Because we are not a normal tech company, and this is not a normal AI environmental calculator (see how it compares). Web Experts builds and advocates for open-source, low-power, and offline AI. It is far friendlier to the planet, and for most real work it is more than good enough. We are an environmentally conscious, ethical AI development team.
Free, and no email required. We built this so you can see what your AI habit actually costs, with no signup and no catch. If it helps, the best thing you can do is share it.
Choose what you know. TokenPrint handles the technical assumptions for you, and Expanded View is available whenever you want more control.
Hover, focus, or tap a question mark for a plain-language explanation.
Pick the kind of AI use and a rough amount. We will handle the model, hardware, and U.S. power-grid assumptions.
Use the token total from your provider when it is available.
Choose a starting point. TokenPrint will fill the calculator, and you can adjust the number.
These are input scenarios, not measurements of a specific app. Message mode uses the visible assumption of about 900 tokens per exchange.
Observed time to the next named generation:
Audience reference points: OpenAI reported more than 500 million users and 2.5 billion messages per day in 2025. Google reported 950 million monthly active Gemini app users in Q2 2026.
These intervals measure successor cadence, not retirement. Providers may keep older models available. Platform audience is also not the same as users of one specific model, so the calculator keeps audience size editable.
Final 2024 generation mix from the EIA. The carbon default uses the EPA eGRID 2023 national generation factor: 770.884 lb CO₂e/MWh, or about 350 g CO₂e/kWh. It excludes transmission losses. Regional grids vary by more than sixfold. Find your EPA grid region.
Almost all of it turns into heat, almost immediately.
Not from the data center itself, but from making the electricity it uses.
The "trees to absorb it" figure reflects that forests pull CO₂ back out slowly, over years.
It is not destroyed, but most of it leaves, which is why it counts as "consumed" rather than just used.
So a little returns as dirty water and the rest evaporates into the atmosphere. It rejoins the global water cycle, but it is taken out of the local supply, which is what matters in a drought. Closed-loop and air-cooled designs use far less water, but usually trade that for more electricity.
Developer APIs usually provide the clearest counts. Consumer apps may show message, plan, credit, or context limits instead. Image products also vary: some use image tokens, while others report credits, images, or GPU time.
| App | Shows tokens? | What it reports | Where to look |
|---|
You have a right to know what your AI use actually costs. If a platform will not show you your token usage or its energy and water footprint, ask them to. The more people request it, the sooner transparency becomes the norm. Tell your providers that showing this should be standard, not optional.
Every constant below is editable in spirit. We start with mid-range inference estimates, show the documented spread, and separately add the selected share of model training. Figures compiled from the cited sources on August 9, 2026; AI efficiency is improving quickly, so expect these to change over time.
The default is a conservative estimate, not a known model-specific audience. TokenPrint spreads training compute across 100 million active users over six months. That audience is intentionally below the whole-platform figures providers have published. OpenAI reported more than 500 million active users and 2.5 billion messages per day in July 2025. Google reported 950 million monthly active Gemini app users in Q2 2026. Neither figure tells us how many people used one particular model generation, so Expanded View lets visitors choose 5 million, 25 million, 100 million, 1 billion, or inference only.
Why the default uses six months: we compared the time between four named model generations using provider release dates.
Those intervals average 204.5 days, about 6.7 months, and have a median of 184 days, about six months. TokenPrint uses the six-month median as its default. These intervals measure the arrival of a named successor, not when an older model was retired.
How the share is calculated: the selected model's training-token estimate is converted to compute, then divided across the chosen audience, generation window, five messages per person per day, and about 900 tokens per message. The five-message assumption comes from OpenAI's published ratio of 2.5 billion daily messages to more than 500 million users. The 900-token message size is an editable calculator assumption, not a provider-reported average. The resulting training factor is applied to the visitor's usage and shown separately from inference.
| Constant | Low | Default | High |
|---|---|---|---|
| Text, per 1k tokens (by model) | 0.1 | 0.6 | 34 |
| Image, per image (Wh) | 0.5 | 2.9 | 11.5 |
| Data-center PUE | 1.1 | 1.15 | 1.3 |
| Constant | Low | Default | High |
|---|---|---|---|
| Cooling water (mL/Wh) | 0.9 | 1.1 | 1.8 |
| Total water (mL/Wh) | 33 | 60 | 167 |
| Grid (g CO₂e/kWh) | 40 | 350 | 650 |
| Grid, eGRID subregions (g CO₂e/kWh) | 110 | 350 | 680 |
| Embodied hardware (g CO₂e/kWh) | 16.4 | 18.9 | 43.6 |
| Equivalent | Factor | Equivalent | Factor |
|---|---|---|---|
| Phone charge | 11 Wh | Web search | 0.2 g CO₂ |
| LED bulb | 10 W | Car (petrol) | 170 g CO₂/km |
| Kettle boil | 110 Wh | Mature tree | 21 kg CO₂/yr |
| Average home | 30 kWh/day | NYC–London flight | ~1 t CO₂ |
| Fridge | 1.3 kWh/day | Beef | 60 kg CO₂/kg |
| Water bottle | 500 mL | 10-min shower | 62 L |
Inference and training are shown separately. The figures begin with per-use inference and add the amortized training share you select. Expanded View lets you adjust that assumption or exclude it.
Training-token scale is separate from your usage. It describes how much material a developer processed while building a model. TokenPrint uses this scale to estimate and separately display your amortized share of model development.
| Models in this calculator | Training tokens | Confidence |
|---|---|---|
| GPT-5.6 Luna, Terra and Sol | Not disclosed. Estimated at roughly 10T to 50T+ | Estimate |
| Claude Haiku, Sonnet 5 and Opus 5 | Not disclosed. Estimated at roughly 10T to 50T+ | Estimate |
| Gemini 3.6 Flash and Gemini 3.1 Pro | Not disclosed. Estimated at roughly 10T to 50T+ | Estimate |
| Grok 4 | Not disclosed. Estimated at roughly 10T to 50T+ | Estimate |
| DeepSeek V4-Flash | Not disclosed. Estimated at roughly 15T to 25T, anchored to V3's published 14.8T | Estimate |
| Llama 4 | More than 30T across text, image and video data | Published |
| Qwen3 | About 36T | Published |
| Kimi K2 | 15.5T | Published |
| Ollama Cloud and local models | Model-dependent, commonly about 2T to 36T+ | Varies |
How to read this: T means trillion training tokens. Closed-model estimates are broad Web Experts ranges based on the scale of documented contemporary open models, not vendor disclosures. Training data may be repeated across epochs, and multimodal developers count image, audio and video tokens differently. The amortization control uses the common approximation that training takes about three times as much compute per token as inference. Published sources: Meta Llama 4, Qwen3, DeepSeek-V3, Kimi K2, and Epoch AI's training and inference analysis. Disclosure references: OpenAI, Anthropic, Google DeepMind, and xAI.
Model choice dominates energy. Reasoning models and settings can use substantially more compute than a small model for the same visible answer. The difference varies by model, task, and reasoning effort.
Water is the most contested metric. The "cooling only" and "including power generation" numbers aren't contradictory, they draw the system boundary in different places. We show both.
The grid decides the carbon. The computation's energy does not change with location, but its emissions do. We use EPA's national eGRID factor as the U.S. default and keep clean and coal-heavy scenarios to show location sensitivity.
The model matters more than the chip. Switching to efficient silicon (Groq, TPU, Cerebras) is a real but modest ~1.5–3× win; the "10–35×" vendor claims compare against badly-run GPUs. The bigger lever is architecture, an efficient open MoE model can be 5–15× lighter per token.
Building the hardware counts too. Manufacturing carbon is added as a separate line at 18.9 g CO₂e per kWh, derived from a published life-cycle assessment of a real H100 partition. On a clean grid it is nearly half the total. Embodied water and minerals are still unmodeled.
Building the hardware has a carbon cost, and almost no AI calculator counts it. Tokenprint adds it as a separate line, at 18.9 g CO₂e per kWh of data-center electricity. That figure is not a guess. It is taken from a published bottom-up life-cycle assessment of an actual NVIDIA H100 partition, the Jean Zay supercomputer at CNRS/IDRIS: 194.2 tonnes CO₂e per year of annualized manufacturing emissions across compute, storage, power chain, cooling, refrigerant and diesel, divided by the 10,299 MWh per year that partition draws, PUE included.
The range reflects how long the hardware lasts and how hard it is worked. The low figure of 16.4 amortizes compute over 15 years. The high figure of 43.6 assumes a 4-year refresh cycle at 60% utilization, closer to commercial cloud practice than to a national research facility. The published study amortizes compute over 10 years at 88% utilization.
Why it matters more than it looks. In that study, manufacturing and operations came out at 46% and 54% of the annual footprint, nearly even, because the French grid is unusually clean at 21.7 g CO₂e/kWh. On a dirty grid, electricity dominates and embodied carbon is a rounding error. On a clean grid, or as grids decarbonize, the hardware itself becomes the larger half. That is the case for showing both numbers separately rather than folding them into one.
What this does not include: embodied water, mineral depletion, end-of-life, the data-center building itself, networking beyond the partition, or your own device. Those are real and unmodeled. This line covers manufacturing carbon only.
Comprehensive here means scope, not polish: how many of the real costs a calculator is willing to count, how honestly it handles what nobody knows, and whether it helps you find your actual usage instead of guessing. We checked every public AI footprint calculator we could find in August 2026. Here is where Tokenprint sits, including the places where other tools are ahead of us.
| What it counts | Tokenprint | EcoLogits | Masley | AI Impact Calc. | Omni | HF Energy Score |
|---|---|---|---|---|---|---|
| Energy | Yes | Yes | Yes | Yes | Yes | Measured |
| Carbon | Yes | Yes | Yes | Yes | No | No |
| Water | Two scopes | No | Yes | Yes | Yes | No |
| Uncertainty ranges on all three | Yes | Point estimates | Partial | Partial | No | n/a |
| Model-training share, amortized per user | Yes, editable | Excluded | Excluded | Mentioned | No | No |
| Embodied hardware carbon | Yes | Yes | Yes | Flat toggle | No | No |
| Regional grid resolution | 27 eGRID subregions | One world factor | Country presets | 5 presets | No | n/a |
| Shows you how to find your real usage | 10 apps | No | No | No | No | No |
| Second language | Spanish | No | No | No | No | No |
Only calculator that counts training. Building a model has a carbon cost, and your share of it is real. EcoLogits and Masley both exclude training outright as too uncertain to model. We include it, show the arithmetic, and let you set the audience and the generation lifespan yourself, or switch it off entirely.
Only calculator that teaches you your own number. Every other tool asks you to guess how many prompts you send. We list where the real count lives in ten different apps, from Claude Code and the OpenAI API to Midjourney and local Stable Diffusion, and we say plainly which ones hide it from you.
Only calculator that ranges everything. Energy, carbon, and water each carry a low, central, and high figure, because the honest answer to "what does a prompt cost" is a spread, not a number. A tool that gives you one confident digit is hiding the disagreement in the research.
Where other tools are ahead of us, and we are not going to pretend otherwise. Hugging Face's AI Energy Score does not estimate at all, it physically measures 166 models on standardized H100 hardware. That is a laboratory, and it is better evidence than any formula. EcoLogits reports impact categories we do not, including mineral depletion and primary energy, and models per-model parameter counts where we use eight model tiers. If you need a measured figure for one specific model, go to them. This tool is built for the person trying to understand their own total across everything they use.
So the claim we will actually defend: this is the most comprehensive consumer-facing AI footprint calculator we know of. It is the only one that reports energy, carbon, and water with uncertainty on all three, counts both the training and the manufacturing nobody else counts, resolves carbon to your own grid region, and then shows you how to find your real usage, in two languages, free, with no email. Comparison current as of 10 August 2026. If you know of one we missed, tell us and we will add it, including if it beats us.
First, we are coders.
We have been hand coding websites and custom software for a long time. We fought the templatization of our industry. We have always bucked trends that other web companies followed. Real designers, real code, no outsourcing, and no shortcuts. We are about ethics, relationships, and results. We believe you can build great things and still be environmentally conscious.
Efficient open models (DeepSeek V4, Llama 4, Qwen3, Kimi K2) are transparent, ownable, and cheap to run, in dollars and in energy.
A right-sized model on efficient hardware can do the same job for a fraction of the energy, water, and carbon of a frontier model.
Models that run on your own machine keep your data private, work without a connection, and draw only the power already in the room.
We help teams put the right-sized AI in the right place, the efficient way. If that is something you want to explore, we would be glad to talk it through.
Tokenprint