What Does AI ACTUALLY Cost in 2025? Your Guide on How to Find the Best Value (API vs. Subs vs. Team plans and more)

Anthropic just released a new Claude Max plan, a premium tier for their popular AI model that comes in two options—$100/month for 5× the limits of the Pro tier, or $200/month for 20× higher limits​.

That's right: you can now easily autopay two Benjamins a month to chat with an AI, which also matches up with ChatGPT’s $200 a month Pro tier.

It begs the question: Is AI getting insanely expensive, or is the pricing just... weirdly complicated now? Short answer: Yes.

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First, the Max Plan deets:

1. Claude Max subscribers get priority access to new features and models, though Anthropic’s Max is still usage-capped (unlike ChatGPT Pro)​.
2. The usage limit increases are the big deal here, because anyone seriously using Claude at the Pro level complains constantly about getting cut off.
3. But the most interesting aspect of all is the mid-tier—that new $100 a month tier—because It essentially forces the hands of other providers to lower their costs, too.

And by other providers, we mean OpenAI.

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Who should pay $100-$200 a month? These plans clearly target the power users—folks running complex coding projects (like those CS students Anthropic just found using Claude heavily), those doing constant deep research, or needing near-unlimited access to the smartest models like Claude 3.7 Sonnet or OpenAI's o1 series.

For them, hitting usage caps is a workflow killer, justifying the premium.

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But below that top tier, the landscape is a confusing mix:

First there’s the $20 Club:

• This seems to be the standard “prosumer” AI price point.
• This plan offers more usage than free tiers and often bundles features or ecosystem perks.
• ChatGPT Plus, Claude Pro, Microsoft Copilot Pro, Perplexity Pro, and Google One AI Premium (with Gemini Advanced) all hover around $20-30 per month.

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Then there’s the pay-per-sip scene (APIs):

• This is what we'd call the "developer tier." 
• This tier prices AI based on usage as opposed to a monthly subscription.
• You would use this pricing scheme only if you need to work with the AI providers inside another application (like your own app or something like Cursor).

To use this, you connect to the AI company's application programming interface, or API, to directly communicate with their servers and stream AI answers from them to your app (using a special key that's private to you and the AI company; it's kinda like a bouncer who checks to see if you're on your list, or if you live in Los Angeles, checks to see if you have enough Instagram followers before letting you into the club).

You share that private key, call the AI service, and then you can stream its answers to your questions inside your own applications; pretty cool, huh? 

Keep in mind, when using AI services via API, you're usually charged per token. Think of tokens as pieces of words; about 100 tokens make up 75 words. Easy, right?

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Now here’s the catch: Most model providers charge different rates for the tokens you send in (your prompt, called input tokens) versus the tokens they send out (the answer, or output tokens).

Because of this, API costs are wildly variable.

1. Brace yourself for OpenAI's high-end o1 ($15 / $60 per M)—the outdated Claude Opus is the same.
2. Mid-range powerhouses like Claude Sonnet ($3 / $15 per M) or GPT-4o ($2.50 / $10 per M) are more moderate.
3. And for simple stuff? Models like Claude Haiku ($0.25 / $1.25 per M) or Google's Gemini Flash (under $0.10 / $0.40 per M) are dirt cheap.
4. Need the absolute best? Gemini 2.5 Pro is about $1.25 / $2.50 per M and $10 / $15 / winds up at around $3.44 / M tokens.

And because of the annoying nature of these differing input / output costs, to compare apples-to-apples, sites like Artificial Analysis calculate a “blended” price per million tokens (M tokens).

This assumes a typical mix of input and output (like 3 parts input to 1 part output) to give a single cost number.

Here are how some key models stack up using those blended rates:

• Top Shelf Intelligence: Google's Gemini 2.5 Pro Experimental leads the pack on smarts and comes in at a blended $3.44 / M tokens.
• High-End Heavyweights: Need serious power and willing to pay? OpenAI's o1 engine costs a hefty $26.25 / M tokens, and Anthropic's Claude 3 Opus isn't far behind at $30.00 / M tokens.
• Great All-Arounders: Mid-range powerhouses like Claude 3.7 Sonnet ($6.00 / M tokens) or GPT-4o (the Nov '24 version blends to $4.38 / M tokens) offer a solid balance of capability and cost.
• Dirt Cheap Options: For simpler tasks? Claude 3.5 Haiku ($1.60 / M tokens) or Google's Gemini 2.0 Flash ($0.17 / M tokens) get the job done for pennies.

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Then there's the open source Wild West. Models like Meta's Llama family (including the new Llama 4), DeepSeek, and Mistral can be incredibly powerful and potentially much cheaper via API... if you pick the right host.

Luckily, Artificial Analysis compares all the cloud model pricing in one place!

For example:

• Running Llama 3.3 70B could cost around $0.17 per million tokens (blended input/output) on a specialized host like Lambda.
• The same model on AWS Bedrock? Closer to $0.71 per million tokens (calculated from their per-1k token pricing). That's over 4x the cost!

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And what about Meta's new Llama 4 models (Scout & Maverick)?

• If you want Llama 4 Scout for dirt cheap, CentML offers it at $0.10 / M tokens.
• Want Scout as expensive as possible? Kluster.ai offers it at $0.71 / M tokens.
• That’s a 7x price difference for the same model depending only on the provider!

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So now, let's analyze the Artificial Analysis data to find the models offering the best intelligence for the price.

First, a quick reminder on the data:

• Intelligence Index: Higher is better (scale seems roughly 0-70+).
• Blended Price: USD per 1 Million Tokens, assuming a 3:1 input-to-output ratio. Lower is better.
• Provider Matters: The same model can have vastly different prices depending on the API provider hosting it.

1. Max Intelligence Overall (Highest Index Score, regardless of price):
These are simply the models with the highest intelligence scores.

• Gemini 2.5 Pro Experimental (Google): Index 68 ($3.44/M)
• o3-mini (high) (OpenAI / Microsoft Azure): Index 66 ($1.93/M)
• o3-mini (OpenAI / Microsoft Azure): Index 63 ($1.93/M)
• o1 (OpenAI / Microsoft Azure): Index 62 ($26.25/M)
• DeepSeek R1 (Multiple Providers): Index 60 (Price varies, e.g., ~$0.95/M on Lambda/Deepinfra, $2.36/M on Azure/Bedrock)

2. Best Value (Highest Intelligence per Lowest Cost):
These models offer the most intelligence points per dollar spent (Index Score / Blended Price). Models with very low index scores are generally excluded even if their price is near zero.

• QwQ-32B (Alibaba via Deepinfra): Index 58 / $0.14 ≈ 414 points per dollar
• Llama 4 Maverick (Meta via CentML FP8): Index 51 / $0.20 ≈ 255 points per dollar
• Llama 4 Scout (Meta via CentML): Index 43 / $0.10 ≈ 430 points per dollar (Note: Lower index than Maverick but even better value score)
• Gemini 2.0 Flash (Google AI Studio): Index 48 / $0.17 ≈ 282 points per dollar
• DeepSeek V3 (Mar '25) (DeepSeek via Deepinfra): Index 53 / $0.52 ≈ 102 points per dollar

(Note: Extremely cheap, lower-intelligence models like Llama 3.2 1B/3B on Nebius/Deepinfra have astronomical value scores but might not meet a minimum intelligence bar for many tasks).

3. Best Value - Open Source Only (Highest Intelligence per Lowest Cost):
Same calculation as above, but limited to models identifiable as open-source.

• QwQ-32B (Alibaba via Deepinfra): Index 58 / $0.14 ≈ 414 points per dollar
• Llama 4 Scout (Meta via CentML): Index 43 / $0.10 ≈ 430 points per dollar
• Llama 4 Maverick (Meta via CentML FP8): Index 51 / $0.20 ≈ 255 points per dollar
• DeepSeek V3 (Mar '25) (DeepSeek via Deepinfra): Index 53 / $0.52 ≈ 102 points per dollar
• DeepSeek R1 (DeepSeek via Deepinfra / Lambda Labs): Index 60 / ~$0.95 ≈ 63 points per dollar

Alternative Analysis (Finding the "Sweet Spots"):

This pure "Intelligence / Price" ratio heavily favors extremely cheap models. Here's another way to look at it, finding models that hit a good balance:

A. Top Tier Value (High Intelligence, Relatively Low Cost):
Models with an Index Score above 55, sorted by lowest blended price.

• QwQ-32B (Alibaba via Deepinfra): Index 58 / $0.14
• DeepSeek R1 (DeepSeek via Deepinfra / Lambda Labs): Index 60 / ~$0.95
• o3-mini (high) (OpenAI / Microsoft Azure): Index 66 / $1.93
• o3-mini (OpenAI / Microsoft Azure): Index 63 / $1.93
• Gemini 2.5 Pro Experimental (Google): Index 68 / $3.44
• Claude 3.7 Sonnet Thinking (Anthropic / Amazon Bedrock): Index 57 / $6.00

B. Strong Mid-Tier Value (Good Intelligence, Very Low Cost):
Models with Index Score 45-55, sorted by lowest blended price.

• Llama 4 Maverick (Meta via CentML FP8): Index 51 / $0.20
• Gemini 2.0 Flash (Google AI Studio): Index 48 / $0.17
• DeepSeek V3 (Mar '25) (DeepSeek via Deepinfra): Index 53 / $0.52
• Llama 3.3 70B (Meta via Nebius Base / Deepinfra Turbo FP8): Index 41 / $0.20 (Slightly below threshold but great price)
• DeepSeek V3 (Dec '24) (DeepSeek via Hyperbolic FP8 / Nebius): Index 46 / $0.25 - $0.75

C. Budget Performers (Acceptable Intelligence, Dirt Cheap):
Models with Index > 30, sorted by lowest blended price.

• Llama 3.2 1B / 3B (Meta via Nebius Base / Deepinfra): Index 10-20 / ~$0.01 per M tokens
• Gemma 3 4B (Google via Deepinfra): Index 24 / $0.03 per M tokens
• Ministral 3B (Mistral): Index 20 / $0.04 per M tokens
• Mistral 7B (Mistral via Deepinfra): Index 10 / $0.04 per M tokens
• Gemma 2 9B (Google via Nebius Base / Deepinfra): Index 22 / $0.03 - $0.04 per M tokens

So, in sum...

• For absolute top intelligence, Google's Gemini 2.5 Pro and OpenAI's o3-mini (high) lead, but o3-mini (high) is cheaper.
• For the best raw value (smarts per dollar), open-source models like Alibaba's QwQ-32B (on Deepinfra) and Meta's Llama 4 Scout/Maverick (on CentML) are incredibly compelling, offering high-mid tier intelligence at rock-bottom prices. Google's Gemini 2.0 Flash is also excellent value in the proprietary space.
• If you need high intelligence but want to optimize cost, DeepSeek R1 (on cheaper providers) and o3-mini (high) offer strong alternatives to the most expensive models.
• If cost is the absolute priority, many smaller open-source models (Llama 1B/3B, Gemma, smaller Mistrals) are available for fractions of a cent per million tokens, providing decent capabilities for basic tasks.

Remember, these blended prices assume a 3:1 input/output ratio. Your actual costs will vary based on usage patterns!

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Enterprise plan level: And then there's the enterprise plan. Let's say you're a company and you want to use Anthropic. You want your whole team to be able to share prompts and project knowledge. You don't have to buy them all Pro account. Teams can opt for Claude Team at $30/user/month (min 5 users, $25 if annual)​, which includes centralized billing, shared knowledge bases, and the full 200k-token context window for collaborative workflows​.

Every major AI provider has a plan like this. But some of them also have "enterprise plans", which are basically a fancy way to charge big companies a lot more money for AI. The easiest way to compare and contrast all the different team and enterprise plans is to list them out, like so: ‍

Anthropic

• Team
  • Price: $30/user/month (monthly) or $25/user/month (annual)
  • Minimum Users: 5
  • Base License Required: No
  • Key Notes: Higher usage than Pro, central billing
• Enterprise
  • Price: Custom
  • Billing: Custom
  • Minimum Users: Custom
  • Base License Required: No
  • Key Notes: Enhanced security (SSO, SCIM, Audit Logs), control, larger context window

OpenAI

• Team
  • Price: $30/user/month (monthly) or $25/user/month (annual)
  • Billing: Monthly or Annual
  • Minimum Users: 2
  • Base License Required: No
  • Key Notes: Shared workspace, admin console, data not used for training
• Enterprise
  • Price: Custom (~$60/user/month reported, unconfirmed)
  • Billing: Custom (Annual?)
  • Minimum Users: 150 (reported)
  • Base License Required: No
  • Key Notes: Unlimited GPT-4o, enhanced security (SSO, SOC2), API credits

Google

• Google Workspace (with Gemini)
  • Price: Business Standard = $14/user/month annual ($16.80 monthly), while Plus = $22/user/month annual ($26.40 monthly)
  • Billing: Monthly or Annual
  • Minimum Users: 1
  • Base License Required: Yes (Workspace)
  • Key Notes: Integrates Gemini into Workspace apps

• Gemini Enterprise
  • Price: variable, but last clocked at $36/user/month (flexible) or $30/user/month (annual)
  • Billing: Monthly or Annual
  • Minimum Users: 1
  • Base License Required: Yes (Workspace)
  • Key Notes: Integrates Gemini, includes Gemini Advanced features

Microsoft

• Copilot for Microsoft 365
  • Price: $31.50/user/month (monthly commit) or $30/user/month (annual commit)
  • Billing: Annual Commitment
  • Minimum Users: 1
  • Base License Required: Yes (Qualifying M365)
  • Key Notes: Integrates with M365 apps & data, includes Copilot Studio
• Copilot for Sales/Service
  • Price: $50/user/month (standalone) or $20/user/month (add-on to Copilot for M365)
  • Billing: Annual Commitment
  • Minimum Users: 1
  • Base License Required: Yes (for add-on price)
  • Key Notes: Role-specific features

Mistral AI

• Le Chat Team
  • Price: $24.99/user/month
  • Billing: Monthly
  • Minimum Users: Not specified
  • Base License Required: No
  • Key Notes: Higher limits than Le Chat Pro
• Enterprise
  • Price: Custom
  • Billing: Custom
  • Minimum Users: Custom
  • Base License Required: No
  • Key Notes: Likely platform/API focused, potential self-deployment

Perplexity

• Enterprise Pro (< 250 users)
  
  • Price: $40/user/month (monthly) or ~$33.33/user/month (annual, $400/yr)
  • Billing: Monthly or Annual
  • Minimum Users: 1
  • Base License Required: No
  • Key Notes: SSO, SOC2, Enhanced Privacy, Internal Knowledge Integration
• Enterprise Pro (>250 users)
  • Price: Custom
  • Billing: Custom
  • Minimum Users: >250
  • Base License Required: No
  • Key Notes: Tailored quote required

xAI

• Access via X Plans
  • Price: X Premium+: $50/month, SuperGrok: $30/month ($25 annual equivalent)
  • Billing: Monthly or Annual
  • Minimum Users: 1
  • Base License Required: No
  • Key Notes: Individual plans; Enterprise API planned, pricing TBD

Cohere

• Enterprise Platform
  • Price: Custom
  • Billing: Custom
  • Minimum Users: N/A
  • Base License Required: No
  • Key Notes: API/Platform focused, RAG specialization, deployment flexibility

IBM

• watsonx Components
  • Price: Varies (RU, Instance, MAU based)
  • Billing: Not specified
  • Minimum Users: Not specified
  • Base License Required: Not specified
  • Key Notes: Not provided

There's an old saying that goes "no one ever gets fired for buying IBM", so worst case scenario and all other analysis fails... go with IBM.

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Here's where all that leaves you, the would-be AI buyer: The good news is fierce competition, especially in the API space, is driving down per-token costs. The bad news? Comparing plans and providers requires serious homework. Stack up your free credits and plans wisely!

For example: did you know you can use Claude 3.7 for free about ~7 times per day with a free account? Or did you know you could use Perplexity’s “Pro Search” feature about 3 times a day with a free account?

And some models you can run entirely for free in Open Router, or run quantized (meaning condensed) versions of local models on your computer with LM Studio or Ollama.

We also asked Deep Research to compile all the free plan criteria and free credits available for all the major AI providers, and you can view that here.

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So we guess the answer to our article's headline = forget a single “price of AI.” The cost depends entirely on what you need (basic chat vs. complex reasoning), how much you use it, and how you access it (subscription vs. API vs. hosted open source).

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BTW: Did you know you can use Gemini 2.5 Pro with Deep Research now? In theory, this should be a lot better than the O.G. “D.R.” Gemini launched with (which was pretty good at the time of launch, but then got smoked by ChatGPT DR).

Gemini DR might be the best way to use Gemini Thinking with Google Search, as we’ve found with subsequent testing that “Grounding with Google Search” in AI Studio can be really hit or miss.

Anyway, we used both ChatGPT Deep Research and Gemini Deep Research to, you guessed it, research this report.

Here are the full reports for both (GPT DR, Gemini DR) so you can compare their findings.

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Bottom line: There's no magic “best value” AI. It all depends on your needs.

Running simple stuff constantly? Check out Gemini 2.0 Flash, GPT-4o mini, Claude Haiku, or Llama 3 8B on a budget host.

Need brainiac-level reasoning? Prepare your wallet for Gemini 2.5, ChatGPT o1, or grab a premium sub to Claude or ChatGPT.

And always, always compare providers, abuse those free tiers and credits , and look for cost-saving hacks like prompt caching.

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P.S: This is a 1.0 version of this guide. We'll try to improve it over time; but not bad for something we threw together in a couple hours, huh? 

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