Cost of Top 10 LLMs | Compare & Find the Best Budget Option

Are you burning money without even realising it? Let’s talk about the cost of top 10 LLMs and how quickly those seemingly tiny per-token fees can add up.

The rise of Large Language Models (LLMs) has revolutionized how businesses and developers approach AI solutions. But with great power comes great… expense? Understanding the cost of top 10 LLMs isn’t just for budgeting but it’s essential knowledge for choosing the right model for your specific needs.

Costing of Top 10 LLMs per Million Tokens

The prices listed below are in unites of per 1M tokens. A token, the smallest unit of text that the model recognizes, can be a word, a number, or even a punctuation mark.

LLM Name	Input Cost ($/M Tokens)	Output Cost ($/M Tokens)	Total Cost ($/M Tokens)
Gemini 2.0 Flash-Lite	0.075	0.30	0.375
Gemini 2.0 Flash	0.10	0.40	0.50
DeepSeek R1	0.55	2.19	2.74
DeepSeek-V3	0.27	1.10	1.37
Grok-2	2.00	10.00	12.00
Qwen 2.5-Max	1.60	6.40	8.00
OpenAI o3-mini	1.10	4.40	5.50
Claude 3.7 Sonnet	3.00	15.00	18.00
GPT-4.5	75.00	150.00	225.00

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What are Tokens?

Let’s understand from a basic query that you would ask from an LLM.

Think of a token as approximately 4 characters or about 3/4 of a word in English. In the above example, 10 to 11 tokens are used for a this query.

This is an approximation—actual token counts depend on the specific tokenizer used by the LLM (e.g., OpenAI’s tiktoken for GPT models)

You might be thinking, Why Calculate is only 1 token? Shouldn’t it be broken into 4 characters?

LLMs use a tokenizer that is not strictly character-based. Instead, it uses a subword-based approach (like Byte Pair Encoding or Unigram models). This means:

• Common words (like “Calculate”) are often stored as single tokens because they appear frequently in training data.
• Uncommon or long words might get broken down into smaller subwords (e.g., “unbelievable” → "un", "believ", "able").
• Rare words, numbers, or symbols (e.g., “XyZ#2024!”) may be split into multiple tokens.

Let’s look at some other token examples:

Word	Estimated Tokens
“Calculate”	1 token
“Calculation”	2 tokens (“Calcul”, “ation”)
“Adoption”	1 token
“Unbelievable”	3 tokens (“un”, “believ”, “able”)
“AI”	1 token
“U.S”	1 or 2 tokens (“U.S” or “U”, “.”, “S”)

So, does 1 token = 4 characters always?

Not always! The “1 token ≈ 4 characters” rule is just an average across a large dataset.

• Some tokens may be longer than 4 characters.
• Others may be shorter (like “a”, “I”, or punctuation).

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Maths behind the Cost of Tokens

Let’s walk through a real example using one of the more affordable options: Gemini 2.0 Flash-Lite.

Sample Calculation for Gemini 2.0 Flash-Lite

Imagine you’re summarising a text with 20,000 characters. Here’s how you’d calculate the cost:

Step 1: Convert Characters to Tokens

The industry standard is roughly 4 characters per token:

Tokens = Characters ÷ 4 = 20,000 ÷ 4 ≈ 5,000 tokens

Step 2: Calculate Input Cost

Gemini 2.0 Flash-Lite charges $0.075 per million tokens for input:

Input Cost = (5,000 ÷ 1,000,000) × $0.075 ≈ $0.000375

Step 3: Calculate Output Cost

Assuming the output length would be half the size of the input since we are trying to generate a summary, i.e 2500 tokens and Gemini 2.0 Flash-Lite charges $0.30 per million tokens for output:

Output Cost = (2500 ÷ 1,000,000) × $0.30 ≈ $0.00075

Step 4: Total Cost

Total Cost = Input Cost + Output Cost ≈ $0.000375 + $0.00075 ≈ $0.00112

So, processing this text costs approximately $0.001. Doesn’t seem like much, does it? But multiply that by thousands of requests per day, and suddenly you’re looking at significant expenses.

Cost Comparison: From Bargain to Premium

Wondering how the cost of top 10 LLMs stacks up? The differences might shock you.

For instance, if you processed the same text using GPT-4.5 instead of Gemini 2.0 Flash-Lite, your cost would jump from $0.001 to approximately $0.75—a 666x increase! Is the quality difference worth paying 666 times more? That depends entirely on your use case.

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Making Smart Choices Based on Cost

The cost of top 10 LLMs should factor into your decision, but it shouldn’t be the only consideration. Sometimes paying more for a premium model saves money in the long run through better outputs and fewer iterations.

On the other hand, if you’re running high-volume, relatively simple tasks, opting for a more affordable model like Gemini 2.0 Flash might be the financially prudent choice.

Here is an AI tool created with Gemini 2.0 Flash LLM which summarises a Github issue, try it and checkout its performance:
https://gitbrief.cleancodestack.com/

The Complete Cost Breakdown

Here’s the comprehensive comparison of the cost of top 10 LLMs based on our sample text of 20,000 characters(5,000 input tokens) and 2,500 output tokens:

LLM Name	Input Cost ($)	Output Cost ($)	Total Cost ($)
Gemini 2.0 Flash-Lite	0.000375	0.00075	0.00112
Gemini 2.0 Flash	0.0005	0.001	0.00150
DeepSeek R1	0.00275	0.005475	0.00823
DeepSeek-V3	0.00135	0.00275	0.00410
Grok-2	0.0100	0.0250	0.03500
Qwen 2.5-Max	0.0080	0.0160	0.02400
OpenAI o3-mini	0.0055	0.0110	0.01650
Claude 3.7 Sonnet	0.0150	0.0375	0.05250
GPT-4.5	0.3750	0.3750	0.75000

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Final Thoughts: Balancing Cost and Capability

The above cost of top 10 LLMs reveals an important truth about the AI landscape: price doesn’t always directly correlate with performance for every use case.

Understanding the cost of top 10 LLMs isn’t just about pinching pennies—it’s about making intelligent allocation decisions that maximize your AI investment. The most expensive model isn’t always the best choice, and the cheapest isn’t always the most cost-effective in the long run.

Are you ready to make smarter decisions about which LLM to use for your next project? Armed with this knowledge about the cost of top 10 LLMs, you’re now prepared to balance capability against cost for your specific use case.

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