▲ 0 r/artificial+1 crossposts

The AI pricing market is completely unhinged

Wanted to know what different models actually cost across the whole market. Numbers turned out really interesting.

The spread.

Cheapest output on the platform is Mistral Nemo, $0.03 per million tokens. Most expensive is o1-pro at $600. I re-ran that twice because it looked like a units bug. Median paid model is about $2, so most of the catalog sits down near the floor and there's a thin little line of stuff way up at the top.

Provider averages, with a caveat.

  • OpenAI: $47.63
  • Anthropic: $44.79
  • Google: $5.58
  • Mistral: $3.68
  • Qwen: $2.86
  • Meta: $0.74

Caveat first because someone will say it anyway: these are averages over each provider's catalog, not weighted by what people actually run. OpenAI's number is dragged way up by o1-pro, which I doubt anyone is using at volume. Blended is 3:1 input to output, which is roughly what my own usage looks like.

Even so, Meta at $0.74 against OpenAI at $47.63 is a 64x gap. For the stuff I use models for (mostly code and summarizing), I don't get 64x anything.

Output tokens are where reasoning models get you.

Input and output are priced separately, and on the thinking models the ratio gets silly. Qwen3's thinking variants are $0.20/1M in and $2.40/1M out, so 12x. Gemini 2.5 Flash is 8.3x. Fine if you're sending one question. Less fine if you've got an agent looping thirty times and every step is paying the output rate. I got a bill like that once and it took me an embarrassingly long time to work out why.

19 free models, and a few are usable.

Not trial-credit free, actually free on the API:

  • NVIDIA Nemotron 3 Ultra, 1M context
  • Google Gemma 4, the 26B and 31B, multimodal, takes video, 262K context
  • Poolside Laguna S and XS, 262K
  • gpt-oss-20b, 131K (an OpenAI model, on the free list)

There are rate limits obviously. But for messing around or something low volume it's a lot better than it used to be.

Context went up 63x, price didn't really move.

Year Avg context Avg cost/1M
2023 10.5K $22
2024 140K $12
2025 357K $21
2026 662K $16

Price per token is roughly flat across three years. Context is up 63x. Whatever you think about everything else going on, that part is real.

Feels like two separate products now.

One side is $0.03 to $2 per million with big context windows, Mistral and Meta and Qwen and DeepSeek. The other is $30 to $600, OpenAI and Anthropic up top. They're not really pitching the same buyer anymore. Down at the bottom price stops being a thing you think about at all, and up top you're paying because the output quality moves some number in the business.

Data's from the OpenRouter API on Aug 16.

Link to full dashboard: https://app.vetros.dev/dash/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ0eXAiOiJzaGFyZSIsInBpZCI6IjEyMmZmNTk1IiwiZGFzaCI6ImRfODdmNDU3MzkiLCJ2ZXIiOjIsImlhdCI6MTc4NzA4NDc5MH0.V8uCPZtnzJ-djAXAv3HEmmZUHPkhO2NfhSgG2zGMYqw

reddit.com
u/move-size123 — 2 days ago

The AI pricing market is completely unhinged

Wanted to know what different models actually cost across the whole market, so I pointed my tool at OpenRouter's API and let it do the math. Numbers turned out really interesting.

The spread.

Cheapest output on the platform is Mistral Nemo, $0.03 per million tokens. Most expensive is o1-pro at $600. I re-ran that twice because it looked like a units bug. Median paid model is about $2, so most of the catalog sits down near the floor and there's a thin little line of stuff way up at the top.

Provider averages, with a caveat.

  • OpenAI: $47.63
  • Anthropic: $44.79
  • Google: $5.58
  • Mistral: $3.68
  • Qwen: $2.86
  • Meta: $0.74

Caveat first because someone will say it anyway: these are averages over each provider's catalog, not weighted by what people actually run. OpenAI's number is dragged way up by o1-pro, which I doubt anyone is using at volume. Blended is 3:1 input to output, which is roughly what my own usage looks like.

Even so, Meta at $0.74 against OpenAI at $47.63 is a 64x gap. For the stuff I use models for (mostly code and summarizing), I don't get 64x anything.

Output tokens are where reasoning models get you.

Input and output are priced separately, and on the thinking models the ratio gets silly. Qwen3's thinking variants are $0.20/1M in and $2.40/1M out, so 12x. Gemini 2.5 Flash is 8.3x. Fine if you're sending one question. Less fine if you've got an agent looping thirty times and every step is paying the output rate. I got a bill like that once and it took me an embarrassingly long time to work out why.

19 free models, and a few are usable.

Not trial-credit free, actually free on the API:

  • NVIDIA Nemotron 3 Ultra, 1M context
  • Google Gemma 4, the 26B and 31B, multimodal, takes video, 262K context
  • Poolside Laguna S and XS, 262K
  • gpt-oss-20b, 131K (an OpenAI model, on the free list)

There are rate limits obviously. But for messing around or something low volume it's a lot better than it used to be.

Context went up 63x, price didn't really move.

Year Avg context Avg cost/1M
2023 10.5K $22
2024 140K $12
2025 357K $21
2026 662K $16

Price per token is roughly flat across three years. Context is up 63x. Whatever you think about everything else going on, that part is real.

Feels like two separate products now.

One side is $0.03 to $2 per million with big context windows, Mistral and Meta and Qwen and DeepSeek. The other is $30 to $600, OpenAI and Anthropic up top. They're not really pitching the same buyer anymore. Down at the bottom price stops being a thing you think about at all, and up top you're paying because the output quality moves some number in the business.

Data's from the OpenRouter API on Aug 16.

reddit.com
u/move-size123 — 2 days ago
▲ 4 r/ETFs

Tracked 20 ETFs across every major asset class and sector

https://preview.redd.it/8x3401m426kh1.png?width=2028&format=png&auto=webp&s=2b92aca4200fd73c063b439aa5ab6c39ecaec10e

https://preview.redd.it/f9taf0c926kh1.png?width=1994&format=png&auto=webp&s=307e3bfcefbb66ee8d56373c0ad8ce808fd26888

https://preview.redd.it/kchr9zef26kh1.png?width=2184&format=png&auto=webp&s=e5048fd80d2ed9bf22f826fc0e86872818840e77

https://preview.redd.it/wyeyw9zh26kh1.png?width=2190&format=png&auto=webp&s=c31c29bd6e91fbdf1027399727ce5adca05adbea

Full dashboard here

TL;DR

Tracked 20 ETFs across every major asset class and sector - broad market, tech, bonds, commodities, international, sectors - Feb to Aug 2026, 126 trading days.

XLK +36%. Best return and best risk-adjusted score in the whole group. Not close.

SPY +13.7% vs VTI +13.9%. Sharpe and win rate identical to two decimal places. That debate is dead.

ARKK +15.4% sounds ok until you see the vol (2.4% daily) and Sharpe (0.90). Coin flip win rate at 50.8%. SPY beat it on every risk-adjusted metric while moving half as much.

GLD -10.4%, SLV -11.9% for the full period. SLV hit 31% below its peak at one point. Then both ripped in August - GLD +7.9%, SLV +11.3%. Make of that what you will.

Every single equity ETF beat every single bond ETF. BND -3.4%, AGG -3.5%, LQD -5.0%. AGG's average down day is bigger than its average up day. Not what you hold bonds for.

IWM and XLI are sitting at period highs as of mid-August. Small caps and industrials, quietly.

XLV only +6.4% but the best gain/loss ratio of anything tracked (1.37). Loses less on bad days than almost everything here. Does what a defensive holding is supposed to do - unlike bonds.

reddit.com
u/move-size123 — 2 days ago
▲ 1 r/ETFs

Performance of Top 20 ETF's

https://preview.redd.it/ipu8v3bfb4kh1.png?width=2136&format=png&auto=webp&s=4a78b012ac3d7f7023379d597043d15a1493e45f

https://preview.redd.it/r610x7thb4kh1.png?width=2174&format=png&auto=webp&s=5a1a15d8faa913015dbcbb46be2be54905dba787

https://preview.redd.it/jzi98jskb4kh1.png?width=2188&format=png&auto=webp&s=8279366e25b4d0ca082d3562ba03620bf4947dcc

https://preview.redd.it/jg079rjnb4kh1.png?width=2190&format=png&auto=webp&s=220b1a8b71bfa515573e7bc4abca1eada3b709f6

Hi, I have compiled some stats of Top 20 ETFs. Data is from 2026 February. If you want a link to dashboard, tell me, I will send.

TL;DR

Tracked 20 ETFs, Feb-Aug 2026, 126 trading days.

XLK +36%. Best return AND best risk-adjusted score in the basket. Tech won on both counts.

SPY +13.7% / Sharpe 2.09. VTI +13.9% / Sharpe 2.10. Win rate identical at 54%. Pick one and stop thinking about it.

ARKK +15.4% but daily vol of 2.4% and a Sharpe of 0.90. You were basically coin-flipping (50.8% win rate) with double the turbulence of SPY for a marginally better return.

GLD -10.4%, SLV -11.9%. SLV is 31% off its period high. Metals got wrecked — then both ripped in August (GLD +7.9%, SLV +11.3%). Watching closely.

Bonds: BND -3.4%, AGG -3.5%, LQD -5%. Every equity ETF beat every bond ETF. AGG's average down day (-0.26%) is bigger than its average up day (+0.20%). Not great for something you hold as a hedge.

IWM and XLI are sitting at period highs right now. Small caps and industrials, quietly.

XLV only +6.4% but the best gain/loss ratio in the tracker (1.37) — gives back less on bad days than almost anything else here. Underrated.

reddit.com
u/move-size123 — 2 days ago

Built a ETL pipeline and dash in 10mins

I connected GCP + dbt + apache echarts + anthropic + other open source tools and made a tool to easily produce dashboards. 6 months ago, I didn't think this would work, but the situation has changed :D

youtube.com
u/move-size123 — 8 days ago

I built a product that's like Lovable, but for data apps

Hi all,

I started a personal project just out of curiosity. The goal was to build an app that builds a working pipeline and a dashboard from a single prompt. It:

  1. Ingests
  2. Transforms
  3. Models
  4. Creates dashboards or other data products
  5. Refreshes the whole thing on picked schedule

The idea arose because I've noticed that agents are making LEAPS of progress at data engineering and analytics in a short timeframe.

There are many versions of this already released (some services build dashboards on your established data warehouse, some services are building pipelines, some are doing transformations, some excel at visualizations). E.g. Bruin, Basedash, Dot, Omni analytics

But I haven't noticed a single platform where you could start from scratch and have a dashboard from a randomly picked API or DB or Google Sheets in a matter of 10 mins - a full package. So I scrambled the top available open-source tools for ingestion, transformation and visualization, and I glued them together.

I developed this project non-stop for some time and got a pretty nice-looking and functioning beta version (in my eyes). And I wanted some users to test it. The platform is totally free - I'm not trying to monetize yet (I don't even have payments set up). I'm just looking for some honest opinions and feedback so I can iterate :)

There is a free tier with $5 worth of AI credits - you are welcome to use it, and please, give some feedback (hello@vetros.dev or here)! For testing, you can use public APIs, DBs, or even connect your Google Sheets or other private data sources. Or play with the demo dashboards/datasets.

I have attached a video of me building a dashboard from the Alpha Vantage API (an API for stock market and financial data) in 10 mins.

here are some interactive examples of dashboards my app generated from a few prompts:

  1. Google Analytics
  2. Exchange Traded Funds
  3. State of AI pricing
  4. Credit Risk Dashboard
reddit.com
u/move-size123 — 8 days ago