OpenRouters 2025 Wrapped cards are one of the few year-end stat formats that can teach you something. Not because big number go up is interesting, but because the mix of numbers tells you what kind of builder someone is.
This year, my card reads like a real operating pattern for multi-model apps:
- 588.5M tokens routed, which the card equates to 6K novels, 101 years of non-stop speech, or 39 human work years
- 181 models used across the year, including 11 stealth models
- 291 active days with a 266-day streak and Tuesday as the favorite day
- 731 images generated
If youve ever built anything that calls LLMs more than a handful of times per day, you can feel the difference between I used one model a lot and I rotate models as a standard workflow. This is the second one.
What my top-model list says about the workload
My top 5 models by token volume were:
- Google Gemini 2.5 Pro Experimental: 66.6M tokens (Top 2%)
- OpenAI GPT-5 Mini: 65.9M tokens (Top 2%)
- Google Gemini 2.5 Flash Preview 04-17: 44.2M tokens (Top 7%)
- Google Gemini 2.0 Flash: 42.7M tokens (Top 3%)
- Google Gemini 2.5 Flash Lite: 42.5M tokens (Top 3%)
My top 5 models by tokens routed on OpenRouter in 2025.
This list is basically a two-layer setup:
- A heavy thinking model for difficult reasoning, coding, and long-context work.
- Multiple fast models for throughput: extraction, short reasoning, quick tool calls, and anything where speed and cost win.
Gemini 2.5 Pro Experimental sits in my deep work slot: complex coding, math/science problems, multimodal tasks, and long context. GPT-5 Mini being almost tied with it suggests I like a smaller model that still does step-by-step reasoning and tool calling well. Then the rest of the list is basically a speed stack of Flash variants.
If you are building agents, the fast stack isnt optional. Most agent steps are not deep genius moments. They are short operations: read some context, choose a tool, extract fields, write a small diff, update state, move on. I want those steps cheap and fast, and I save the expensive model for the few steps where I need the strongest reasoning I can get.
The number that matters is 181 models used
588.5M tokens is a lot. But 181 models is the bigger signal.
Using 181 models means Im constantly routing around tradeoffs. It implies three things that dont show up on the single best model debates:
- Uptime and rate limits are part of the design. Providers throttle, models have incidents, and behavior shifts. A real app needs fallback.
- Cost is an engineering constraint. I do not send bulk requests to my priciest model if I care about margins.
- Task fit is real. A model that is great at code review might be mediocre at extraction or vision-heavy workflows.
This is why OpenRouter exists: one OpenAI-compatible interface that lets you swap models across providers without rewriting half your stack. The unified API pitch sounds boring until you have to migrate endpoints, re-test auth, re-validate tool calling, and re-tune prompts because the new model has different failure modes.
And my own mix is me using OpenRouter the way its meant to be used: not as a novelty, but as a routing layer. This matches where the industry is going. AI is moving from casual chat into core infrastructure, and core infrastructure cares about routing, billing, observability, and failover. I wrote more on that shift here: Enterprise AI Adoption in 2025: From Casual Chat to Core Infrastructure.
How concentrated is my usage?
Its also worth noticing: even with 181 models used, the top 5 still account for a large slice of tokens.
The top 5 total 261.9M tokens. Out of 588.5M, thats about 44.5%. So yes, theres a clear default stack, plus a long tail of tests and niche routing choices.
Token share split: my top 5 models vs the remaining models.
The badges are marketing, but they point to real behavior
My card earned badges like First Steps, Early Adopter, Stealth Ninja, and Picasso.
- Stealth Ninja maps to 11 stealth models used. Thats me testing new releases early, even if they are not fully promoted yet.
- Picasso maps to 731 images generated. Thats consistent multimodal use, not a one-off demo.
My take: the gateway is becoming the default
If you are still thinking in pick one model and standardize on it forever, you are going to feel friction all year.
The better mental model is: pick a stack.
- One deep model for correctness when the task is difficult.
- One fast model for the bulk workload.
- A second fast model as fallback when pricing changes, traffic spikes, or a provider has a bad day.
- Measurement by route so experiments do not become permanent cost leaks.
And if you look at my Gemini-heavy speed layer, it pairs with what Ive said about why tool calling and fast models are the point of modern stacks. Related: Gemini 3 Flash Looks Imminent: Pricing, Nano Banana 2 Flash Leaks, and Why Tool Calling Is the Whole Point.
588.5M tokens is high usage. 181 models and 291 active days is the story. Its what serious iteration looks like when you treat models like interchangeable components, not a brand identity.
