Miami startup Subquadratic challenges AI's core math with a new model.A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.
The company claims its first model, SubQ 1M-Preview, is the first LLM built on a fully subquadratic architecture — one where compute grows linearly with context length. If that claim holds, it would be a genuine inflection point in how AI systems scale. At 12 million tokens, the company says, its architecture reduces attention compute by almost 1,000 times compared to other frontier models — a figure that, if validated independently, would dwarf the efficiency gains of any existing approach.
The company is also launching three products into private beta: an API exposing the full context window, a command-line coding agent called SubQ Code, and a search tool called SubQ Search. It has raised $29 million in seed funding from investors including Tinder co-founder Justin Mateen, former SoftBank Vision Fund partner Javier Villamizar, and early investors in Anthropic, OpenAI, Stripe, and Brex. The New Stack reported that the raise values the company at $500 million.
The numbers Subquadratic is publishing are extraordinary. The reaction from the AI research community has been, to put it mildly, mixed — ranging from genuine curiosity to open accusations of vaporware. Understanding why requires understanding what the company claims to have solved, and why so many prior attempts to solve the same problem have fallen short.
The quadratic scaling problem has shaped the economics of the entire AI industry
Every transformer-based AI model — which includes virtually every frontier system from OpenAI, Anthropic, Google, and others — relies on an operation called "attention." Every token is compared against every other token, so as inputs grow, the number of interactions — and the compute required to process them — scales quadratically. In plain terms: double the input size, and the cost doesn't double. It quadruples.
This relationship has shaped what gets built and what doesn't. The industry standard is 128,000 tokens for many AI models and up to 1 million tokens for frontier cloud models such as Claude Sonnet 4.7 and Gemini 3.1 Pro.
Even at those sizes, the cost of processing long inputs becomes punishing. The industry built an elaborate stack of workarounds to cope. RAG systems use a search engine to pull a small number of relevant results before sending them to the model, because sending the full corpus isn't feasible. Developers layer retrieval pipelines, chunking strategies, prompt engineering techniques, and multi-agent orchestration systems on top of models — all to route around the fundamental constraint that the model itself can't efficiently process everything at once.
Subquadratic's argument is that these workarounds are expensive, brittle, and ultimately limiting. As CTO Alexander Whedon told SiliconANGLE in an interview, "I used to manually curate prompts and retrieval systems and evals and conditional logic to chain together the workflows. And I think that that is kind of a waste of human intelligence and also limiting to the product quality."
Subquadratic's fix is deceptively simple: stop doing the math that doesn't matter
The company's approach, called Subquadratic Sparse Attention or SSA, is built on a straightforward premise: most of the token-to-token comparisons in standard attention are wasted compute. Instead of comparing every token to every other token, SSA learns to identify which comparisons actually matter and computes attention only over those positions. Crucially, the selection is content-dependent — the model decides where to look based on meaning, not on fixed positional patterns. This allows it to retrieve specific information from arbitrary positions across a very long context without paying the quadratic tax.
The practical payoff scales with context length — exactly the inverse of the problem it's trying to solve. According to the company's technical blog, SSA achieves a 7.2x prefill speedup over dense attention at 128,000 tokens, rising to 52.2x at 1 million tokens. As Whedon put it: "If you double the input size with quadratic scaling laws, you need four times the compute; with linear scaling laws, you need just twice." The company says it trained the model in three stages — pretraining, supervised fine-tuning, and a reinforcement learning stage specifically targeting long-context retrieval failures — teaching the model to aggressively use distant context rather than defaulting to nearby information, a subtle failure mode that quietly degrades performance in existing systems.
Three benchmarks paint a strong picture, but what they leave out may matter more
On the surface, SubQ's benchmark numbers are competitive with or superior to models built by organizations spending billions of dollars. On SWE-Bench Verified, it scored 81.8% compared to Opus 4.6's 80.8% and DeepSeek 4.0 Pro's 80.0%. On RULER at 128,000 tokens, a standard benchmark for reasoning over extended inputs, SubQ scored 95% — edging out Claude Opus 4.6 at 94.8%. On MRCR v2, a demanding test of multi-hop retrieval across long contexts, SubQ posted a third-party verified score of 65.9%, compared with Claude Opus 4.7 at 32.2%, GPT-5.5 at 74%, and Gemini 3.1 Pro at 26.3%.
But several details warrant scrutiny. The benchmark selection is narrow — exactly three tests, all emphasizing long-context retrieval and coding, the precise tasks SubQ is designed for. Broader evaluations across general reasoning, math, multilingual performance, and safety have not been published. The company says a comprehensive model card is "coming soon."
According to The New Stack, each benchmark model was run only once due to high inference cost, and the SWE-Bench margin is, as the company's own paper acknowledges, "harness as much as model." In benchmark methodology, single runs without confidence intervals leave room for variance. There is also a significant gap between SubQ's research results and its production model. On MRCR v2, the company reported a research score of 83 — but the third-party verified production model scored 65.9. That 17-point gap between the lab result and the shipping product is notable and largely unexplained.
Subquadratic also told SiliconANGLE that on the RULER 128K benchmark, SubQ scored 95% accuracy at a cost of $8, compared with 94% accuracy and about $2,600 for Claude Opus — a remarkable cost claim. But the company has not publicly disclosed specific API pricing, making it impossible to independently verify the cost-per-task comparisons.
The AI research community's verdict ranges from 'genuine breakthrough' to 'AI Theranos'
Within hours of the announcement, the AI research community erupted into a debate that crystallized around a single question: Is this real?
AI commentator Dan McAteer captured the binary mood in a widely shared post: "SubQ is either the biggest breakthrough since the Transformer... or it's AI Theranos." The comparison to the infamous blood-testing fraud company may be unfair, but it reflects the scale of the claims being made. Skeptics zeroed in on several pressure points. Prominent AI engineer Will Depue initially noted that SubQ is "almost surely a sparse attention finetune of Kimi or DeepSeek," referring to existing open-source models.
Whedon confirmed this on X, writing that the company is "using weights from open-source models as a starting point, as a function of our funding and maturity as a company." Depue later escalated his criticism, writing that the company's O(n) scaling claims and the speedup numbers "don't seem to line up" and called the communication "either incredibly poorly communicated or just not real."
Others raised structural questions. One developer noted that if SubQ truly reduces compute by 1,000x and costs less than 5% of Opus, the company should have no trouble serving it at scale — so why gate access through an early-access program? Developer Stepan Goncharov called the benchmarks "very interesting cherry-picked benchmarks," while another commenter described them as "suspiciously perfect."
But not everyone was dismissive. AI researcher John Rysana pushed back on the Theranos framing, writing that the work is "just subquadratic attention done well which is very meaningful for long context workloads," and that "odds of it being BS are extremely low." Linus Ekenstam, a tech commentator, said he was "extremely intrigued to see the real-world implications" particularly for complex AI-powered software.
Magic.dev made strikingly similar claims two years ago — and then went quiet
Perhaps the most pointed critique of SubQ's launch comes not from its specific claims but from recent history. Magic.dev announced a 100-million-token context-window model in August 2024, with a claimed 1,000x efficiency advantage, and raised roughly $500 million on the strength of those claims. As of early 2026, there is no public evidence of LTM-2-mini being used outside Magic.
The parallels are uncomfortable. Both companies claimed massive context windows. Both touted roughly 1,000x efficiency gains. Both targeted software engineering as their primary use case. And both launched with limited external access.
The broader research landscape reinforces the caution. Kimi Linear, DeepSeek Sparse Attention, Mamba, and RWKV all promised subquadratic scaling, and all faced the same problem: architectures that achieve linear complexity in theory often underperform quadratic attention on downstream benchmarks at frontier scale, or they end up hybrid — mixing subquadratic layers with standard attention and losing the pure scaling benefits.
A widely cited LessWrong analysis argued that these approaches "are all better thought of as 'incremental improvement number 93595 to the transformer architecture'" because practical implementations remain quadratic and "only improve attention by a constant factor."
Subquadratic is directly aware of this history. Its own technical blog specifically addresses each prior approach — fixed-pattern sparse attention, state space models, hybrid architectures, and DeepSeek Sparse Attention — and argues that SSA avoids their tradeoffs. Whether it actually does remains an empirical question that only independent evaluation can settle.
A five-time founder, a former Meta engineer, and $29 million to prove the doubters wrong
The team behind the claims matters in evaluating them. CEO Justin Dangel is a five-time founder and CEO with a track record across health tech, insurancetech, and consumer goods, and his companies have scaled to hundreds of employees, attracted institutional backing, and reached liquidity. CTO Alexander Whedon previously worked as a software engineer at Meta and served as Head of Generative AI at TribeAI, where he led over 40 enterprise AI implementations.
The team includes 11 PhD researchers with backgrounds from Meta, Google, Oxford, Cambridge, ByteDance, and Adobe. That is a credible collection of talent for an architecture-level research effort. But neither co-founder has published foundational AI research, and the company has not yet released a peer-reviewed paper. The technical report is listed as "coming soon."
The funding profile is unusual for a company making frontier AI claims. Subquadratic raised $29 million at a reported $500 million valuation — a steep price for a seed-stage company with no publicly available model, no peer-reviewed research, and no disclosed revenue. The investor base, led by Tinder co-founder Mateen and former SoftBank partner Villamizar, skews toward consumer tech and growth investing rather than deep technical AI research. The company is not open-sourcing its weights but plans to offer training tools for enterprises to do their own post-training, and has set a 50-million-token context window target for Q4.
The real test for SubQ isn't benchmarks — it's whether the math survives independent scrutiny
Strip away the marketing language and the social media drama, and the underlying question Subquadratic is asking is genuinely important: Can AI systems break free of quadratic scaling without sacrificing the quality that makes them useful?
The stakes are enormous. If attention can be made truly linear without degrading retrieval and reasoning, the economics of AI shift fundamentally. Enterprise applications that today require elaborate retrieval pipelines — processing entire codebases, contracts, regulatory filings, medical records — become single-pass operations. The billions of dollars currently spent on RAG infrastructure, context management, and agentic orchestration become partially redundant.
Whedon's willingness to engage publicly with technical criticism — posting a technical blog within hours of pushback — suggests a team that understands it needs to show its work, not just describe it. And to its credit, the company acknowledged openly that it builds on open-source foundations and that its model is smaller than those at the major labs.
Every frontier model in 2026 advertises a context window of at least a million tokens, but almost none of them are actually great at making use of all that information. The gap between a nominal context window and a functional one — between what a model accepts and what it reliably reasons over — remains one of the most important unsolved problems in AI. Subquadratic says it has closed that gap. If independent evaluation confirms that claim, the implications would ripple far beyond a single startup's valuation. If it doesn't, the company joins a growing list of long-context promises that sounded revolutionary on launch day and unremarkable six months later.
In computing, every fundamental constraint eventually falls. When it does, the breakthrough never comes from the direction the industry expected. The question hanging over Subquadratic is whether a team of 11 PhDs and a $29 million seed round actually found the answer that has eluded organizations spending thousands of times more — or whether they just found a better way to describe the problem.
Xiaomi's latest open models deliver powerful AI at an accessible priceXiaomi, the Chinese firm best known for its smartphones and electric vehicles, has lately been shipping some incredibly affordable and high-powered open source AI large language models.
The trend continued today with the release of Xiaomi MiMo-V2.5 and Xiaomi MiMo-V2.5-Pro, both available under the permissive, enterprise-friendly MIT License, making them suitable for use in production in commercial applications. Enterprises and individual/independent developers can now download either of the models (and more Xiaomi open source options) directly from Hugging Face, modify them as needed, and run them locally or on virtual private clouds as they see fit.
The most notable attribute of these models besides the open source licensing is that, according to Xiaomi's published benchmarks, they are among the most efficient available for agentic "claw" tasks, that is, powering systems such as OpenClaw, NanoClaw and Hermes Agent, in which users can communicate with them directly over third-party messaging apps and have the agents go off and complete tasks on the human user's behalf, such as making and publishing marketing content, running accounts, organizing email and scheduling, etc.
As Xiaomi's ClawEval benchmark chart shows, both MiMo-V2.5 and the Pro version in particular appear near the top left of the chart, indicating high performance in completing the benchmarked claw tasks while using the fewest amount of tokens — saving the human user money, especially in a world where more and more services such as Microsoft's GitHub Copilot are moving to usage-based billing (charging the human behind the agents for each token used rather than imposing rate limits like Anthropic or providing an "all-you-can-eat" buffet-style subscription like OpenAI).
In fact, the Pro model leads the open-source field with a 63.8% success rate, consuming only ~70K tokens per trajectory.
This is roughly 40–60% fewer tokens than those required by Anthropic Claude Opus 4.6, Google Gemini 3.1 Pro, and OpenAI GPT-5.4 to achieve comparable results.
By combining a massive 310B-parameter architecture with a highly efficient "active" footprint and a native 1-million-token context window, Xiaomi MiMo is challenging the dominance of closed-source frontier models from Google and OpenAI, especially when it comes to the latest and greatest craze in enterprise AI deployments — agentic tasks and "claws" similar to OpenClaw.
A two-pronged pincer
Xiaomi has released two distinct versions of the model to serve different ends of the development spectrum: MiMo-V2.5 (the "Omni" multimodal specialist) and MiMo-V2.5-Pro (the "Agent" specialist).
While the base model provides native multimodality, the MiMo-V2.5-Pro is specifically engineered for "long-horizon coherence" and complex software engineering.
On the GDPVal-AA (Elo) benchmark, the Pro model achieved a score of 1581, surpassing competitors like Kimi K2.6 and GLM 5.1.
Xiaomi researchers further released data on several high-complexity tasks performed autonomously by V2.5-Pro:
SysY Compiler in Rust: The model implemented a complete compiler from scratch—including lexer, parser, and RISC-V assembly backend—in 4.3 hours. Spanning 672 tool calls, the model achieved a perfect 233/233 score on hidden test suites, a task that typically takes a computer science major several weeks.
Full-Featured Video Editor: Over 11.5 hours and 1,868 tool calls, the model produced an 8,192-line desktop application featuring multi-track timelines and an export pipeline.
Analog EDA Optimization: In a graduate-level engineering task, the model optimized a Flipped-Voltage-Follower (FVF-LDO) regulator in the TSMC 180nm process. By iterating through an ngspice simulation loop, the model improved metrics like line regulation by 22x over its initial attempt.
These experiments highlight a "harness awareness" in V2.5-Pro, where the model actively manages its own memory and shapes its context to sustain coherence over thousands of sequential tool calls.
Over the API, Xiaomi is pricing the models at competitive rates for both domestic (Chinese) and international markets (like the U.S.). For overseas developers, the high-performance MiMo-V2.5-Pro is priced at $1.00 per million input tokens (for a cache miss) and $3.00 for output within context windows up to 256K.
For ultra-long context tasks between 256K and 1M tokens, the cost doubles to $2.00 for input and $6.00 for output, though the architecture’s caching capabilities offer significant relief, reducing input costs to as little as $0.20 to $0.40 per million tokens upon a cache hit.
Domestically, these rates are mirrored in yuan, with the Pro model starting at ¥7.00 per million input tokens for standard context and reaching ¥14.00 for the extended 1M range. Meanwhile, the base model starts at just $0.40 USD for overseas input per million tokens and $2.00 per million output, putting it among the more affordable third of leading LLMs globally (see our chart below):
Model
Input
Output
Total Cost
Source
Grok 4.1 Fast
$0.20
$0.50
$0.70
xAI
MiniMax M2.7
$0.30
$1.20
$1.50
MiniMax
MiMo-V2.5 Flash
$0.10
$0.30
$0.40
Xiaomi MiMo
Gemini 3 Flash
$0.50
$3.00
$3.50
Google
Kimi-K2.5
$0.60
$3.00
$3.60
Moonshot
MiMo-V2.5
$0.40
$2.00
$2.40
Xiaomi MiMo
MiMo-V2-Pro (≤256K)
$1.00
$3.00
$4.00
Xiaomi MiMo
GLM-5
$1.00
$3.20
$4.20
Z.ai
GLM-5-Turbo
$1.20
$4.00
$5.20
Z.ai
DeepSeek V4 Pro
$1.74
$3.48
$5.22
DeepSeek
GLM-5.1
$1.40
$4.40
$5.80
Z.ai
Claude Haiku 4.5
$1.00
$5.00
$6.00
Anthropic
Qwen3-Max
$1.20
$6.00
$7.20
Alibaba Cloud
Gemini 3 Pro
$2.00
$12.00
$14.00
Google
GPT-5.2
$1.75
$14.00
$15.75
OpenAI
GPT-5.4
$2.50
$15.00
$17.50
OpenAI
Claude Sonnet 4.5
$3.00
$15.00
$18.00
Anthropic
Claude Opus 4.7
$5.00
$25.00
$30.00
Anthropic
GPT-5.5
$5.00
$30.00
$35.00
OpenAI
GPT-5.4 Pro
$30.00
$180.00
$210.00
OpenAI
To lower the barrier for agentic development further, Xiaomi has made cache writing free of charge for a limited time across all models, alongside a total fee waiver for the entire MiMo-V2.5-TTS suite, which includes its specialized voice cloning and design features.
This pricing logic is clearly designed to accelerate the transition from simple chat applications to persistent, long-horizon agents that can operate at a fraction of the cost of legacy frontier models.
Xiaomi has also introduced an overhauled version of its subscription offerings, called the "Token Plan," now available in four levels:
The Lite "Starter Pack" provides 720 million credits for $63.36 USD per year
Standard tier offers 2.4 billion credits for $168.96 per year
A Pro tier provides 8.4 billion credits for $528.00 per year (designed for enterprise use cases)
Max —aimed at high-intensity coding enthusiasts—delivers 19.2 billion credits for $1,056.00 per year
Beyond credit allotments, all plans include preferential API rates, a 20% discount for off-peak calls, and "Day-0" support for popular coding scaffolds like Cursor, Zed, and Claude Code.
However, both through the API and via the Token Plan, accessing the Xiaomi models from China may present barriers or additional compliance and regulatory risks to U.S.-based enterprise customers. As such, the best bet for U.S. enterprises concerned about relying on Chinese tech but wanting to take advantage of the low cost and open source models is likely setting up their own virtual private clouds or local servers, downloading the model weights, and running the models domestically.
MoE architecture but divergent training regimens for V2.5 and V2.5-Pro
At the heart of MiMo-V2.5 is a Sparse Mixture-of-Experts (MoE) architecture. While the model boasts a total of 310 billion parameters, only 15 billion are "active" during any given inference cycle.
Meanwhile, V2.5-Pro is 1.02 trilion-parameter Mixture-of-Experts model with 42 billion active parameters.
In either case, the design functions much like a specialized research hospital: while the facility has hundreds of doctors (parameters), only the specific specialists required for a particular case (query) are called into the room.
This massive increase in parameter volume for the Pro version provides the "neural capacity" required for the deep, multi-step reasoning found in complex software engineering and long-horizon tasks, as though even more specialists are available in an even larger hospital.
According to Xiaomi's blog post, the regular V2.5 follows a rigorous five-stage evolution:
Text Pre-training: Building a massive language backbone on 48 trillion tokens.
Projector Warmup: Aligning in-house audio and visual encoders with the language core.
Multimodal Pre-training: Scaling across high-quality cross-modal data.
Agentic Post-training: Progressively extending the context window from 32K to 1M tokens.
RL and MOPD: Utilizing Reinforcement Learning and Multimodal Preference Optimization (MOPD) to sharpen real-world reasoning and perception.
The backbone utilizes a hybrid sliding-window attention architecture, inherited from MiMo-V2-Flash, which optimizes how the model "remembers" long-range information. This technical foundation enables MiMo-V2.5 to see, hear, and reason natively, rather than relying on external "plug-in" tools for visual or auditory processing.
Conversely, the training of MiMo-V2.5-Pro prioritizes "action space" over sensory perception. Instead of sensory alignment, the Pro model’s training focus shifts toward scaling post-training compute.
This process is designed to instill "harness awareness," where the model is specifically trained to manage its own memory and context within autonomous agent scaffolds like Claude Code or OpenCode.
While the base V2.5 model is trained to reason across modalities, the Pro version is trained to sustain coherence across more than a thousand sequential tool calls.
The standard V2.5 model balances local and global attention to maintain multimodal perception. The Pro model, however, utilizes an increased hybrid attention ratio—evolving from the 5:1 ratio of previous generations to a more aggressive 7:1 ratio.
This allows the Pro model to "skim" the vast majority of its context while applying high-density attention to the specific 15% of data most relevant to its current objective, a critical feature for debugging large repositories or optimizing graduate-level circuits.
Finally, while both models undergo Reinforcement Learning (RL) and Multimodal Preference Optimization (MOPD), the objectives of these stages differ.
For MiMo-V2.5, the RL stage is used to sharpen perception and multimodal reasoning. For MiMo-V2.5-Pro, RL is focused on instruction following within agentic scenarios, ensuring the model adheres to subtle requirements embedded deep within ultra-long contexts and recovers gracefully from errors during autonomous execution.
This results in the Pro model's "self-correcting" discipline, as seen in its ability to diagnose and fix regressions during the 4.3-hour SysY compiler build.
Full MIT License is perfect for enterprise use cases
In a move that distinguishes it from many "open" models that include restrictive "Acceptable Use" policies, Xiaomi has released MiMo-V2.5 under the MIT License.The MIT License is the gold standard of permissive software licensing. For developers and enterprises, this means:
No Authorization Required: Companies can deploy the model commercially without seeking explicit permission from Xiaomi.
Continued Training: Developers are free to fine-tune the model on proprietary data and even release those derivative weights.
Unrestricted Commercial Use: There are no revenue caps or user-base limits that often plague "community" licenses.
By choosing MIT over a custom "open weights" license, Xiaomi is positioning MiMo as the foundational infrastructure for the next generation of AI agents, effectively inviting the global developer community to treat the model as a public utility.
Xiaomi's background: from smartphones and EVs to Chinese open source AI darling
Xiaomi’s pivot toward frontier AI agents is the logical culmination of a decade spent building one of the world's most dense hardware-software flywheels.
Founded in 2010 as a smartphone disruptor, the Beijing-based company has executed a high-stakes transition into a vertically integrated powerhouse defined by its "Human x Car x Home" strategy. This ecosystem now encompasses over 823 million connectable smart devices unified under the HyperOS architecture.
The company’s 2024 entry into the automotive sector with the SU7 and the subsequent high-performance YU7 SUV served as a proof of concept for this integration, positioning Xiaomi as a direct competitor to global luxury marques.
By investing 200 billion yuan ($29B USD) into foundational R&D for chips and operating systems, Xiaomi has moved beyond consumer electronics assembly; it has become an architect of the "action space," using its massive hardware footprint as the primary testing ground for the agentic intelligence found in the MiMo-V2.5 series.
Ecosystem support
The release has been met with immediate "Day-0" support from the broader AI ecosystem. The MiMo team announced that SGLang and vLLM—two of the most popular high-throughput inference engines—supported the V2.5 series at launch.
This was made possible through hardware partnerships with AWS, AMD, T-HEAD, and Enflame, ensuring the model can run efficiently on everything from cloud-based H100s to domestic Chinese accelerators.
Fuli Luo, the project lead at Xiaomi MiMo and a former key member of the DeepSeek team, underscored the philosophy behind the release on X (formerly Twitter):
"A model's value isn't measured by rankings alone — it's measured by the problems it solves. Let's build with MiMo now!"
To kickstart this building phase, Luo announced a 100-trillion free token grant for builders and creators. This massive incentive is designed to lower the barrier to entry for developers who want to experiment with the 1M context window without immediate financial risk.
The economic realignment: open source vs. metered proprietary
The launch arrives at a critical juncture for AI economics. The shift toward usage-based billing marks the definitive end of the "all-you-can-eat" buffet era for AI services, a trend underscored by GitHub’s announcement today that its AI coding assistant Github Copilot will transition all plans to metered, token-based credits.
As seat-based predictability gives way to consumption-driven costs, premium agentic workflows—which can consume millions of tokens in a single reasoning session—are becoming increasingly difficult for enterprises to budget.
User sentiment has turned predictably cynical, with developers lamenting that they will "get less, but pay the same price" as subscriptions convert into finite allotments. This pricing evolution significantly enhances the strategic appeal of the MiMo series. By releasing under a permissive MIT License, Xiaomi allows organizations to bypass the escalating "SaaS tax" and reclaim financial predictability through private deployment.
Crucially, Xiaomi has eliminated the "context tax" for its API. The 1-million-token context window is now billed at the standard rate—1 token = 1 credit for V2.5 and 2 credits for the Pro version—with no additional multiplier. This stands in stark contrast to the industry-wide move toward session-based caps, positioning MiMo as a refuge for cost-sensitive, high-volume development.
Analysis for enterprises
The launch of MiMo-V2.5 is more than just a weight drop; it is a declaration of independence for the open-source community.
By matching Claude Sonnet 4.6 in multimodal agentic work and Gemini 3 Pro in video understanding, Xiaomi has proven that the gap between "closed-door" labs and open research is effectively closed.
With the MIT license as a catalyst and a 100T token grant as fuel, the coming months will likely see a surge in specialized, agentic applications built on the MiMo backbone.
Confirming the project's ambitious trajectory, the team noted they are already training the next generation, focusing on "deeper reasoning" and "richer real-world grounding". For now, MiMo-V2.5 stands as a testament to the power of sparse architectures and permissive licensing in the race toward functional AGI.
