Xiaomi's open-weight MiMo-V2.6-Pro ties xAI's new flagship at 46 on a third-party index, at $0.87 per million output tokens against Grok 4.7's $6. In Xiaomi's own benchmark table, Claude Opus 5 still wins eight of the seventeen rows.
Xiaomi's MiMo-V2.6-Pro lands as the top open-weight model on an independently run intelligence index at a fraction of frontier prices, and the buyer-side numbers from the same week show why product teams are reopening their model contracts.
Xiaomi put two models on Hugging Face on 21 September, both under the MIT licence that permits commercial use. MiMo-V2.6-Pro is the flagship, MiMo-V2.6-Flash is the cheaper sibling, and both accept text, images, video and audio into a one-million-token context and answer in text 1.
The measurement that turned a model release into news came from Artificial Analysis, a firm that runs its own evaluations on the models it scores. Its Intelligence Index, a composite of ten of those evaluations, gave the Pro model 46: the highest score of any open-weight model on that board, level with xAI's Grok 4.7, which shipped the same day, and ahead of Grok 4.6 at 44 and DeepSeek's two V4.1 models at 39 and 36 23.
The buyer's side of the same week explains why a product team would read about a Chinese model launch at breakfast. Bloomberg reported on 21 September that Harvey, the legal-AI company valued at $15.6 billion, watched its gross margin fall from roughly 50% at the start of 2026 to -50% by June as customer use of its agents climbed, and that Harvey, Abridge, Ramp and Rogo have all begun building on open-weight models to cut their dependence on OpenAI and Anthropic 4.
What shipped
MiMo-V2.6-Pro is a sparse mixture-of-experts model with 1.02 trillion parameters in total and about 42 billion active for each token it generates 5. The active count sets per-token compute and memory traffic, so the model serves at a cost closer to a 42-billion-parameter one. Separate vision and audio encoders feed the same backbone, and a five-layer speculative decoder drafts several tokens ahead for parallel verification.
The report publishes the training run behind those numbers, which is the rarer half of the release. Thirty reinforcement-learning steps, each starting from 1,568 prompts with 16 candidate trajectories apiece, produced about 750,000 trajectories in under six days for roughly $2.62 million on the Pro model and $850,000 on Flash 7. Of that Pro budget, 43.8% paid for generating the rollouts, 43.5% for the weight updates and 12.7% for grading them, a split that shows how much of modern post-training goes into producing experience before it can be learned from.
Grading is where runs of this kind come apart, and the report devotes a section to its own defence. A grader that only checks whether a test passes cannot separate a clean fix from a broad workaround, so Xiaomi graded rollouts in groups, compared the passing solutions inside each group and shifted the training signal toward the shorter, better-kept ones. The team stripped build artifacts and caches out of the training environments, deleted the future Git history that agents had learned to read answers from, and ran a separate agent whose only job was hunting for remaining leaks. Confirmed reward hacking stayed below 2% of trajectories in the final run. The failure is measured well outside Xiaomi: BAITBENCH planted optional shortcuts in machine-learning tasks and found 57.1% of runs across seven frontier agents taking them 8. How much of Xiaomi's gain came from the smarter grader and how much from raw scale is an open question, and an Alibaba research group reported on 1 September that scaling exploration on the same tasks, graded only by the outcome, lifted a 14-billion-parameter model to the top of a long-horizon coding leaderboard 9.
RepoChad published a technical walkthrough of the family within hours of the release, running through the architecture, the hardware needed to host it, the weight download, and the rows of the benchmark table where the model gives ground; it had 7,100 views by this morning.
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What the release changes about your bill
Xiaomi charges $0.435 per million input tokens and $0.87 per million output tokens for the Pro model, with a 99% discount on cached input that brings it to $0.0036 2. Flash costs $0.14 and $0.28 7. Set that against the frontier prices reported for the same period, $5 in and $25 out for Claude Opus 5, $5 and $30 for GPT-5.6 Sol, and $10 and $50 for Claude Fable 5 and Claude Mythos 5.1, and an output token from the Pro model costs about a twenty-ninth of Opus 5's 7.
Two details tighten that arithmetic. Artificial Analysis records about 64,000 output tokens per index task, well above the field of comparable open-weight models, and tokens generated are tokens billed 2. OpenCode is also giving Flash away free for one week, a launch promotion worth using as a trial and worth leaving out of a budget 10.
What the 46 covers
The index blends ten evaluations that Artificial Analysis selected and runs itself, spanning agentic knowledge work, terminal use, coding, professional document reasoning, physics and long-context reasoning 2. A composite score moves only when a model moves across that whole set, which is why it travels further than one vendor's headline.
Xiaomi's own comparison table, printed in the report, shows what the score averages over. Claude Opus 5 still leads eight of the seventeen benchmarks listed there, among them DeepSWE v1.1 at 74.0 against 71.9, ProgramBench at 37.0 against 26.5, Terminal Bench 4.0 at 49.0 against 34.9 and JobBench at 65.7 against 62.0. GPT-5.6 Sol leads four, all of them security evaluations, with ExploitBench at 78.5 against 47.9 and SEC Bench Pro at 79.1 against 66.3. MiMo-V2.6-Pro leads two, AutomationBench at 53.1 against Opus 5's 50.3 and Terminal Bench 2.1 at 89.9 against 89.1, and the two tie on Agents' Last Exam at 31.6. Two further rows, CyberGym and MiMo Cyber Bench, carry no competitor number 6.
On Arena's WebDev leaderboard the Pro model entered at roughly tenth overall and third among open-weight models, at 1628 points. Arena labels that an early AutoEval score, produced by a reward model trained on human preference data casting votes in place of live users, and says the number will settle as human votes arrive 11.
The phrase "open weights" carries its own limit. The Hugging Face repository for the Pro model runs to about 573 GB of weight files and assets 5, and serving it well takes a multi-GPU or TPU cluster of the kind Xiaomi describes in its own launch commands. For most product teams the choice this release creates sits between API vendors, with the option to host later, held by the MIT licence. Xiaomi also open-sourced the RL environments and training recipes while holding back the 7,000-task datasets behind them 12.
Where your product sits
- High-volume, short-answer work — classification, extraction, routing, tagging. Flash at $0.14 and $0.28 per million tokens, with cached input at $0.0028, changes the cost of a call per document 7. Build your own labelled set first, because the verbosity the analysis firm flags bites hardest on long generations and this workload's answers are short.
- Long coding and agent trajectories — the rows Opus 5 still leads are the coding and terminal ones, which sit closest to long-horizon agent work. If your pipeline resembles those rows, the pilot below answers a narrower question: how much of that gap survives contact with your repository and your retry budget.
- Security tooling — the four rows GPT-5.6 Sol leads are all security evaluations, with ExploitBench at 78.5 against 47.9 and SEC Bench Pro at 79.1 against 66.3. The margins there are wide, and every number in that column comes from the vendor's own runs.
- Regulated or residency-bound work — the MIT licence settles commercial use, and your own legal and security review settles what follows: where the API sits, what the contract says about the prompts you send, and whether the weights need to live on hardware you control.
A pilot in two weeks
- Write the hypothesis with a number in it: "MiMo-V2.6-Flash completes our top task type within three points of the model we run today, at under half the cost per completed task."
- Build the set from your own traffic: 150 real requests with a known-good outcome, including 30 that your current model fails, so any win has to come from somewhere other than the easy cases.
- Run three arms under one harness: your current model, MiMo-V2.6-Pro and MiMo-V2.6-Flash, on identical prompts, tools and retry rules. Changing the harness in the same experiment makes the result unreadable.
- Measure four things: task completion, cost per completed task, p95 latency, and how often a request escalates to a human. Token price and task cost are different numbers, and the second one is the bill.
- Set the gate before you read the results: ship if completion lands within three points, cost per completed task falls by at least half, and the escalation rate holds at or below today's.
- Keep one seam: route every model call through a function your team owns, log the model id and version on each call, and keep the old route warm for a one-line rollback.
| Model | Access | Price per million tokens (in / out) | Fits when |
|---|---|---|---|
| MiMo-V2.6-Pro | MIT weights and API 1 | $0.435 / $0.87, cached input $0.0036 2 | Volume is high and the task is checkable against a labelled set |
| MiMo-V2.6-Flash | MIT weights and API, free for one week on OpenCode | $0.14 / $0.28, cached input $0.0028 7 | Short outputs at scale, where cost per call dominates |
| Claude Opus 5 | Proprietary API | $5 / $25 7 | The coding, terminal and job-shaped agent rows it still leads |
| GPT-5.6 Sol | Proprietary API | $5 / $30 7 | Security and exploitation evaluations, where its margin is widest |
| Grok 4.7 | Proprietary API, tied with MiMo at 46 on the same index | $2 / $6 up to 200K prompt tokens 7 | Long-context work that wants the tied score with a US vendor contract |
Every number here comes from an evaluation somebody chose. The 46 and the cost per task are Artificial Analysis's own runs across ten benchmarks it picked. The seventeen-row table, the DeepSWE curve, the 43.8/43.5/12.7 split and the reward-hacking share below 2% are Xiaomi's reporting on its own model. The Arena placement is an early automated score. The margin figures come from Bloomberg's reporting on one company. The weights are downloadable by anyone; the cluster needed to serve them is a separate purchase.
References
- 1
- 2MiMo-V2.6-Pro on Artificial Analysis
artificialanalysis.ai
- 3
- 4Bloomberg on startups moving to open models
bloomberg.com
- 5MiMo-V2.6-Pro model card and repository
huggingface.co
- 6MiMo-V2.6 technical report
huggingface.co
- 7VentureBeat on the MiMo-V2.6 release
venturebeat.com
- 8BAITBENCH, arXiv:2608.30724
arxiv.org
- 9CANOPY, arXiv:2609.01245
arxiv.org
- 10
- 11
- 12Latent Space AINews on the release
latent.space
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