MAI-Base-2 vs DeepSeek V4 Pro Comparison

MAI-Base-2 beats DeepSeek-V4-Pro-Base on Microsoft's own held-out tests. MAI-Base-2 cannot be used yet, so DeepSeek V4 Pro is the only one of the two you can build on today. Microsoft AI published its held-out evaluation results on October 15, 2026, saying MAI-Base-2 outperformed every open-weight model it tested, including DeepSeek-V4-Pro-Base from DeepSeek. MAI-Base-2 is currently undergoing post-training.

MAI-Base-2 features 2.8 trillion total parameters with 80 billion active per token across a Mixture-of-Experts (MoE) design, trained on 50 trillion tokens of text and vision data. DeepSeek V4 Pro operates with 1.6 trillion total parameters and 49 billion active parameters per token, trained on more than 32 trillion tokens with open weights under the MIT license.

While Microsoft claims that MAI-Base-2 achieved 4 to 12 times greater pre-training floating-point operations (FLOPs) efficiency than DeepSeek-V4-Pro-Base, Microsoft published no individual benchmark numbers. Engineering teams evaluating deployments today must weigh Microsoft's self-reported scaling-law curve fits against DeepSeek's self-published benchmark scores and the fact that DeepSeek V4 Pro is available now.

MAI-Base-2 vs. DeepSeek V4 Pro: Side-by-Side

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Short Answer on Benchmark Claims and Live Usability

MAI-Base-2 outperforms DeepSeek-V4-Pro-Base on Microsoft's internal evaluations, but MAI-Base-2 cannot be deployed today because it remains an unreleased base model undergoing post-training. For active software development, DeepSeek V4 Pro is the only accessible model among the two, offering production application programming interface (API) access and downloadable open weights under the MIT license.

Microsoft published relative scaling-law gains indicating superior compute efficiency during training, but provided no downloadable files, no API endpoints, and no benchmark scores. DeepSeek V4 Pro has a published 1 million token context window, self-published base-model scores and a live API.

Microsoft has not published a release date for MAI-Base-2. Teams with immediate deployment deadlines must select DeepSeek V4 Pro, while enterprise organizations committed to the Microsoft ecosystem can monitor Foundry for future licensing announcements.

• Select DeepSeek V4 Pro for immediate API deployment, local weight hosting, or MIT-licensed commercial applications
• Select DeepSeek V4 Pro for established 1 million token context processing and half-price off-peak API rates
• Track MAI-Base-2 if your enterprise requires native vision pre-training and future Foundry licensing
• Recognize that Microsoft has published no raw score tables or public access dates for MAI-Base-2 as of October 2026

Scope of the Microsoft Base Model Benchmark Claims

Microsoft's technical comparison evaluated MAI-Base-2 directly against the base checkpoint DeepSeek-V4-Pro-Base rather than the post-trained DeepSeek V4 Pro. A base model represents the pre-trained state of a neural network before instruction tuning, reasoning, or safety training are applied. A base model is not a chat assistant. Products are built on post-trained versions.

Microsoft used three kinds of evaluation: held-out text scored by bits-per-byte, held-out code from Microsoft AI's own codebase, and vendor-commissioned STEM, math and Q&A evaluations. Microsoft decontaminated these benchmark suites against its 50 trillion token training corpus to prevent test data memorization.

Microsoft reported that MAI-Base-2 outperformed every open-weight model tested on these held-out suites. Microsoft compared MAI-Base-2 with DeepSeek-V4-Pro-Base base to base. It compared Kimi K3, Qwen3.8-2.4T-A95B and MiMo-V2.6-Pro-RL only as post-trained checkpoints. Microsoft included an explicit caveat noting that post-training procedures applied to those external models could influence evaluation performance.

• The primary head-to-head evaluation compared MAI-Base-2 to DeepSeek-V4-Pro-Base
• Evaluations incorporated internal Microsoft codebases alongside decontaminated STEM, math, and Q&A assessments
• Comparisons against Kimi K3, Qwen3.8-2.4T-A95B, and MiMo-V2.6-Pro-RL evaluated post-trained checkpoints rather than pure base models
• Microsoft published no specific scores, test logs, or task-level accuracy figures for MAI-Base-2

Architecture and Parameter Specifications

MAI-Base-2 features a substantially larger total parameter footprint than DeepSeek V4 Pro while preserving a high degree of routing sparsity per token. MAI-Base-2 contains 2.8 trillion total parameters with 80 billion active per token across an MoE architecture that uses dropless routing in every layer. In contrast, DeepSeek V4 Pro contains 1.6 trillion total parameters with 49 billion active per token. MAI-Base-2 represents a structural departure from Microsoft's earlier MAI-Base-1 model, which alternated between dense layers and MoE layers.

Microsoft trained MAI-Base-2 on a cluster of 16,000 NVIDIA GB200 graphics processing units (GPUs) over 38 days during August and September 2026. Microsoft recorded an average model FLOPs utilization (MFU) of 21% and an overall goodput of 89%, with MoE-layer MFU rising from 24% to 35%. Training used the Muon optimizer, which Microsoft says gave more stable training dynamics than AdamW.

Vision capabilities represent another major distinction between the two architectures. MAI-Base-2 was pre-trained natively with vision, using an encoder-free vision-language model (VLM) design. Vision-language data made up 18% of its 50 trillion token training mixture, drawn from 4.5 trillion unique vision-language tokens. DeepSeek V4 Pro, detailed in the DeepSeek V4 explained guide, is text only on the DeepSeek API, where vision is not supported. DeepSeek trained its model on more than 32 trillion tokens.

• MAI-Base-2 operates 2.8 trillion total parameters and 80 billion active parameters per token
• DeepSeek V4 Pro operates 1.6 trillion total parameters and 49 billion active parameters per token
• MAI-Base-2 integrates native vision processing across 18% of its 50 trillion token training mixture
• Microsoft trained MAI-Base-2 on 16,000 NVIDIA GB200 GPUs across 38 days using the Muon optimizer

Pre-Training FLOPs Efficiency and Operational Realities

Microsoft's claim that MAI-Base-2 achieves 4 to 12 times greater FLOPs efficiency refers specifically to the compute required during pre-training rather than live operational costs. Microsoft's technical post cites an efficiency improvement of 4.2x to 12.3x over DeepSeek-V4-Pro-Base, estimated from Microsoft's internal scaling-law curve fits. Microsoft also reported a 6.3x to 12.7x FLOPs-efficiency improvement over MAI-Base-1.

Pre-training FLOPs efficiency indicates that MAI-Base-2 required fewer total floating-point arithmetic operations to achieve a given level of loss or benchmark competency during the pre-training run.

However, pre-training compute efficiency does not translate directly into inference efficiency or cheaper API pricing.

Published Benchmark Scores and Evaluation Transparency

DeepSeek publishes its own benchmark scores for DeepSeek-V4-Pro-Base, whereas Microsoft disclosed zero discrete benchmark numbers for MAI-Base-2. The DeepSeek-V4-Pro model card on Hugging Face lists nine scores for DeepSeek-V4-Pro-Base, covered in the DeepSeek V4 benchmarks guide.

DeepSeek-V4-Pro-Base achieved 90.1 on Massive Multitask Language Understanding (MMLU) under 5-shot testing, 90.8 on MMLU-Redux, and 73.5 on MMLU-Pro. In reasoning and mathematics, the model scored 83.1 on AGIEval, 87.5 on Big-Bench Hard (BBH), 92.6 on Grade School Math 8K (GSM8K) and 64.5 on MATH. It also scored 51.5 on LongBench-V2. For coding evaluations, it achieved 76.8 pass@1 on HumanEval.

Microsoft asserted that MAI-Base-2 surpassed open-weight models on held-out text, code, and STEM assessments, but published no scores for those evaluations. Because Microsoft has not released model checkpoints or public API access, independent research teams cannot replicate or verify Microsoft's claims as of October 2026.

• DeepSeek self-publishes scores including 90.1 on MMLU, 76.8 on HumanEval, and 92.6 on GSM8K
• DeepSeek-V4-Pro-Base weights are downloadable on Hugging Face in 8-bit floating point (FP8) format
• Microsoft published no specific accuracy numbers, benchmark tables, or evaluation code for MAI-Base-2
• Independent third-party verification of MAI-Base-2 performance remains impossible until access is provided

Access Terms and API Pricing

DeepSeek V4 Pro is fully accessible today through commercial API endpoints and public weight repositories, whereas MAI-Base-2 offers no public access path or published pricing. Developers can query DeepSeek V4 Pro on the DeepSeek API under model identifier deepseek-v4-pro, listed on the DeepSeek API pricing page. Developers can also self-host the open weights released under the MIT license. Other self-hostable models are in the guide to the best open-weights AI models.

DeepSeek's API rates are covered in the DeepSeek V4 pricing guide and compared with other models on the AI model pricing page. Peak hours are 01:00 to 04:00 and 06:00 to 10:00 UTC, Monday to Friday, excluding Chinese public holidays. At peak, DeepSeek charges $0.044 per million input tokens on a cache hit, $1.32 on a cache miss and $3.96 per million output tokens, as of October 16, 2026. During off-peak hours, rates drop by 50% to $0.022 per million cache hit tokens, $0.66 per million cache miss tokens, and $1.98 per million output tokens. The API supports a 1 million token context window, up to 384,000 maximum output tokens, and a concurrency limit of 500 requests.

MAI-Base-2 has no published pricing, no API tier, and no consumer interface. Microsoft announced that pre-training is complete and post-training is underway, stating an intention to license model weights to trusted partners through Foundry in the future. Microsoft has not announced licensing terms, release dates, or partner qualifications, meaning developers cannot deploy MAI-Base-2 regardless of budget.

• DeepSeek V4 Pro provides live API access with cache-miss input rates of $1.32 at peak and $0.66 off-peak per million tokens
• DeepSeek V4 Pro weights are freely available under the permissive MIT open-source license
• DeepSeek supports 1 million context tokens with up to 384,000 output tokens on its API
• MAI-Base-2 pricing, commercial availability, and Foundry licensing terms remain unpublished

→ Compare per-token pricing for every AI model

Future Post-Training Milestones and Model Evolution

The ultimate impact of MAI-Base-2 depends on the post-trained models Microsoft constructs on top of this foundation. In June 2026, Microsoft introduced MAI-Thinking-1, a general-purpose model with strong reasoning built on MAI-Base-1. Microsoft is currently executing post-training on MAI-Base-2, and says more results are coming soon.

Microsoft stated that preparations for MAI-Base-3 are already underway, with no details published.

Build on DeepSeek V4 Pro today. Track MAI-Base-2 until Microsoft offers API access or weights.

• Microsoft is post-training MAI-Base-2 and says more results are coming soon
• Microsoft announced MAI-Thinking-1 in June 2026 and built it on MAI-Base-1
• Microsoft has already commenced initial preparations for its next iteration, MAI-Base-3

The Verdict

DeepSeek V4 Pro is the clear choice for engineering teams that need to deploy, test, or host a language model today. DeepSeek V4 Pro has MIT-licensed weights and a 1M token context window. Its API charges off-peak input rates of $0.022 per million tokens on a cache hit and $0.66 on a cache miss, as of October 16, 2026.

MAI-Base-2 is a research milestone rather than an operational product. Microsoft's 4x to 12x pre-training FLOPs-efficiency figure is self-reported. MAI-Base-2 cannot be accessed, tested or built into workflows as of October 2026. Neither model fits a team that needs image input today, because the DeepSeek API is text only and MAI-Base-2 is not available.

Organizations should avoid planning immediate product roadmaps around MAI-Base-2 until Microsoft releases post-trained checkpoints, publishes benchmark scores, and discloses commercial licensing terms through Foundry.

This verdict changes if Microsoft opens API or weights access to MAI-Base-2 and independent evaluations confirm its claims. Native vision and 2.8 trillion parameters would then make MAI-Base-2 worth testing against DeepSeek V4 Pro. Until those milestones occur, DeepSeek V4 Pro remains the only functional model in this comparison.