We have solved this problem by working with the providers to implement a prices and models API that we scrape, which is how we keep our marketplace up to date. It's been a journey; a year ago it was all happening through conversations in shared Slack channels!
The pricing landscape has become more complex as providers have introduced e.g. different prices for tokens depending on prompt length, caching, etc.
I do believe the right lens on this is actually the price per token by endpoint, not by model; there are fast/slow versions, thinking/non-thinking, etc. that can sometimes also vary by price.
The point of this comment is not to self promote, but we have put a huge amount of work into figuring all of this out, and have it all publicly available on OpenRouter (admittedly not in such a compact, pricing-focused format though!)
This is great, but as others have mentioned the UX problem is more complicated than this:
- for other models there are providers that serve the same model with different prices
- each provider optimizes for different parameters: speed, cost, etc.
- the same model can still be different quantizations
- some providers offer batch pricing (e.g., Grok API does not)
And there are plenty of other parameters to filter over- thinking vs. non-thinking, multi-modal or not, etc. not to even mention benchmarks ranking.
https://artificialanalysis.ai gives a blended cost number which helps with sorting a bit, but a blended cost model for input/output costs are going to change depending on what you're doing.
I'm still holding my breath for a site that has a really nice comparison UI.
There was a time when it was unbelievably frustrating to navigate the bunch of marketing pages required to find the cost of a newly announced model, now I just look at OpenRouter to find pricing.
> The only place I am aware of is going to these provider's individual website pages to check the price per token.
Openrouter is a good alternative. Added bonus that you can also see where the open models come in, and can make an educated guess on the true cost / size of a model, and how likely it is it's currently subsidised.
Can you gather historical information as well? I did a bit of spelunking of the Wayback Machine to gather a partial dataset for OpenAI, but mine is incomplete. Future planning is well-informed by understanding the trends — my rough calculation was that within a model family, prices drop by about 40-80% per 12 months.
I've run into this a ton of times and these websites all kinda suck. Someone mentioned the OpenRouter /models endpoint in a sibling comment here, so I quickly threw this together just now. Please feel free to PR!
llm-pricing opus-4 -v
=== ANTHROPIC ===
Model: anthropic/claude-opus-4
Name: Anthropic: Claude Opus 4
Description: Claude Opus 4 is benchmarked as the world's best coding model, at time of release,
bringing sustained performance on complex, long-running tasks and agent workflows. It sets new
benchmarks in software engineering, achieving leading results on SWE-bench (72.5%) and
Terminal-bench (43.2%).
Pricing:
Input: $15.00 per 1M tokens
Output: $75.00 per 1M tokens
Cache Read: $1.50 per 1M tokens
Cache Write: $18.75 per 1M tokens
Per Request: $0
Image: $0.024
Context Length: 200000 tokens
Modality: text+image->text
Tokenizer: Claude
Max Completion Tokens: 32000
Moderated: true
KV caching is priced and managed quite differently between providers as well. Seeing as it becomes a huge chunk of the actual tokens used, wondering if there's an easy way to compare across providers.
Maybe I am blinded by my own use case, but I find the caching pricing and strategy (since different providers use a different implementation of caching as well as different pricing) to be a major factor rather than just the "raw" per token cost, and that is missing here, as well as on the Simon Willison site [1]. Do most people just not care / not use caching that much that it matters?
Nice! It will be good to also pull in leaderboard rankings and/or benchmarks for each of these models, so we understand capability perhaps from lmsys (not sure if there is a better source)
We are working on a similar problem, https://apiraces.com,
to personalize the cost calculation of your llm api use case,
We have uploaded mostly the openrouter api models, but trying to do it in a useful way to personalize calculation and comparison. If someone would like to test or have a demo, we will be glad for any feedback.
Does anyone have an API that maintains a list of all model versions for a provider? I hand-update OpenAI into a JSON file that I use for cost reporting in my apps (and in an npm package called llm-primitives).
What tools / experiments out there exist to exercise these cheaper models to output more tokens / use more CoT tokens to achieve the quality of more expensive models?
eg, Gemini 2.5 flash / pro ratio is 1 1/3 for input, 1/8 for output... Surely there's a way to ask Flash to critique it's work more thoroughly to get to Pro level performance and still save money?
I'd love to see this data joined with common benchmarks, in order to see which models get you the most "bang for your buck", i.e. benchmark score / token cost
what's point of comparing token prices? especially for thinking models.
Just now I was testing the new Qwen3-thinking model. I've run the same prompt five times. The costs I got, sorted: 0.0143, 0.0288, 0.0321, 0.0389, 0.048 .
And this is for single model.
Also, in my experience, sonnet-4 is cheaper than gemini-2.5-pro, despite token costs being higher.
Love it! It's going on my toolbar. I face the same problem, constantly trying to hunt down the latest pricing which is often changing. I think it's great that you want to add more models and features, but maybe keep the landing page simple with a default filter that just shows the current content.
this is great, I've always wanted something like this, do you think you can add other model metadata, like api name (`gemini-2.5-pro`), context length, modalities, etc
Are we really at a point already where we're treating tokens as a commodity? I certainly would not consider a token generated by Claude or Gemini to be of similar value to a token by Copilot, for example.
can we not just self host, expose things through VPN, and something that needs sharing with the world, then tunnel through some cloud server to keep the internal servers secure?
I am newly to this hobby, but would like to know more about what experienced person things and do.
tldr; low effort website that only contains 26 Google, OpenAI and Anthropic models and only input and output prices but no info about prompt cache and prompt cache prices. For a list of 473 models of 60+ providers with input, output, context, prompt caching and usage: https://openrouter.ai/models
(no affiliation)
Show HN: Price Per Token – LLM API Pricing Data
(pricepertoken.com)310 points by alexellman 25 July 2025 | 123 comments
Comments
We have solved this problem by working with the providers to implement a prices and models API that we scrape, which is how we keep our marketplace up to date. It's been a journey; a year ago it was all happening through conversations in shared Slack channels!
The pricing landscape has become more complex as providers have introduced e.g. different prices for tokens depending on prompt length, caching, etc.
I do believe the right lens on this is actually the price per token by endpoint, not by model; there are fast/slow versions, thinking/non-thinking, etc. that can sometimes also vary by price.
The point of this comment is not to self promote, but we have put a huge amount of work into figuring all of this out, and have it all publicly available on OpenRouter (admittedly not in such a compact, pricing-focused format though!)
[1] https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-fla...
- for other models there are providers that serve the same model with different prices
- each provider optimizes for different parameters: speed, cost, etc.
- the same model can still be different quantizations
- some providers offer batch pricing (e.g., Grok API does not)
And there are plenty of other parameters to filter over- thinking vs. non-thinking, multi-modal or not, etc. not to even mention benchmarks ranking.
https://artificialanalysis.ai gives a blended cost number which helps with sorting a bit, but a blended cost model for input/output costs are going to change depending on what you're doing.
I'm still holding my breath for a site that has a really nice comparison UI.
Someone please build it!
- An image will take 10x token on gpt-4o-mini vs gpt-4.
- On gemini 2.5 pro output token are token except if you are using structure output, then all character are count as a token each for billing.
- ...
Having the price per token is nice, but what is really needed is to know how much a given query / answer will cost you, as not all token are equals.
Are there any tutorials you can recommend for somebody interested in getting something running locally?
- Off-peak pricing by DeepSeek
- Batch pricing by OpenAI and Anthropic
- Context window differentiated pricing by Google and Grok
- Thinking vs non-thinking token pricing by Qwen
- Input token tiered pricing by Qwen coder
I originally posted here: https://x.com/paradite_/status/1947932450212221427
Openrouter is a good alternative. Added bonus that you can also see where the open models come in, and can make an educated guess on the true cost / size of a model, and how likely it is it's currently subsidised.
https://github.com/tekacs/llm-pricing
--- ---[1] https://llm-prices.com/
The UI, however, is really clean and straight to the point. I like the interface, but miss the content
We have uploaded mostly the openrouter api models, but trying to do it in a useful way to personalize calculation and comparison. If someone would like to test or have a demo, we will be glad for any feedback.
Here's the current version:
I would love to replace this with an API call.What tools / experiments out there exist to exercise these cheaper models to output more tokens / use more CoT tokens to achieve the quality of more expensive models?
eg, Gemini 2.5 flash / pro ratio is 1 1/3 for input, 1/8 for output... Surely there's a way to ask Flash to critique it's work more thoroughly to get to Pro level performance and still save money?
Just now I was testing the new Qwen3-thinking model. I've run the same prompt five times. The costs I got, sorted: 0.0143, 0.0288, 0.0321, 0.0389, 0.048 . And this is for single model.
Also, in my experience, sonnet-4 is cheaper than gemini-2.5-pro, despite token costs being higher.
- Filter by model "power" or price class. I want compare the mini models, the medium models, etc.
- I'd like to see a "blended" cost which does 80% input + 20% output, so I can quickly compare the overall cost.
Great work on this!
Should I use copilot pro in agent mode with sonnet 4, or is it cheaper to use claude with sonnet 4 directly?
You should also aggregate prices from Vertex and AWS Bedrock .
I am newly to this hobby, but would like to know more about what experienced person things and do.