Buying snapshot
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Decision point | What OpenAI states |
|---|---|
API model limits | GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra and GPT-6 Luna: 1.05M context length and 128K max output tokens each |
Buying ChatGPT for a team | Business starts at 2 users and accepts any major credit card; Enterprise goes through sales, with invoicing as an alternative payment option |
API security and compliance | SOC 2 Type 2, BAA for HIPAA compliance, SSO and MFA, IP allowlist and mTLS, AES-256 at rest and TLS 1.2+ in transit |
Use of your data | API: no training on your data; zero data retention by request. ChatGPT Free, Go, Plus and Pro: content is used to train OpenAI's models, with opt-out available |
Python SDK |
|
Discounts and free programs | Nonprofits: up to 75% off ChatGPT Business or Enterprise. Verified U.S. K-12 educators: free ChatGPT for Teachers plan through June 2027 |
Our take
OpenAI fits teams that want hosted models through pay-as-you-go API pricing and can send each job to a suitable model and processing mode. If you mainly want a per-seat cost for staff use, buy ChatGPT's per-user plans instead.
Evaluate OpenAI
Popular comparisons
See all 7 OpenAI comparisons
Where it stands out
One model, several speeds. Each flagship API model has Standard, Batch and Fast prices, and gpt-6-astra and gpt-6.1-sol also have Ultrafast. You can run batch jobs and urgent requests on the same model and pay for the speed each one needs.
Agent building blocks in the platform. The Agents SDK and Responses API come with built-in tools such as web search, file search and remote MCP servers, so a first agent needs less of your own glue code.
Cost and access controls for admins. Role based access to tools, models and projects, billing and usage alerts, and usage and cost by project help a platform team keep many projects in check. OpenAI also lists a dedicated account team and prioritized support among its enterprise-grade features.
Production-minded Python SDK. The library supports workload identity with short-lived tokens from Kubernetes, Azure and Google Cloud identity providers instead of long-lived API keys, and retries certain errors 2 times by default.
Product screenshot
Trade-offs to weigh
Residency and FedRAMP cost extra. Regional processing (data residency) endpoints add a 10% uplift for models released on or after March 5, 2026, and FedRAMP endpoints also add 10%. A data-location requirement changes the bill, not only the setup.
Long prompts move to a different rate. Short-context rates cover up to 272K input tokens; above that, long-context rates apply. For example, gpt-6-astra in Ultrafast mode goes from $60.00 to $120.00 per 1M input tokens.
Fine-tuning is being wound down. The platform is closed to new users; existing users can create training jobs for the coming months, and fine-tuned models stay available for inference until their base models are deprecated. Plan custom-model work around that.
Tools add their own charges. Eligible Hosted Shell and Code Interpreter container sessions are billed by the minute with a 5-minute minimum per session, and GPT-Live 1 voice sessions charge backend model and tool usage separately. Token rates alone understate an agent's bill.
Pricing and plan conditions
The API is pay-as-you-go: tokens are billed at the chosen model's input and output rates, and the Responses, Chat Completions, Realtime, Batch and Assistants APIs are not priced separately. ChatGPT paid plans (Go, Plus, Business and Enterprise) are priced per user per month, with monthly plans for Go, Plus and Business and annual plans for Business and Enterprise. Business and Enterprise can purchase credits for more access.
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Model | Standard | Batch | Fast |
|---|---|---|---|
gpt-6-astra | $10.00 / $50.00 (cached input $1.00) | $5.00 / $25.00 | $20.00 / $100.00 |
gpt-6.1-sol | $2.00 / $10.00 (cached input $0.10) | $1.00 / $5.00 | $4.00 / $20.00 |
gpt-6.1-terra | $1.00 / $5.00 (cached input $0.05) | $0.50 / $2.50 | $2.00 / $10.00 |
gpt-6-luna | $0.10 / $0.50 (cached input $0.01) | $0.05 / $0.25 | $0.20 / $1.00 |
The Standard rates are standard processing rates for context lengths under 272K. Ultrafast costs $60.00 / $300.00 for gpt-6-astra and $12.00 / $60.00 for gpt-6.1-sol; Priority processing was renamed Fast mode on July 30, 2026. Separately, OpenAI states that GPT-5.6 Sol's promotional pricing is available at least through November 21, 2026.
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Model | Published rate |
|---|---|
gpt-5.6-sol (Cyber models table, Standard, short context) | $4.00 input / $20.00 output per 1M tokens |
gpt-live-1 (voice) | $0.05 per minute, billed per second without rounding up to a whole minute |
text-embedding-3-small | $0.02 per 1M input tokens |
text-embedding-3-large | $0.13 per 1M input tokens |
omni-moderation-latest | Free |
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Plan | What it includes |
|---|---|
Free | Unlimited text chats with GPT-6 (subject to abuse guardrails); limited messages with uploads, image creation, voice chats, deep research, Codex access and ChatGPT Work access on desktop |
Go | Everything in Free and: more messages with tools, more uploads, more image creation, more voice chats, longer memory. This plan may include ads |
Plus | Everything in Go and: advanced reasoning models with GPT-6, expanded deep research, projects, scheduled tasks and custom GPTs, expanded Codex usage, ChatGPT Work on desktop, web and mobile. Limits apply |
Pro | Everything in Plus and: choice of 3 usage tiers, Pro reasoning powered by GPT-6 Astra, longer Codex and ChatGPT Work sessions, Dot (an always-on agent), sharing Plugins with your workspace |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
450 GitHub commits 90d32.1k GitHub stars0 vulnerabilities across 2 packages
See all signals from 9 sources
npm · openai@7.53.0 · PyPI · openai@3.46.0
Alternatives to OpenAI
The reviewed substitutes for OpenAI among the foundation model providers, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Anthropic
- Choose this if you need the best long-form writing quality and document analysis with strong safety guarantees.Applies to: Choosing between two products of the same kind for one job.
- Cohere
- OpenAI and Cohere offer language models through an API for text generation, embeddings and retrieval-augmented applications. OpenAI's GPT models span text, code, vision and audio for a broad market, while Cohere aims at enterprises, with models for search, embeddings and classification and data privacy controls built in.Applies to: Choosing between two products of the same kind for one job.
- Gemini API
- Two foundation-model APIs an application calls for text, multimodal and agent workloads, both priced per 1M input and output tokens. Google documents that Gemini models are accessible using the OpenAI libraries and REST API (ai.google.dev/gemini-api/docs/openai), so a team can switch one for the other in the same code; it routes its traffic to one or splits it deliberately.Applies to: Choosing which foundation-model API an application will call.
- Mistral AI
- OpenAI and Mistral AI are model providers whose large language models power chat, coding and text applications through an API. OpenAI sells proprietary GPT models, while Mistral AI, a European company, offers open-weight models that teams can run on their own infrastructure as well as commercial models with strong multilingual skills.Applies to: Choosing between two products of the same kind for one job.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Hugging Face
- Choose this if you want full model control, open-source flexibility, and the ability to fine-tune or self-host models.Applies to: When an application needs a language model it can call over an API without hosting it.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- LangChain
- A model provider supplies the model behind an API; a framework structures the application that calls it, handling tools, memory and control flow. Frameworks are model-agnostic by design, so the two sit at different layers of the same application.Applies to: Whether an application calling a model API needs a framework around it.
- OpenClaw
- OpenClaw is model-agnostic and runs on API keys you supply, so a model provider is what it calls rather than what it replaces. The reader's question is whether a self-hosted assistant removes the need for a hosted model service, and it does not: it changes where the assistant runs and who holds the data, not where the model comes from.Applies to: Whether a self-hosted assistant replaces a hosted model service, or calls one.
- Snowflake Cortex
- The extractor proposed this as a substitution and it is not one. Snowflake gives customers day-zero access to models from OpenAI and others inside Cortex -- there is a $200M partnership behind it -- so Cortex is a delivery path for those models, not a competitor to them. The documented pattern is that most teams using Cortex still call the general-purpose API directly from application code, for models Cortex does not host yet or where a pinned version is required: Cortex handles in-warehouse analytics and enrichment, the direct API handles everything downstream of the warehouse. Recorded as complementary so the alternatives grid does not tell a reader to choose.
Head to head
See detailed alternatives analysis
OpenAI dominates the AI platform market with its GPT model family, powering everything from chatbots to enterprise automation. But the AI Platforms landscape has matured significantly, and several OpenAI alternatives now offer compelling advantages in cost, openness, safety, and specialized deployment. We break down the strongest contenders so you can pick the right platform for your workload.
Top Alternatives Overview
Anthropic is the strongest direct competitor to OpenAI and the safety-first choice for enterprise teams. Claude offers a 200,000-token context window (with 1M experimental access), making it the go-to for processing entire codebases, lengthy legal contracts, or dense research papers in a single pass. Claude Pro costs $20/month and the Team plan runs $25-$30/user/month. Anthropic's Constitutional AI training approach produces outputs that are less likely to hallucinate and more predictable for regulated industries. Choose this if you need the best long-form writing quality and document analysis with strong safety guarantees.
Hugging Face is the open-source powerhouse of machine learning, hosting over 2 million models, 500K+ datasets, and 300K+ Spaces demo apps. The Transformers library has 159,000+ GitHub stars and ships under the Apache-2.0 license, making it the de facto standard for working with pre-trained models. Hugging Face Pro starts at $9/month, Team at $20/user/month, and GPU compute runs from $0.60/hour. Over 50,000 organizations including Meta, Google, Microsoft, and Apple use the platform. Choose this if you want full model control, open-source flexibility, and the ability to fine-tune or self-host models.
Perplexity Computer takes a fundamentally different approach by orchestrating 19 models in parallel. Rather than locking you into a single model family, it routes tasks to the best available model, connects to your existing tools, maintains context across sessions, and runs secure agents autonomously. It handles research, design, code, deployment, and project management end-to-end. Choose this if you need multi-model orchestration and autonomous project execution without building the routing infrastructure yourself.
Edgee solves the cost problem at the infrastructure layer. It compresses prompts before they reach LLM providers, cutting token costs by up to 50% while maintaining an OpenAI-compatible API that supports 200+ models. The service adds intelligent routing between providers with no markup on model costs. Choose this if your primary pain point is API spend and you want to reduce LLM bills without changing your code.
Zylon serves regulated industries that cannot send data to external cloud providers. This on-premise AI platform deploys entirely within your own infrastructure, giving financial institutions, healthcare organizations, and government agencies full data sovereignty. It provides governance controls, compliance features, and audit capabilities built for sectors where data residency is non-negotiable. Choose this if regulatory requirements prevent you from using cloud-hosted AI services.
Hugging Face Inference Providers deserves a separate mention for teams that want API access to 45,000+ models from leading AI providers through a single, unified API with no service fees. This offers marketplace-style model access with enterprise security features like SSO, SAML, audit logs, and data residency controls. Choose this if you want provider-neutral model access with enterprise governance.
Architecture and Approach Comparison
OpenAI operates as a closed, vertically integrated platform. OpenAI also provides an agent-building platform with a visual Agent Builder canvas and a code-first Agents SDK, plus the Realtime API for voice applications.
Anthropic mirrors this closed-API architecture but differentiates through Constitutional AI, a training methodology where the model is guided by explicit principles rather than pure RLHF. This produces outputs that are more consistent and less prone to generating harmful content. Claude's 200K context window outperforms GPT-5.4's 128K max output for tasks requiring long-form generation from extensive source material.
Hugging Face flips the model entirely. Instead of a proprietary API, it provides the infrastructure for you to run, fine-tune, and deploy any model. The Transformers library (latest release v5.5.4, April 2026) supports PyTorch-native workflows across text, vision, audio, and multimodal tasks. You can self-host on your own GPUs, use managed inference endpoints, or access third-party providers through their unified API.
Edgee operates as a transparent middleware layer. It intercepts API calls, applies token compression to reduce prompt size, then forwards compressed requests to any of 200+ supported models. Zero code changes required: swap your API endpoint and immediately save on token costs.
Pricing Comparison
OpenAI lists standard API processing rates for context lengths under 272K tokens:
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Model | Input | Output | Context length | Max output tokens |
|---|---|---|---|---|
| GPT-5.6 Luna | $0.20 per 1M tokens | $1.20 per 1M tokens | 1.05M | 128K |
| GPT-5.6 Terra | $2.00 per 1M tokens | $12.00 per 1M tokens | 1.05M | 128K |
| GPT-5.6 Sol | $4.00 per 1M tokens | $20.00 per 1M tokens | 1.05M | 128K |
OpenAI's pricing page describes these as standard processing rates for contexts under 272K tokens. It also states that GPT-5.6 Sol promotional pricing is available at least through November 21, 2026. Buyers evaluating a deployment should confirm how rates apply to longer contexts and whether promotional terms apply to their planned usage.
When to Consider Switching
Switch from OpenAI to Anthropic when your workload involves processing documents over 128K tokens, when you operate in a regulated industry that demands auditable safety guarantees, or when your team needs consistently high-quality long-form writing output. Claude's 200K standard context window handles entire books and large codebases that would require chunking with OpenAI.
Switch to Hugging Face when you need to fine-tune models on proprietary data, when you want to avoid vendor lock-in, or when you need to run inference on your own infrastructure. The Transformers ecosystem gives you access to 2 million+ models covering every modality, and Apache-2.0 licensing means no usage restrictions.
Switch to Edgee when your monthly API spend exceeds a threshold where a 30-50% cost reduction becomes material. If you are spending $10,000+/month on OpenAI tokens, Edgee's compression layer could save $3,000-$5,000 monthly with no code changes.
Switch to Zylon when your legal or compliance team has determined that no data can leave your infrastructure. Financial services firms, healthcare organizations bound by HIPAA, and government agencies with classified data all fall into this category.
Migration Considerations
Moving from OpenAI to Anthropic is straightforward. Both offer REST APIs with similar request/response patterns. The main work involves adjusting prompt engineering, since Claude responds differently to system prompts and tends to follow instructions more literally. Expect 1-2 weeks for prompt tuning on a medium-sized application. Anthropic's API supports tool use (function calling) with syntax that maps closely to OpenAI's implementation.
Migrating to Hugging Face requires more architectural work. You are moving from a managed API to either self-hosted inference or managed endpoints. Plan for infrastructure setup, model selection and benchmarking, and potentially fine-tuning. The payoff is full control and no per-token costs beyond compute. Teams with ML engineering capacity typically complete this in 4-8 weeks.
Edgee migration is nearly zero-effort. Because it exposes an OpenAI-compatible API, you change your base URL and API key, and existing code works immediately. This is the lowest-friction switch on this list.
Zylon migration involves deploying on-premise infrastructure, requiring IT involvement for hardware provisioning, network configuration, and security review. Plan for 2-4 months for a full enterprise deployment. The API interfaces follow industry conventions, but operational overhead is significantly higher than cloud-hosted alternatives.
For all migrations, we recommend running the new platform in shadow mode alongside OpenAI for 2-4 weeks, comparing outputs on production traffic before cutting over.