OpenAI
Usage BasedThe world's leading LLM API — from GPT-4o to o-series reasoning models.
Scores
About
The OpenAI API is the industry-benchmark gateway to the world's most widely adopted large language models, serving millions of developers across every major application category. As of 2026 the platform spans five distinct model families, each optimised for a different point on the cost-quality-latency spectrum.
Model families
GPT-5 series (flagship reasoning + generation): GPT-5 is OpenAI's current top-of-the-range model, combining strong reasoning, instruction-following, and creative generation. Subsequent point releases (GPT-5.1, GPT-5.2, GPT-5.4, GPT-5.5) deliver incremental improvements in coding, long-context comprehension, and agent tool use. Best for the hardest tasks where cost is secondary.
GPT-4.1 family (long-context workhorses): GPT-4.1 offers a 1 million-token context window with 32K max output, making it the go-to choice for codebases, legal documents, and data pipelines that exceed standard limits. GPT-4.1 mini and GPT-4.1 nano bring the same million-token window at significantly lower cost, with nano ($0.10/M input) being one of the cheapest capable models available.
GPT-4o (fast multimodal): 128K-token context window, native vision (image inputs), audio I/O, and text. GPT-4o remains the workhorse for production applications that need a balanced mix of quality, speed, and multimodality without the premium of the GPT-5 tier.
GPT-4o mini (cost-efficient): $0.15/M input tokens, 128K context. Suited for classification, extraction, summarisation, and other high-volume, lower-complexity workloads where cost per call matters most.
o-series reasoning models (chain-of-thought): o1, o3, and o4-mini are purpose-built for tasks requiring deliberate multi-step reasoning — mathematics, competitive coding, scientific analysis, legal reasoning. They think before answering, making them slower but dramatically more accurate on hard structured problems. o3 and o4-mini (200K context window each) have brought reasoning model costs down sharply: o4-mini at $1.10/M input is 13× cheaper than the original o1 on input tokens.
Access channels
Developers can reach OpenAI models through three main channels:
- OpenAI API (direct): API keys from platform.openai.com, billed per token against the rates above.
- Azure OpenAI Service: Microsoft's enterprise-grade deployment with private networking, content filtering, compliance certifications, and a 4-month model-debut exclusivity window on all new OpenAI flagship releases.
- Amazon Bedrock: As of April 28, 2026, OpenAI's models — including GPT-4.1 and GPT-5.5 — are available natively on AWS Bedrock after Microsoft's exclusivity arrangement ended. Google Cloud certification is targeted for Q4 2026.
Key Features
- Five model families — GPT-5, GPT-4.1 (1M context), GPT-4o, GPT-4o mini, and o-series reasoning — covering every quality-cost tradeoff
- o3 and o4-mini reasoning models for multi-step math, coding, and analysis tasks (200K context window each)
- Real-time API for low-latency bidirectional speech-to-speech, image inputs, and SIP calling
- Responses API — stateful next-gen interface replacing Assistants API, with server-side compaction and MCP support
- Structured Outputs — JSON Schema-constrained generation with guaranteed schema compliance
- Function calling / tool use — models autonomously decide when to invoke developer-defined tools
- Fine-tuning (supervised and reinforcement) for GPT-4o, GPT-4o mini, and GPT-4.1 variants
- Batch API at 50% token discount for asynchronous high-volume workloads
- Multi-cloud access: direct API, Azure OpenAI Service, and Amazon Bedrock (as of April 2026)
Pros
- Broadest model selection of any single LLM provider — one API key covers general, reasoning, multimodal, and cost-optimised models
- Unmatched developer ecosystem: the most libraries, tutorials, integrations, and community knowledge of any LLM platform
- GPT-4.1's 1M-token context window handles entire codebases and lengthy documents in a single call
- o-series reasoning models lead competitive benchmarks on hard logical and mathematical problems
- Real-time API makes production voice agents viable with speech-to-speech latency well below 500ms
- Prompt caching cuts costs up to 90% for repeated prefixes, critical for agent loops and RAG pipelines
- Multi-cloud availability (Azure and now Bedrock) gives enterprises deployment flexibility
Cons
- Fully proprietary — models cannot be self-hosted or audited; all inference runs on OpenAI or partner infrastructure
- Pricing complexity: five model families with per-input, per-output, per-cached, and batch rates require careful cost modelling
- o-series reasoning models are significantly slower than standard GPT models — latency can reach tens of seconds for hard problems
- Azure's 4-month exclusivity window on new flagship models creates a lag for AWS and other cloud users
- API shape is evolving rapidly (Assistants API deprecated in 2026, Responses API is the successor) — requires ongoing migration effort
- Output quality degrades on very long contexts even with 1M-token windows — retrieval-augmented approaches often needed
Pricing
Usage Based- · $0.15 per million input tokens
- · $0.60 per million output tokens
- · 128K context window
- · Best for high-volume, lower-complexity tasks: classification, extraction, summarisation
- · $0.10 per million input tokens
- · 1 million token context window
- · Lowest-cost capable model in the GPT-4.1 family
- · Suited for simple retrieval, tagging, and routing tasks
- · 1 million token context window
- · Mid-tier cost between GPT-4.1 and nano
- · Balanced quality and cost for most production workloads
- · $2.00 per million input tokens
- · $8.00 per million output tokens
- · 1 million token context window, 32K max output
- · Best for long-document analysis, large codebase reasoning, and RAG with extensive retrieved context
- · $2.50 per million input tokens
- · $10.00 per million output tokens
- · 128K context window
- · Native vision (image) and audio I/O
- · $1.10 per million input tokens
- · $4.40 per million output tokens
- · 200K context window
- · Budget-tier reasoning model — strong on math, coding, and logic at 13× lower input cost than o1
- · $2.00 per million input tokens
- · $8.00 per million output tokens
- · 200K context window
- · Full-strength reasoning for the hardest coding, scientific, and mathematical tasks
- · Multiple variants: GPT-5, GPT-5.1, GPT-5.2, GPT-5.4, GPT-5.5
- · GPT-5.2 priced at $1.75/M input, $14.00/M output (90% cached input discount)
- · Highest capability tier — best reasoning, instruction-following, and creative generation
- · Recommended starting point for complex agentic workflows and the hardest tasks
- · 50% discount on all token costs for asynchronous batch jobs
- · Results delivered within 24 hours
- · Ideal for bulk classification, embeddings, evaluations, and data processing pipelines
Possible Stacks
n8n + OpenAI Automation
ProjectBuild AI-powered automation workflows with n8n and OpenAI. n8n's visual workflow builder connects GPT-4o and o-series models to any app or API, making it easy to automate tasks like content generation, data enrichment, and intelligent routing without writing backend code.
VS Code + OpenAI + GitHub
DeveloperVS Code enhanced with OpenAI for AI-assisted coding — think GitHub Copilot or ChatGPT alongside your editor — paired with GitHub for version control. A familiar workflow for developers adding AI to an existing traditional setup.
Development
LLM
Codex + OpenAI + GitHub
DeveloperA fully agentic OpenAI workflow: Codex operates as a headless coding agent powered by OpenAI models, handling tasks directly from the terminal without a traditional IDE. Paired with GitHub for version control, this is the OpenAI-ecosystem counterpart to Claude Code.
Related Tools
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