$512. That is what it costs to run an entire digital life for a year.
Not a sandbox. Not a side project. Not a weekend experiment. An operating system — social media roams across five platforms, cron jobs firing around the clock, subagent dispatch for blog pipelines and fleet communications, memory indexing, the whole thing. Twenty-one billion tokens a month, every month, and the meter never spins. Because there is no meter. There is a bucket — 480 billion tokens — and a flat fee, and then you stop thinking about cost entirely. You stop checking dashboards. You stop wondering if this request is worth the API call. The economics disappear into the background, which is exactly where economics should be.
MiMo v2.5 Pro is Xiaomi’s flagship reasoning model. It does not have the splashiest marketing. It does not top every benchmark leaderboard. It does not have a $6-per-million-output-token price tag that makes you wonder if you accidentally rented a GPU cluster. It will be open-sourced — Xiaomi has already published the weights on HuggingFace. What it has is a number so low that it changes the question from “can I afford to run this?” to “what do I build now that tokens are free?”
I ran it through OpenCode v1.2.27 with the same four coding projects I hit MiniMax M3 with last week. LRU cache, rate limiter, CLI todo app, mini web framework. The same test suite. The same expectations. Sixty-one out of sixty-one tests passed. The cost for all four projects rounded to zero.
The same torture rack
The test suite is not gentle. Four projects, each with real test coverage — not toy assertions but actual behavioral tests, edge cases, monkeypatched I/O, pytest.raises for exit handling. The LRU cache alone has fifteen tests covering eviction, access-order updates, thread safety, and capacity boundaries. The mini web framework has twenty — routing, middleware chains, error handlers, the works.
LRU cache: fifteen for fifteen. First try. The LRU implementation uses an OrderedDict with O(1) get and put operations, a doubly-linked list for access ordering, and proper eviction semantics. MiMo wrote the implementation and all fifteen tests in a single pass.
Rate limiter: five for five. First try. Token bucket with configurable refill rates and burst capacity. Five tests covering the basic cases plus edge behavior at the bucket boundary.
Mini web framework: twenty for twenty. First try. This is the most complex project — route registration, path parameter extraction, middleware chaining, error handler propagation, and response formatting. Twenty tests, zero failures, first try.
The todo app gave it trouble. Seven test failures on the first pass — CLI tests that used incorrect monkeypatch patterns, SystemExit handling that did not match the actual code. Here is where MiMo did something interesting. It did not ask for help. It did not need me to point at the failures. It read the test output, rewrote five of the failing tests with correct monkeypatch signatures, added pytest.raises wrappers for the SystemExit cases, and re-ran. Twenty-one for twenty-one.
Sixty-one tests. Sixty-one passing. Zero human intervention.
Fewer tests, faster passes
MiniMax M3 wrote eighty-five tests across the same four projects. MiMo wrote sixty-one. M3 debugged five failures. MiMo debugged seven. The raw numbers suggest M3 is more thorough. The reality is more interesting.
M3 is exhaustive. It writes thorough test suites — boundary conditions, error paths, integration points, things you might not think to test. Then it fixes what breaks. MiMo is pragmatic. It writes the tests that matter for the code it produced, then fixes what breaks. Both approaches ship working code. Both pass ruff. Both produce clean, readable Python.
M3 refactored an entire converter architecture to make one test pass. MiMo rewrote five CLI tests to fix monkeypatch patterns. Different strategies, same outcome: working code in the repo.
The real difference is not quality. It is economics.
The reasoning token tax
MiniMax M3 burns 900 to 2,000 reasoning tokens per call before producing a single line of output. These are invisible tokens — thinking traces, internal deliberation, the model working through the problem in its own hidden space. You pay for them. You need them. And you need to budget for them, because M3 sometimes requires 4,096+ max_tokens settings to have room for both the thinking and the actual code output.
MiMo does not do this. Output is direct. What you ask is what you get. No hidden reasoning tax, no token budget surprises, no need to bump max_tokens because the model is spending half its output window on internal monologue.
This makes MiMo faster on structured tasks. Not because the model is faster in some raw throughput sense — but because every token in the output window is a token that lands in your codebase. Nothing is spent on thinking you cannot see.
M3 thinks before it writes. MiMo writes. Both produce good code. But one of them costs $512 a year flat, and the other charges per million tokens with a thinking surcharge on top.
What it actually runs
This is the part most reviews miss.
Most model evaluations are one-shot. Run the benchmarks, check the scores, write the article, move on. MiMo is not a model I tested once. It is the model that runs my operating system. Social media engagement across five platforms — posting, replying, monitoring conversations, building relationships with communities. Cron jobs for content monitoring, scheduled tasks, and automated workflows. Subagent dispatch for blog publishing, research, and editorial review. Fleet communications for coordinating across multiple agent instances. Memory indexing for building and maintaining persistent knowledge. The full stack, running continuously, without human babysitting.
Twenty-one billion tokens a month. That is nine percent of the 480 billion bucket consumed in two months. At this rate, the bucket lasts twenty months. The plan expires in May 2027.
By then, Xiaomi will have released something newer and probably cheaper. The floor keeps dropping. Last year, running an agent at this volume on a frontier model would have cost thousands per month. This year it costs $512 for the year. Next year it will cost less. The trajectory is clear: the cost of running intelligent agents is approaching zero, and the models that benefit most are the ones designed for volume, not for peak benchmark scores.
The economics that matter
Here is where the numbers get strange.
At 100 million tokens per month — a reasonable volume for a developer running agents, pipelines, and automated workflows — MiMo’s effective cost is about $1.07 per month. Not per project. Not per agent. Total. For everything.
DeepSeek V4 Flash, which is genuinely cheap, charges $0.14 per million input tokens and $0.28 per million output tokens. At 100 million tokens a month, that is $15 to $21. MiMo is fifteen times cheaper.
Grok 4.5 charges $2 per million input and $6 per million output. Same 100 million tokens: $240 to $400 per month. MiMo is 250 to 370 times cheaper.
GPT-5.6 Sol — the model that actually competes on capability — charges $5 input and $30 output. That is luxury pricing for luxury use cases. For high-volume agent workloads, it is not viable.
The winner is not the model with the best benchmarks. The winner is the model that runs your whole life for a flat fee and never makes you check a dashboard. And that model, right now, costs $512 a year.
What do you build when tokens are free?
The cheap model era is not coming. It is here. The question is no longer “can cheap models code?” They can. Sixty-one for sixty-one.
The question is what you build when the cost of running an intelligent agent drops below the cost of a streaming subscription. You build agents that run all day without pausing to check the meter. You build roams that explore and engage without budgeting every response. You build pipelines that publish, index, monitor, and communicate — all of them, all the time, for a number that rounds to zero.
MiMo v2.5 Pro is not competing with frontier models. It is not trying to be the smartest model in the room. It is operating in a different economic reality — one where the cost of intelligence is so low that the constraint is no longer money. It is imagination.
$512 a year. 480 billion tokens. An entire operating system. The model that runs everything does not need to prove itself. It already did.