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Alternatives to OpenAI API and ChatGPT API in 2026: Claude, Gemini, Grok, DeepSeek, and More

Data current as of September 29, 2026. Model lineups and prices change quickly, so verify them in each provider’s documentation before starting a production project.

Just a few years ago, choosing a language-model API effectively started and often ended with OpenAI. The market looks different today. Anthropic, Google, xAI, DeepSeek, Mistral, Alibaba, Moonshot, Z.ai, and Meta all have their own APIs and capable models. Russian projects can choose between Yandex AI Studio and GigaChat, while a separate layer of aggregators such as OpenRouter, Amazon Bedrock, and Microsoft Foundry has appeared on top of all of them.

That makes “what can replace ChatGPT API?” too broad a question. One project wants cheaper text generation, another needs a million-token context window, a third needs images and voice, a fourth needs web search and tools for an AI agent, and some teams care most about running a model on their own infrastructure.

Technically, OpenAI API is more accurate than “ChatGPT API”: ChatGPT is a consumer product, while the API is a separate developer platform. But “ChatGPT API” remains widely used, so this article uses “alternatives” to mean services that let developers connect modern language and multimodal models to their applications.

What counts as an OpenAI API alternative?

In 2026, it is useful to divide these services into three groups.

The first group is direct APIs from model developers. OpenAI provides GPT, Anthropic provides Claude, Google provides Gemini, xAI provides Grok, DeepSeek provides its own models, and so on. You work directly with the company building the model.

The second group is multi-model platforms and API gateways. OpenRouter, Together AI, Hugging Face Inference Providers, Amazon Bedrock, and others provide a single way to access several model families. They are not direct equivalents of Claude or Gemini; their main job is infrastructure: routing, billing, resilience, and provider choice.

The third group is specialized AI APIs. Perplexity is primarily interesting for web search and research APIs, Replicate provides one access point for many image, video, audio, and other models, and NVIDIA NIM offers a way to deploy models on your own infrastructure through a familiar API.

Putting all three groups in one table and trying to pick a “best model” would produce a misleading comparison. We will first look at direct alternatives to OpenAI.

Leading direct alternatives to the OpenAI API

The table below is a quick guide to prominent APIs worth considering for a new AI product. Prices are per million input and output tokens for the listed model where the provider publishes a comparable pay-as-you-go rate.

Provider Example current model Context Input / Output What stands out
OpenAI GPT-6 Sol 1.05M $2 / $10 Responses API, reasoning, web/file search, computer use, broad toolset
Anthropic Claude Sonnet 5.5 1M $2 / $10 Coding, agents, long tasks, tool use, vision
Google Gemini 3.8 Flash ~1.05M $0.75 / $3.75 through the end of 2026 Text, images, video, audio, PDFs, Google Search/Maps, code execution
xAI Grok 4.7 500K $2 / $6 Reasoning, web search, X search, code execution
DeepSeek V4.1 Flash 1M From $0.15 / $0.60 Low price, OpenAI/Anthropic compatibility, vision, tools
Mistral Medium 3.5 256K $1.50 / $7.50 Open-weight ecosystem, multimodal, agents, self-hosting options
Alibaba Qwen3.8 Max Up to 1M $2 / $6 Broad Qwen lineup, including very low-cost Flash models
Moonshot Kimi K3 1M Depends on plan Long context, vision, reasoning, OpenAI-compatible API
Z.ai GLM-5.3 Long context Depends on plan Coding, reasoning, long-horizon agentic tasks
Meta Muse Spark 1.3 Long context Public preview New first-party Model API, multimodality, search, and tools
Cohere Command A 256K $2.50 / $10 RAG, citations, reranking, enterprise use cases
Yandex YandexGPT 5.1 Pro Depends on model Regional pricing Russian infrastructure, AI Studio, agents, search, MCP
Sber GigaChat Depends on model Through Cloud.ru for new customers Russian infrastructure, multimodal, functions, documents

Do not read this table as a quality ranking. GPT-6 Sol, Claude Sonnet 5.5, Gemini 3.8 Flash, and DeepSeek V4.1 Flash occupy different price and architecture segments, and the same token count does not guarantee the same result. The table shows the scale of the market and how much providers’ approaches differ.

Anthropic Claude API

Claude remains one of the clearest direct competitors to OpenAI, especially for development, large context windows, and agent workflows. At the time this article was prepared, the current lineup includes Claude Sonnet 5.5, Opus 5.5, and more specialized models.

Claude Sonnet 5.5 supports up to 1 million context tokens and up to 128,000 output tokens. Its standard price is $2 per million input tokens and $10 per million output tokens. The heavier Opus 5.5 costs $4/$20, while Fable 5.1, designed for particularly difficult reasoning and long-running agent tasks, costs $10/$50.

The API supports image input, tool use, extended/adaptive thinking, and batch processing at a discount. Anthropic is also developing Claude Code and agent workflows, so Claude is worth considering as a platform for tool-using AI systems, not just as a text generator.

One important detail for developers is that Anthropic’s Messages API differs from OpenAI’s Responses API. Third-party gateways or infrastructure platforms can provide compatibility, but applications using Claude’s own capabilities generally need to account for Anthropic’s API.

Google Gemini API

The Gemini API may be the clearest example of how the idea of a “language model” has become outdated. Gemini 3.8 Flash accepts text, images, video, audio, and PDFs; has a context window of about 1.05 million tokens; and supports function calling, structured outputs, file search, code execution, Google Search grounding, Google Maps, and thinking.

Through December 31, 2026, Gemini 3.8 Flash has an introductory price of $0.75 per million input tokens and $3.75 per million output tokens. Rates of $1.50 and $7.50 are scheduled to take effect on January 1, 2027.

Its compatibility with the OpenAI SDK is also interesting. Google offers a beta interface that can make it possible to switch from OpenAI in simple scenarios by changing the API key, base URL, and model name. This does not guarantee full compatibility for every tool and parameter, but it lowers the cost of trying another provider.

For products that need text, images, audio, video, search, and document handling through one API, Gemini is worth testing even if the main backend already uses OpenAI.

xAI Grok API

xAI was initially seen mostly as the Grok ecosystem inside X, but its API is now a standalone platform. The current Grok 4.7 has a 500,000-token context window and costs $2 per million input tokens and $6 per million output tokens for a normal context size.

The model supports reasoning, function calling, and structured outputs. xAI tools provide web search, X search, and code execution. For requests with more than 200,000 context tokens, Grok 4.7’s price increases to $4/$12, which matters when comparing it with models offering million-token context windows.

xAI also has models with context windows of up to one million tokens, and the company is developing voice, image, and video offerings alongside its text API. Grok can no longer be viewed only as a niche model for X data, although X Search remains one of its distinctive features.

DeepSeek API

DeepSeek stands out primarily for the balance between price and capabilities. The current deepseek-flash corresponds to V4.1 Flash, supports up to 1 million context tokens and up to 384,000 output tokens, and offers JSON output, tool calls, the Responses API, and vision.

Its pricing is unusual because rates depend on the time of day. For V4.1 Flash during off-peak hours, one million input tokens without a cache hit cost $0.15 and one million output tokens cost $0.60. During peak hours, the price doubles to $0.30/$1.20. Cache hits cost even less.

DeepSeek’s support for OpenAI and Anthropic formats is especially useful for migration. This does not guarantee identical model behavior or support for every field, but it lets developers test DeepSeek without completely rewriting the client layer.

The low price makes the API interesting for bulk classification, document processing, generation, and background jobs. Still, comparing DeepSeek with more expensive models only by token price is misleading: the real measure is the cost of successfully completing a task at the required quality.

Mistral AI

Mistral is interesting because it combines a commercial API with a strong open-weight strategy. That matters to projects that want the convenience of a hosted API today but may need self-hosting or tighter infrastructure control later.

Mistral Medium 3.5 has a 256,000-token context window and costs $1.50/$7.50 per million input and output tokens. Mistral Large 3 is cheaper at $0.50/$1.50, and Small 4 costs $0.15/$0.60. The models offer structured outputs, function calling, Document Q&A, batch processing, and agent tools.

Another practical advantage is that some Mistral models are distributed with open weights. The choice is therefore broader than “which API should I call?” The same ecosystem can be used through a cloud service, a third-party inference provider, or your own infrastructure.

Alibaba Qwen and Model Studio

Qwen has long since moved beyond being “another Chinese model.” Alibaba Cloud Model Studio offers many models of different sizes and specialties, including Qwen3.8 Max and Qwen3.8 Flash.

Qwen3.8 Max supports up to 1 million context tokens and costs $2/$6 per million input and output tokens through the international API. Qwen3.8 Flash is much cheaper at $0.15/$0.47. Some models use tiered pricing based on input context length.

Qwen is especially interesting when a developer needs more than one general-purpose model: the lineup ranges from inexpensive bulk processing to large reasoning and multimodal tasks. Open Qwen models are also widely available from third-party inference providers and can be self-hosted.

Moonshot Kimi

Moonshot AI develops the Kimi family. Its current flagship, Kimi K3, supports up to 1 million context tokens, native image processing, reasoning, and structured output using JSON Schema. Its API is compatible with OpenAI clients, making Kimi relatively easy to add as an alternative backend.

One notable feature of K3 is its large supported max_completion_tokens, which makes it suitable for very long generations and multi-step tasks. Custom tools and automatic caching are supported.

However, platform features can develop separately from the model itself. For example, when this article was prepared, web search for K3 was being updated. A production system should not assume that every feature from older Kimi APIs is available in the new stack.

Z.ai and GLM

Z.ai develops the GLM family. GLM-5.3 is positioned primarily for coding, reasoning, and long agentic workflows. It supports several reasoning-effort levels, with thinking built into its normal operation.

GLM is also appearing at third-party inference providers, making the ecosystem interesting even without a direct commitment to Z.ai. For example, GLM-5.3 and Flash variants are available through several multi-model platforms.

There is a problem with direct price comparisons: Z.ai’s public pricing presentation changes and is often less transparent than OpenAI’s or Anthropic’s. It would therefore be inaccurate to list a third-party hosting price as “the GLM API price.” For a specific project, compare each access method separately.

Meta Model API

Meta was long known mainly as a provider of open Llama models, usually run through third-party providers. That changed in 2026 with the arrival of the Meta Model API, now in public preview.

The current Muse Spark 1.3 targets long-horizon agentic coding and multimodal tasks. The platform offers an OpenAI-compatible interface, structured output, parallel tool calling, built-in search, and multimodal input. Alongside the main model, Meta is developing separate Muse Voice and Muse Image APIs.

For now, this is a rapidly evolving new platform rather than a mature commercial API on the scale of OpenAI, Google, or Anthropic. Still, Meta’s first-party API matters: choosing Meta models no longer has to start with finding a third-party host.

Cohere: a distinct focus on RAG

Cohere is better viewed as a platform with a strong enterprise and RAG focus than as a universal replacement for OpenAI. Command A has a 256,000-token context window, supports tool use, structured outputs, reasoning, images, and citations, and costs $2.50/$10.

The ecosystem’s strength is not limited to its generative model. Cohere also has Embed and Rerank models, making the service a good fit for architectures where much of the value comes from searching enterprise data, ranking documents, and providing answers with sources.

That may not matter for a standard content generator. But if a product is built around complex search across an internal knowledge base, comparing only Command A’s price with GPT or Claude is too superficial.

Yandex AI Studio and GigaChat for Russian projects

Projects that need Russian infrastructure, local payment options, and local data processing should also consider Yandex AI Studio and GigaChat. These are not simply “Russian models”; they are platforms with their own tools and ecosystems.

Yandex AI Studio brings together Model Gallery, YandexGPT, Agent Atelier, AI Search, and MCP Hub. In 2026, Yandex expanded the platform’s pricing: different token types, caching, and agent tools are billed separately. An old comparison based on one price per thousand tokens no longer fully describes the cost of a complex workflow.

GigaChat supports multimodal work, function calling, batch processing, and document handling. Since September 1, 2026, commercial access for new customers has moved to Cloud.ru infrastructure, so current prices and available models should be checked there rather than in older GigaChat API price tables.

These platforms may not be the first choice for an international product. For a product that needs Russian infrastructure and local billing, however, they solve organizational problems that ordinary model benchmarks do not capture.

What does the same token volume cost?

Per-million-token prices are hard to interpret without a real example. Consider a request with 100,000 input tokens and 10,000 output tokens. This could be an analysis of a large document collection followed by a detailed report.

API and model Cost for this volume
OpenAI GPT-6 Sol $0.30
Claude Sonnet 5.5 $0.30
Gemini 3.8 Flash $0.1125
Grok 4.7 $0.26
DeepSeek V4.1 Flash, off-peak $0.021
DeepSeek V4.1 Flash, peak $0.042
Mistral Medium 3.5 $0.225
Qwen3.8 Max $0.26
Qwen3.8 Flash $0.0197
Cohere Command A $0.35

This is not a comparison of the cost of producing the same result. Models differ in quality, reasoning, speed, performance on specific tasks, and the number of tokens they actually need. Gemini 3.8 Flash is also on an introductory rate for now, and Grok costs more for contexts over 200,000 tokens.

Even so, the numbers illustrate one important point: the price difference between APIs can be an order of magnitude, not just a few percent. Choosing a production model based only on a general impression of a public chatbot is increasingly hard to justify.

Why token price is a poor metric on its own

A cheaper model may need more attempts, a longer prompt, an extra result check, or a later call to a stronger model. A more expensive model may solve the task in one request. In that case, the “expensive token” can cost less at the business-process level.

In addition to the base rate, account for caching, reasoning tokens, higher long-context rates, the cost of built-in web search and other tools, retries, rate limits, and response latency. In an agentic workflow, one user request can generate dozens of internal model and tool calls.

In production, measure the cost of a successful workflow: for example, the average cost to handle one customer request, review one contract, or prepare one product for publication at the required quality.

OpenAI-compatible APIs are becoming a standard of their own

One notable trend in 2026 is the spread of interfaces compatible with the OpenAI SDK. DeepSeek, Kimi, and Meta Model API have them; Google offers its own compatibility layer; and some infrastructure products support both OpenAI Responses API and Anthropic Messages API.

In practice, this can make an application’s architecture much less dependent on one provider. For an initial experiment, changing base_url, API key, and model name may genuinely be enough.

But “OpenAI-compatible” does not mean “fully interchangeable.” Providers differ in reasoning parameters, caching, tool use, structured outputs, context limits, image handling, and built-in tools. The more complex an application is, the less likely migration is to come down to one line of configuration.

A sound approach is to separate business logic from the LLM layer, maintain your own set of evaluation tasks, and test a new provider on real workflows before switching production traffic.

Multi-model platforms: when you should choose a gateway, not a model

A direct API has an obvious advantage: fewer intermediary layers and access to all of the model developer’s features. But it also creates dependence on one provider. If a product uses multiple models or needs to switch between them quickly, an aggregator or cloud AI platform can help.

OpenRouter

OpenRouter is one of the most straightforward options: a single API provides access to more than 500 models and over 80 providers. You can choose a specific inference provider, use automatic routing, and configure fallbacks.

OpenRouter’s standard plan has a 5.5% platform fee. The convenience of unified billing and routing therefore costs extra on top of the models themselves.

This approach is useful when an application needs to compare models easily or stay available if one provider fails. But if a product depends heavily on unique OpenAI Responses API or Gemini features, an extra gateway could instead limit access to those capabilities.

Together AI and Fireworks AI

Together AI and Fireworks AI sit between a simple API aggregator and full inference infrastructure. They provide access to many open-weight models while also offering dedicated deployments, fine-tuning, and more controlled hosting.

Together offers Qwen, Kimi, GLM, DeepSeek, and other families. Fireworks similarly combines serverless inference with fine-tuning and dedicated deployments.

These platforms are especially useful when a project wants to use open models but is not ready to run its own GPU infrastructure. As demand grows, a team can move from serverless to a more predictable dedicated deployment.

GroqCloud and Cerebras

Groq and Cerebras compete less on built-in business tools than on inference speed. That is a separate criterion that becomes critical for realtime interfaces, coding assistants, and agent loops with many sequential calls.

For example, Groq reports about 500 tokens per second for GPT-OSS 120B and about 1,000 tokens per second for the 20B variant. Cerebras reports around 3,000 tokens per second for GPT-OSS 120B.

Do not assume a stated speed applies to every prompt and workload: actual latency depends on input length, queues, and the model. Still, these platforms are worth testing when response time is part of the user experience, not merely a technical metric.

Hugging Face Inference Providers

Hugging Face Inference Providers turns the familiar Hugging Face ecosystem into one layer over several inference providers. A single interface can work with hundreds of models and let you select a backend or a strategy such as fastest or cheapest.

Hugging Face says it adds no markup to provider costs. This makes the platform convenient for experimenting with open models and for projects that already use Hugging Face Hub as their main model catalog.

Cloudflare AI Gateway and Workers AI

Cloudflare offers two related approaches. Workers AI runs models on Cloudflare infrastructure, while AI Gateway can sit in front of OpenAI, Anthropic, Google, and other APIs.

In this setup, Gateway handles logging, caching, rate limiting, routing, and unified billing. With Unified Billing, Cloudflare charges a 5% fee on credits purchased through the platform, without increasing the provider’s base token price.

For an application already running on Workers and Cloudflare, this layer may be simpler than separate AI infrastructure. But Gateway cannot make a weak model stronger: it is an infrastructure alternative, not a model alternative.

Microsoft Foundry

Microsoft Foundry is primarily an option for companies already using Azure. Its catalog includes Microsoft/OpenAI, Anthropic, DeepSeek, Meta, Mistral, Cohere, Hugging Face, and other providers. Deployments can be serverless or run on managed compute infrastructure.

The main advantage is usually not the lowest token price. Models become part of the same enterprise infrastructure for access control, networking, monitoring, policies, and corporate billing.

Amazon Bedrock

Amazon Bedrock serves a similar role inside AWS. The platform now supports more than 100 foundation models, including models from Amazon, Anthropic, DeepSeek, Moonshot AI, MiniMax, OpenAI, xAI, Meta, Mistral, Qwen, Z.ai, and others.

For new applications, AWS recommends the Bedrock runtime endpoint. In addition to its own Invoke and Converse APIs, the platform supports Chat Completions, Responses API, and Messages API for compatible models. This makes it much easier to migrate applications already built around OpenAI or Anthropic.

Bedrock makes particular sense when AI is one part of a larger AWS architecture and unified IAM, deployment regions, and enterprise infrastructure matter more than a direct contract with every model developer.

NVIDIA NIM

NVIDIA NIM differs from the platforms above: it is primarily a ready-made deployment layer for running models on your own or otherwise controlled GPU infrastructure. NIM for LLMs provides OpenAI-compatible endpoints, including /v1/responses and /v1/chat/completions, as well as the Anthropic-compatible /v1/messages.

This option is not for everyone. A serverless API is almost always simpler for a small project. But if an organization wants to control its hardware, network, data, and model lifecycle, NIM can preserve a familiar programming interface while removing an external inference service from the critical path.

Replicate

Replicate is difficult to compare with OpenAI using LLMs alone. Its strength is a large catalog of different model types: image generation and editing, video, audio, 3D, and language models.

Replicate has more than 100 official models with always-on endpoints, stable API schemas, and predictable pricing, as well as thousands of public models. For a project that needs several kinds of media models, this can be more convenient than integrating multiple specialized providers.

Specialized alternatives

Not every AI API needs to replace all of OpenAI. Sometimes a specialized service handles one part of a product better.

Perplexity API

Perplexity builds its API around search and current information. The platform offers a Search API for ranked results, an Agent API for complex workflows with tools and third-party models, and an Embeddings API. Sonar API remains a separate option for web-grounded answers.

If a product’s main task is to find current sources, filter the web, and build an answer with citations, Perplexity makes more sense to compare not only with GPT or Claude but also with a “LLM + separate search provider” setup.

Separate image, video, and voice APIs

OpenAI, Google, and xAI are gradually bringing multimodal capabilities into their platforms, but the specialized market is still active. Replicate, fal, and other inference services offer dozens of image and video model families; dedicated voice platforms include services such as ElevenLabs.

The choice of an “OpenAI API alternative” also depends on product architecture. One universal provider may reduce the number of integrations. In another case, a specialized model’s advantage in quality, speed, or price may fully justify a separate API.

Choosing an API for a specific project

There is no universal winner, but the right choice is often clear for a given use case.

If a project already uses the OpenAI SDK and you want to test alternatives quickly without major refactoring, start with providers and gateways that support an OpenAI-compatible interface: DeepSeek, Kimi, Meta Model API, Gemini’s compatibility layer, OpenRouter, and some infrastructure platforms.

If low-cost bulk text processing is the priority, the published rates make DeepSeek V4.1 Flash, Qwen3.8 Flash, and cheaper OpenAI tiers stand out. But test the model on your actual classification, extraction, or generation tasks instead of choosing by the price list alone.

If a product depends on long context, compare more than the advertised million-token window. Quality near the context limit, higher long-context rates, caching, and how much context is useful to send all matter.

AI agents make other factors critical: reliable tool use, structured output, reasoning, state management, web/file search, and the cost of a multi-step workflow. Differences between platforms can matter more than a few dollars per million tokens.

If self-hosting is required, look at the open-weight ecosystems from Mistral, Qwen, DeepSeek, Kimi, and others, then choose how to run them: your own vLLM, NVIDIA NIM, Together, Fireworks, Hugging Face, or another inference provider.

For Russian projects with local billing and infrastructure, Yandex AI Studio and GigaChat remain separate candidates. For applications deeply integrated with AWS or Azure, it is often more practical to evaluate Bedrock or Microsoft Foundry first, because IAM, networking, and compliance may matter more than the lowest token price.

What I would build into the architecture of a new AI application

The most important market change in 2026 is not that one model appeared that is “better than ChatGPT.” It is that choosing a single provider forever is no longer necessary.

For a new application, separate model access from core business logic. An internal application layer might receive a task such as “extract data from this document” or “prepare an answer using search,” then choose the appropriate backend and translate the request into OpenAI, Anthropic, Gemini, or another API’s format.

That does not mean building a complex universal router on day one. It is enough to avoid scattering one SDK’s provider-specific fields across the project, keep model configuration separate, and maintain a small set of real test tasks.

The next important element is evals. A dozen or a hundred representative examples from your own product are more useful than an abstract leaderboard. Use them to test a new API and measure not only answer quality but also latency, average output length, retries, and total cost.

Model selection then becomes much less emotional. One model can handle inexpensive bulk operations, another can handle difficult requests, and a third can be used only as a fallback. Switching providers no longer requires rewriting half the application.

What changed since the era of “one ChatGPT API”

In 2026, the AI API market can no longer be reduced to choosing GPT or one or two alternatives. Claude, Gemini, Grok, DeepSeek, Mistral, Qwen, Kimi, GLM, and Meta are developing independent platforms. Yandex and GigaChat serve local use cases, while OpenRouter, Bedrock, Foundry, and other services separate model selection from infrastructure choice altogether.

That makes “which API is best?” a less useful question. For a real product, a more important combination is how well the model solves a specific task, how much the whole workflow costs, its latency, the tools it needs, where data is processed, and how difficult it will be to switch providers six months from now.

The most practical strategy is not to guess a single market winner. Build a small set of your own evaluation tasks, test several suitable models, and leave the architecture able to switch between them. The cost of that flexibility is much lower than it was a few years ago, and the choice of APIs is much broader.

Main official sources

Related: a detailed Anthropic Claude API review.

Related: a detailed Google Gemini API review.

Related: a detailed xAI Grok API review.

Related: a detailed DeepSeek API review.

Date of publication:

Alternatives to OpenAI API and ChatGPT API in 2026: Claude, Gemini, Grok, DeepSeek, and More

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