Summary: Rakuten Group made Rakuten AI 3.0 generally available on March 17, 2026, releasing all weights on Hugging Face under Apache 2.0 as Japan’s largest high-performance AI model at roughly 671B parameters. The open release honors Rakuten’s December 2025 pledge and gives Japanese-language teams a domestic self-hosting option through the GENIAC-backed program. The claimed up-to-90% saving is per-token only, while the full MoE is heavy enough that many teams will need distillation, hosted infrastructure, or teacher-model use rather than direct deployment.
Rakuten Group put Rakuten AI 3.0 into general availability on March 17, 2026. The model is a Mixture-of-Experts design carrying roughly 671 billion total parameters — Rakuten’s own release rounds this to “about 700 billion” — and every weight is on Hugging Face under the Apache 2.0 license. The company bills it as Japan’s largest high-performance AI model, and it stands as the most prominent result yet from GENIAC, the national generative AI program that METI and NEDO run. With this release, the open-weight pledge Rakuten made when it first showed the model in December 2025 is honored.
The takeaway for developers needs no cushioning: no Japanese company had ever open-sourced a complete, Japanese-optimized model at the 671B scale before. Alongside Qwen, DeepSeek, and GPT-OSS, teams working in Japanese now have a domestic heavyweight they are free to host themselves.
Three Releases, from 7B to 671B
The company built up to this in stages. February 2025 brought Rakuten AI 7B; the Rakuten AI 2.0 family (8x7B at 47B parameters, plus a 2B mini) landed the same month; and Rakuten AI 3.0 received its first public showing on December 18, 2025. Two figures led that announcement. On the Japanese MT-Bench the new model scored 8.88, clearing gpt-4o (8.67), plamo-2.1-prime (7.85), and ABEJA-Qwen2.5-32b (8.04). And standing in for the third-party frontier models behind Rakuten’s own ecosystem services, it showed per-token cost savings of up to 90% in the comparison. The training itself ran on a GPU cluster of Rakuten’s own, and the weights shipped only after roughly three more months of fine-tuning.
GENIAC, or How the Government Pays for Compute
GENIAC — Generative AI Accelerator Challenge in full — is the vehicle METI and NEDO use to cultivate generative AI inside Japan, and what it hands out, at bottom, is training compute. Rakuten entered with the program’s third term in July 2025, and GENIAC picked up part of what it cost to train Rakuten AI 3.0. The arrangement follows the familiar sovereign-AI script: a government puts up compute, a company puts in the engineering, and in exchange the nation gains model autonomy over a language and an industry it cares about. On the very same day, South Korea ran its own version of the play, as news spread that Reflection AI and Shinsegae are building a 250MW sovereign AI factory. Two export-driven Asian powers, two different instruments, one and the same gap being closed.
How the Model Scores on Japanese Benchmarks
Four benchmarks were run against the production release: JamC-QA, which probes Japanese cultural knowledge plus history and geography; MMLU-ProX, a graduate-level reasoning test; MCLM MATH-100, drawn from competitive math; and M-IFEval, a check of Japanese instruction following. Both reference models were Japanese-tuned — GPT-OSS-Swallow-120B-RL-v0.1 and ABEJA-QwQ32b-Reasoning-Japanese-v1.0 — and the published scores received a revision on March 18, 2026. Back at the December unveiling, Rakuten had also framed that 8.88 MT-Bench figure across generations: 6.79 for Rakuten AI 2.0 and just 4.35 for the original 7B. The company names writing, code generation, and document analysis and extraction as the model’s strong suits. In Chief AI and Data Officer Ting Cai’s telling, Rakuten means to keep delivering models that are high in quality and cost-efficient, and to share them openly so that AI development across Japan moves faster.
What an Open-Weight 671B Actually Takes
Under the hood, every token is dispatched to eight specialized experts alongside one always-active shared expert, leaving about 40 billion parameters firing at any moment. That is DeepSeek-V3’s blueprint, and the prevailing read across community threads such as r/LocalLLaMA is that this is an open base carrying Japanese tuning, not a pretrain begun from zero. None of that cancels the model’s worth; it just calibrates what to expect from it. The Apache 2.0 license poses no obstacle to commercial use — the obstacle is bulk, because the complete weights remain so heavy that plenty of teams can fetch them yet never put them to work. Three postures work in practice: host it yourself across multiple GPUs and nodes, distill it into something smaller, or let it serve as the teacher model behind Japanese evaluation and data-processing work.
The Developer Takeaways
Three points are worth acting on. Japanese-first products — customer support, document processing, compliance review — can at last run a domestic 671B on their own infrastructure, putting the “data never leaves the country” compliance argument on solid footing. The headline 90% saving rests on per-token cost comparisons alone, so any team leaning hard on Japanese should work out for itself where self-hosted total cost of ownership crosses over API pricing. And since GENIAC subsidized the training while the release carries Apache 2.0, public money has effectively turned into weights anyone can download — expect the next wave of Japanese distillations and tooling to rise on this base.