitable for statistical analysis scenarios that require certainty - the output of LLM is probabilistic in nature, and work that requires extremely high reliability of "accurate statistics" (financial report audit data extraction) should be combined with traditional rule engine double verification.

• Enterprises and mid-to-large organizations: Embed LLM capabilities into core business systems through Azure OpenAI Service or Enterprise Plan. Typical scenarios: intelligent customer service knowledge base, internal knowledge retrieval (RAG), contract audit assistance, code warehouse security scanning. Purchasing Prerequisites: Enterprises should have a basic AI literacy team (able to distinguish "what LLM can do vs. what it cannot do"), and establish a regular sampling inspection mechanism for model output quality. Before purchasing, you need to verify: whether the Azure deployment area and the data location match, and the expansion conditions and costs of the Provisioned Throughput in the commercial contract regarding the notification window period for model version depreciation.

Summary and Outlook

The industry position of OpenAI API can be summarized in one sentence: it is the "definer" of commercialization of large models, and it is also the default option for almost all AI application developers in the early stages of selection. Its core competitiveness lies not only in the model itself, but also in its year-round infrastructure reliability, global coverage of inference nodes, and almost blind spots in the SDK ecosystem.

Current core advantages: Model capabilities are balanced and continuously iterated (flagship, economic and inference models form complete coverage); the maturity and stability of the API infrastructure are still industry benchmarks; the developer ecosystem is the most complete, with SDK, documentation, and community support forming a low-learning threshold; the "deterministic" capabilities built by Function Calling and Structured Outputs have practical value in production and business integration scenarios.

Current major limitations: API costs are no longer competitive in plain-text high-concurrency scenarios (price impacts from DeepSeek and Gemini); non-open source models limit the adoption of data sovereignty-sensitive industries (scenarios such as finance and government affairs that require privatized deployment); the model version depreciation mechanism brings continuous migration pressure to production systems; single dependence on API suppliers poses business continuity risks (service interruptions, price adjustments, policy changes).

Procurement and Adoption Risk Assessment: For individual developers and start-up teams, the OpenAI API has a very low trial threshold (initial bonus + pay-as-you-go). It is the lowest-risk way to access AI capabilities and is suitable as the preferred model supplier in the product verification cycle. However, for high-frequency applications that pursue ultimate cost efficiency, it is recommended to build a multi-vendor routing layer (such as model switching through gateways such as LiteLLM and OpenRouter) to select the most cost-effective model for different tasks. For enterprise customers, it is recommended to position OpenAI API as a "high-quality main model supplier" and reserve a privatized deployment solution for open source models (such as DeepSeek) as a second path to data sovereignty compliance. When signing an enterprise contract, you need to pay attention to: the length of the transition period for model version depreciation, the commitment boundary of data not leaving the country (Azure region selection + data transfer path), and the SLA compensation clause for service interruption. In the long run, OpenAI API's moat in terms of "flagship quality" is being caught up, but in the two dimensions of "the most mature developer experience" and "the most complete multi-modal capability matrix", it is still difficult to have a substitute in the short term.

Related tools: CrewAI, LangChain

Version Info

• Public beta version ：OpenAI API, function updates are subject to official website announcements. （2026-07-14）
• earlier version ：Initial version, core functions are subject to release notes. （2026-07-14）