DeepSeek, Xiaomi & Microsoft Redefine Reasoning Models

In the past week, three major players (DeepSeek, Xiaomi, and Microsoft) have each released cutting-edge reasoning models that push the boundaries of what’s possible in math, logic, and code verification.

DeepSeek’s gargantuan Prover V2 (671 B parameters) brings formal proof checking to the masses under an MIT license. Xiaomi’s lean MIMO 7B demonstrates that clever training and extended context can outpace brute-force scaling.

And Microsoft’s Phi-4 Reasoning family (14 B parameters) delivers polished chain-of-thought reasoning with deployable efficiency. Below, we break down each release, explore the technical innovations, and consider what this arms race means for researchers, educators, and developers everywhere.

1. DeepSeek Prover V2: Formal Proofs at Scale

From R1 to Rover V2

Last season’s “R1 Sputnik” moment proved DeepSeek could compete with the best. Now, Prover V2 detonates onto Hugging Face at 671 billion parameters, with a smaller 7 B variant completing the family. Both sit under a permissive MIT license, inviting open-science collaboration.

FP8 Quantization & Download Size

Despite its size, V2 is quantized to FP8, cutting its raw weight footprint to around 650 GB. That’s still a hefty weekend-busting download, but far more practical than the terabyte-class alternatives.

Olympiad-Level Verification

What sets Prover V2 apart is its formal math proof capability. It ingests Olympiad-grade problems, translates them into Lean 4 code, and outputs machine-verifiable proofs, all in seconds rather than days of manual effort.

Lineage & Distillation

Prover V2 builds on the V1.5 and R1 foundations, both distilled from a 7 B synthetic-data base. V1.5 streamlined efficiency and accuracy; V2 scales that strategy up to a new performance tier, yet remains open for student-teacher distillation into smaller “Proverite” variants.

2. Xiaomi MIMO 7B: Small Model, Huge Ambitions

Training on 25 Trillion Tokens

Rather than piling on parameters, Xiaomi’s MIMO 7B focuses on data quality: a three-stage mix of 25 trillion tokens, with 70 percent devoted to math and coding.

32 K Context & Multi-Token Prediction

A 32,768-token window keeps long codebases and multi-step proofs in memory, while multi-token prediction accelerates reasoning tasks by predicting chunks of text at once.

Reinforcement-Learning Variants

Two RL-tuned versions—RL0 (from the raw base) and RL1 (after supervised fine-tuning)—train on 130,000 curated problems. A difficulty-driven reward scheme and easy-problem resampling ensure the model stretches beyond basic templates.

Benchmark Dominance

On AIME 2025, MIMO 7B RL scores 55.4, outpacing OpenAI’s o1-mini by 4.7 points. On LiveCodeBench V5, it achieves 57.8 percent, dwarfing Alibaba’s 32 B QWQ preview at 41.9 percent, yet runs on a single high-VRAM workstation.

3. Microsoft Phi-4 Reasoning: Polished Chain-of-Thought

Three Flavors, One Family

Microsoft’s Phi-4 Reasoning line includes the base 14 B model, a “Plus” variant with extra RL polish, and a pocket-sized Mini. All descend from the core Phi-4 architecture but are tuned for heavy math, science, and software reasoning.

Boundary-Prompt Curation & RoPE Tweaks

Instead of astronomical token counts, Microsoft curated 1.4 million edge-case prompts, paired with reference answers from OpenAI’s 03-mini. They also extended RoPE frequencies to support 32 K tokens—matching MIMO’s workspace.

Chain-of-Thought Tagging

Every output wraps explicit chain-of-thought traces separate from final answers, helping developers audit reasoning steps and ensuring transparency in decision paths.

Deployable Efficiency

Quantized to 4-bit precision, Phi-4 Reasoning can be served from a single consumer-grade GPU, ideal for classrooms, indie tools, or private research labs. Full training logs and eval traces are publicly available for community scrutiny.

4. What This Means for the AI Landscape

Democratization of Proofing & Reasoning
Open-licensed, high-performance models mean anyone with hardware can experiment with formal verification, automated grading, or research prototyping.

Precision vs. Scale Trade-Offs
The race isn’t simply bigger=better. Xiaomi shows that smarter data and architecture can rival models an order of magnitude larger, while DeepSeek demonstrates the unmatched utility of sheer parameter count in complex domains.

Security & Ethics
Lowering the barrier to powerful reasoning tools raises concerns—from misuse in cryptography to academic dishonesty. The community debate around “handing a Ferrari to anyone with a GPU” is only beginning.

Future Directions
Expect distilled student models, sub-10 B variants with competitive performance, and on-device deployments. Control tools to switch domain focus or “personality” in real time will blur the line between single-purpose AIs and versatile assistants.

DeepSeek, Xiaomi, and Microsoft have each staked out unique approaches: scale, efficiency, and deployable polish. As these models roll out under open licenses, the next wave of innovation, both exciting and challenging starts now.