Comparison of the three major APIs of OpenAI, Anthropic, and Google, and actual measurement of large model selection in 2026

By 2026, large-model APIs are already everyday infrastructure for developers. OpenAI, Anthropic, and Google are often lined up and compared on the international market, but when you get down to the specifics of sign-up barriers, model lineups, price ranges, rate limits, compliance, and long-context support, the differences are substantial. This article won't cite specific numbers that could be wrong; instead, it explains the trade-offs of the three companies along the dimensions that actually matter when developers choose a model, and it also touches on how Chinese developers should handle the "can't connect to overseas APIs" problem.

A Rough Positioning of the Three Companies' Model Lineups

All three vendors currently follow a three-tier structure of "flagship + mid-range + ultra-fast and ultra-cheap." The specific model names are constantly being updated, so refer to the official pages. The rough correspondence is as follows:

OpenAI's flagship series handles the heavy lifting of multimodality, complex reasoning, agents, and so on. The mid-range series is far cheaper than the flagship and is suited to high-volume requests. There's also a dedicated reasoning-optimized series, an image-generation series, and a speech-transcription series spread across different product lines.

Anthropic's Claude series also has three tiers: the flagship (the Opus family) focuses on coding and complex tasks, the balanced type (the Sonnet family) is the everyday workhorse, and the ultra-fast and ultra-cheap (the Haiku family) suits large volumes of lightweight calls. Version numbers change frequently, so it's best to check the current list of models published on Anthropic's official site directly.

Google's Gemini series is likewise split into flagship (Pro), balanced (Flash), and ultra-small (Flash Lite), plus an on-device Nano used on Android devices. Google opens up its models through two paths simultaneously: AI Studio and Vertex AI.

If you only look at "who's stronger on the mainstream leaderboards," the gap between the three has been compressed to very little, and different leaderboards often contradict each other. We won't cite specific scores here, and later we'll explain how to test for yourself.

Differences in the Sign-Up and Key-Acquisition Experience

The main differences in the three companies' developer sign-up flows show up in regional support and payment methods.

OpenAI provides its API through platform.openai.com. With an overseas credit card, you can get a key immediately after signing up. There has never been a direct channel for mainland China accounts; you typically need an overseas identity and an overseas card to use it normally.

Anthropic provides its API through console.anthropic.com. The flow is similar, requiring email and phone verification. There is currently likewise no direct-connect channel for mainland China; using it usually requires an overseas legal entity or a third-party proxy.

Google offers two paths: AI Studio has the lowest sign-up barrier, usable with just a Google account, comes with a free quota, and suits prototyping; production use usually migrates to Vertex AI, which requires binding a GCP project and a payment method.

As for "compliant versions" within China, the more stable options right now are the OpenAI series via the Microsoft Azure China edition, and the Anthropic series via AWS Bedrock in some overseas regions. Google Vertex AI does not offer a domestic compliant version in mainland China. The specific available regions and model lists are updated by each company, so it's best to reconfirm with the corresponding cloud vendor before procuring for compliance.

If your business is itself based in China, the most direct way to bypass this layer is to use domestic alternatives: series like Zhipu GLM, DeepSeek, Moonshot's Kimi, Alibaba's Qwen, and ByteDance's Doubao all have smooth sign-up flows, accept domestic payment methods, and many vendors already offer interfaces compatible with the OpenAI SDK, so you can switch just by changing the endpoint.

The Overall Landscape of Rate Limits and Concurrency

The three companies have rather different design philosophies for rate management.

OpenAI uses a tiered system. Based on cumulative historical spending and account age, you're automatically upgraded; the higher the tier, the higher your requests per minute and tokens per minute. A new account starts off fairly conservative in rate, suiting a low-traffic start, after which you can apply for an increase.

Anthropic doesn't have an explicit tier table like OpenAI's, but it does have rate limits by account and model. If you need a higher rate, you can submit a request, and production users can also go through enterprise sales to get a custom quota. Anthropic also has a Batch API: submit non-real-time tasks in bulk and the price is considerably cheaper.

Google's quotas on Vertex AI can be checked in the GCP console, and before production you usually need to separately apply to raise the quota to the level your business needs. AI Studio suits prototyping; don't rely directly on the free tier for production.

As for latency and stability, the three differ significantly across different times and regions. Don't trust any numbers a blog post gives for this kind of thing; the only meaningful results come from running your real production traffic live for a week.

Context Windows and Long-Document Handling

All three flagships currently support million-token context, with the specific limits and pricing strategies to be confirmed from the official announcements. Note that the window ceiling is "how much you can stuff in," which is not the same as "accuracy doesn't drop once it's full."

In experience, the first thing to lose accuracy in long-context scenarios is "finding one or two details within a very long document," that is, the commonly mentioned needle-in-a-haystack type of task. All companies have made optimizations in this direction, but real-world differences still exist. If your business is large-document retrieval or long-meeting-minutes analysis, it's worth running a comparison with your own real documents rather than trusting any static evaluation.

The "structured-output capability" on the output side is also worth attention. Which company is more reliable when you have the model generate JSON, tables, or Markdown directly affects the complexity of your downstream parsing code. The overall level of all three is rising, but for the specific schema you commonly use, you still need to test with your own data.

On price, the cost of long-context input can be very different. Google has historically been relatively cheap in "lots of input + little output" scenarios, Anthropic's flagship leans expensive, and OpenAI is in the middle. But the price sheets update very frequently, so we won't cite specific numbers in this article; refer to the three companies' pricing pages.

Function Calling and Tool Use

All three support function calling / tool calling, with slightly different design styles.

OpenAI puts tool definitions under a tools field; the model decides whether to call and with what parameters, and in streaming mode it returns the call JSON incrementally. The ecosystem is mature; frameworks like LangChain and LlamaIndex treat it as a first-class citizen by default.

Anthropic's tool_use is a structured content block; the model returns structured fields directly rather than a string, making code handling a bit cleaner, and it supports returning multiple parallel tool calls at once.

Google puts function calling under the tools config, managed uniformly alongside other multimodal fields. When using it in Vertex AI, you first have to adapt to GCP's whole set of authentication and project management.

In actual development, the differences are mainly in SDK style and ecosystem maturity, not a fundamental gap in capability. If your code is already built on one company's SDK, switching to another requires writing an adaptation layer. There's already plenty of open-source middleware that does this; just pick one you trust.

The Division of Labor in Multimodal Capabilities

To put it bluntly and simply:

OpenAI has the most complete product line: text, image understanding, image generation, voice input/output, and video generation all have dedicated products, but they're spread across different models, so combining them feels more like assembling an ecosystem.

Anthropic mainly puts its energy into text and image understanding, plus deep scenarios like coding and long documents. Its support for video and native audio isn't as strong as the other two. If your application is mainly text and image and you have high demands on coding or reasoning quality, Claude is a very smooth choice.

Google moves rather aggressively in the native-multimodal direction, handling text, image, audio, and video uniformly within a single model, and it's the most complete in video and audio scenarios.

Projects that need a complete multimodal loop can be covered fairly well by Google alone; if you need dedicated image generation or video generation, OpenAI's corresponding products are more mature; for projects centered on code, long-form text, and writing, Anthropic is usually the first choice.

A Few Rules of Thumb on Pricing Strategy

The price sheets update very frequently, so don't memorize specific numbers. Here are a few experiential judgments you can use for rough estimates:

• At the same tier, Google's Flash/Flash Lite series usually has the lowest cost in "huge volumes of lightweight requests" scenarios.
• Anthropic's flagship output price is generally on the high side, but in scenarios where you get it right in one go and reduce retries, the actual spend isn't necessarily more than competitors'.
• OpenAI's overall price is in the middle, and after a new model launches the old model often gets a corresponding price cut.
• Long-context input pricing varies widely among the three; before doing large-document processing, you must run a separate cost calculation.
• All three have batch / async-task discounts; route non-real-time tasks this way as much as possible.

If you really want to nail down the cost, the approach is to estimate your business's monthly total input tokens, total output tokens, the proportion of long context, and whether batching is possible, then run all three candidate tiers through each company's current price sheet, rather than choosing on impression.

What Each Company Is Best Suited For

Stripping away specific model names and scores, looking only at the big-picture trade-offs:

For coding-assistant tools, the Claude series has the most stable reputation in the mainstream developer community. Mainstream IDE tools like Cursor, Windsurf, and Aider recommend it by default, and there's a reason for that. The Pro tier costs around twenty dollars a month; refer to the official page for specifics.

For general-purpose conversational products and chatbots, OpenAI got the earliest start in user experience, ecosystem, and plugins, and has the broadest SDK compatibility. If you're building a consumer-facing conversational product, starting with it is almost never wrong.

For scenarios like long-document processing, contract review, podcast transcription summaries, and video-content analysis, Google's advantages in long-context pricing and native multimodality are fairly clear, while Anthropic is more solid on "can the accuracy actually deliver."

For Chinese-centric scenarios, domestic models are already good enough, and the price is considerably lower than overseas. The advice is to keep a domestic model as at least a fallback, leaving one off the main path.

On enterprise-grade compliance, OpenAI through Azure, Anthropic through AWS Bedrock, and Google through Vertex AI/GCP all have corresponding compliance and data-isolation solutions; which one suits you depends on what contracts your existing cloud vendor has already signed, not on the model itself.

How to Run Your Own Evaluation

Don't trust anyone's (including this article's) take on "who's stronger." The simplest approach is:

Step one, pick 30 to 50 real samples from your own business, each with a clear standard for what counts as a "good answer" versus a "bad answer."

Step two, run the same prompt across six models, the three flagships and three mid-range, and collect all the answers.

Step three, put the answers together with the ground truth and do a blind evaluation (assess it yourself or ask colleagues; the key is not seeing the model names), then tally the results by your business metrics.

Step four, calculate "quality" together with "price + rate" to see which company offers the best value, rather than just picking the highest score.

This whole process can be done within a week, and the conclusions it yields are closer to your business than those of any evaluation agency.

The Overall Trends for the Three Companies Over the Next Year

Without predicting specific version numbers, a few things are certain:

Prices will keep falling. The mid-range and ultra-cheap tiers will handle more and more requests, and the flagship tier will gradually become a "quality backstop."

Long context and native multimodality will further become baseline capabilities rather than premium selling points.

Becoming agentic is a shared direction for all three; tool calling, long-process task execution, and multi-step reasoning will increasingly be supported natively at the model layer rather than cobbled together through prompt engineering.

The position of domestic models will keep rising. Their price advantage plus their Chinese-scenario advantage will push them to become the default option for many businesses, while overseas flagships will hold the "quality first" scenarios.

In one sentence: for choosing a large-model API in 2026, there's no standard answer for "which is strongest," only "which fits your business best." Run your own business samples first, then decide on the main path and the fallback. That's more useful than any evaluation leaderboard.

Frequently Asked Questions

Should I start with one company or use all three in a mix?

The advice is to pick one and get your business working first. Mixed routing adds architectural complexity, so only do it if it's worth it. If your business scenario is singular, one is enough; if your business scenarios are varied, such as doing both a coding assistant and long-document retrieval, it's reasonable to use Claude for coding and Gemini for long text. For small and mid-sized projects, prioritize simplicity; don't chase multi-model routing right out of the gate.

What should domestic developers do if they can't call all three directly?

Roughly three directions. One is through cloud vendors' compliant versions, such as the OpenAI service on Azure's China edition or some Claude models on AWS Bedrock; the specific available models and regions are subject to the latest official announcements. Two is connecting to domestic APIs directly through the OpenAI-compatible protocol; many vendors like DeepSeek, Kimi, and Zhipu support switching just by changing the endpoint, with no code changes. Three is connecting directly through an overseas legal entity, a path suited to teams that already have an overseas business. Most small and mid-sized developers actually choose to use domestic alternatives directly.

Claude is so much more expensive than OpenAI—is it still worth using?

It depends on the scenario. In coding, long documents, and scenarios requiring high-quality structured output, Claude has a higher probability of giving the right answer in one shot, which means fewer retries, and the actual total API spend isn't necessarily higher than a cheaper-tier competitor's. Using the flagship for everyday chat and high-frequency simple requests is a waste; the Sonnet or Haiku tier is more appropriate.

Gemini's price advantage is so obvious—why isn't it the default choice?

Historical and ecosystem reasons. OpenAI got the earliest start and has the broadest SDK compatibility; mainstream frameworks like LangChain and LlamaIndex connect to OpenAI by default. Vertex AI also requires pairing with GCP, raising the barrier a bit above connecting directly to OpenAI. But in 2026 the Gemini flagship has caught up in strength, its price advantage is obvious, and in scenarios like long documents, native multimodality, and batch requests, more and more new projects are making it the default choice.

What do I do if my API key leaks?

Use three layers of protection together. First, the key should never go into code or git; put it in environment variables or a secret manager. Second, separate production keys from development keys; restrict production keys to an IP allowlist or use trusted server-side calls. Third, all three support setting a monthly spending cap, so even if a key leaks, the loss has a ceiling. The moment you discover a leak, immediately revoke it in the admin console and create a new key, then check the logs to find the cause of the leak, which is usually hardcoding in config or code.

Inspiration source: Ruan Yifeng's "Weekly Issue for Tech Enthusiasts," Issue 394 https://www.ruanyifeng.com/blog/2025/10/weekly-issue-394.html