On March 17, 2026, Rakuten Group announced general availability of Rakuten AI 3.0: a Mixture-of-Experts model with roughly 671 billion total parameters (Rakuten’s release rounds it to “about 700 billion”), published in full on Hugging Face under Apache 2.0. Rakuten calls it Japan’s largest high-performance AI model, it is the most visible output to date of GENIAC — the national generative AI program run by METI and NEDO — and it makes good on the open-weight promise the company made when it unveiled the model in December 2025.
For developers the significance is blunt: this is the first time a Japanese company has open-sourced a complete, Japanese-optimized model at the 671B scale. Beyond Qwen, DeepSeek, and GPT-OSS, Japanese-language workloads now have a homegrown heavyweight that can be self-hosted.
From 7B to 671B: Rakuten’s Three-Step Leap
Rakuten’s open-source track record came in stages: Rakuten AI 7B in February 2025, the Rakuten AI 2.0 family the same month (8x7B at 47B parameters, plus a 2B mini), and the first look at Rakuten AI 3.0 on December 18, 2025. At that unveiling Rakuten published two headline numbers: 8.88 on the Japanese MT-Bench, ahead of gpt-4o at 8.67, plamo-2.1-prime at 7.85, and ABEJA-Qwen2.5-32b at 8.04; and up to 90% cost reduction on per-token comparisons against third-party frontier models when powering Rakuten’s own ecosystem services. Training ran on Rakuten’s in-house GPU cluster, followed by roughly three months of fine-tuning before this release.
GENIAC: The State-Funded Compute Backbone
GENIAC — the Generative AI Accelerator Challenge — is Japan’s METI- and NEDO-run program to grow domestic generative AI, and its core subsidy is training compute. Rakuten was selected for the program’s third term in July 2025, and GENIAC covered part of Rakuten AI 3.0’s training cost. This is the standard sovereign-AI playbook: the state supplies compute, a company supplies engineering, and the country buys model autonomy in a key language and industry. The same day, South Korea produced its own version of the move — news broke of Reflection AI and Shinsegae building a 250MW sovereign AI factory. Two Asian export powers, different instruments, same missing piece being filled.
The Japanese-Language Benchmark Scorecard
The production release was evaluated on four benchmarks: JamC-QA (Japanese cultural knowledge, history, and geography), MMLU-ProX (graduate-level reasoning), MCLM MATH-100 (competitive math), and M-IFEval (Japanese instruction following), compared against two Japanese-tuned peers — GPT-OSS-Swallow-120B-RL-v0.1 and ABEJA-QwQ32b-Reasoning-Japanese-v1.0 — with scores revised on March 18, 2026. The December unveiling had also put the model’s Japanese MT-Bench lead in generational context: 8.88 against 6.79 for Rakuten AI 2.0 and 4.35 for the original 7B. Rakuten highlights writing, code generation, and document analysis and extraction as the model’s strengths. Chief AI and Data Officer Ting Cai’s framing: Rakuten is committed to delivering high-quality, cost-efficient models, and open sharing is meant to accelerate AI development across Japan.
The Reality Check of an Open-Weight 671B
Architecturally, each token routes through eight specialized experts plus one always-on shared expert, with roughly 40 billion active parameters — a layout that matches DeepSeek-V3’s design, and community discussion on r/LocalLLaMA largely treats the model as a Japanese-tuned build on an open base rather than a from-scratch pretrain. That does not erase its value, but it sets the right expectations. Apache 2.0 is friendly to commercial use; the catch is that the full weights are still enormous, and most teams can download the model but not run it. The practical postures are three: self-host across multi-GPU nodes, distill it into smaller models, or use it as a teacher for Japanese evaluation and data-processing work.
What It Means for Developers
Three practical takeaways. First, Japanese-first products — customer support, document processing, compliance review — now have a domestic 671B that can be self-hosted, which makes the “data never leaves the country” compliance story real. Second, Rakuten’s claimed 90% savings come from per-token cost comparisons; where the crossover between self-hosted total cost of ownership and API pricing actually sits is worth recalculating for any heavy Japanese workload. Third, GENIAC-subsidized training plus an Apache 2.0 release effectively converts public funding into downloadable weights for anyone — expect the next wave of Japanese distillations and tooling to grow on this base.
Sources
- Rakuten AI 3.0 Now Available, Japan’s Largest High-Performance AI Model — Rakuten Group
- Rakuten Unveils Japan’s Largest High-Performance AI Model — Rakuten Group
- RakutenAI-3.0 model card — Hugging Face
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.