model licensing

Kimi K3.1 Pro's 1.56 TB weights are public and free for internal use. An eight-GPU B300 node breaks even with the $2/$10 API near 13 billion input tokens a month, and legal and tax scores fell from K3.

Mistral Large 4's 1.05T-parameter weights are on Hugging Face. At FP8 they fit one eight-GPU DGX B200 node, internal use is free, and serving the model to outside customers needs a separate commercial licence from Mistral.

Muse Spark Open is the closest US replacement for Kimi K3, five index points behind and cheaper per task. Defense, nuclear and critical-infrastructure buyers should pick Apache 2.0 Reflection Beam instead.

Meta's 816B Muse Spark Open is free for internal use, but its license bans operating critical infrastructure and any nuclear or military use, the same clauses Llama 3 carried.

Prime Intellect bought Moondream for its Photon engine, and Moondream Cloud and Lens close November 13. Base weights self-host free, but local fine-tunes still need a Moondream API key.

Nvidia is reportedly in talks to buy, acquihire or take a bigger stake in Reflection AI, and Beam's promised Apache 2.0 weights have not been published yet. Mirror a released checkpoint before you standardise on Beam.

For annual 10 m scoring, AlphaEarth embeddings plus a few hundred of your own labels match a bespoke imagery pipeline. Pin the version, validate with a geographic buffer, and keep the per-scene vendor only for daily or sub-metre work.

KumoRFM's 14-point RelBench lead was over a LightGBM that saw only one table. Keep XGBoost in production, test KumoRFM against your own model, and get the NIM licence in writing first.

Mistral Large 4 beats Kimi K3 on Harvey's legal agent benchmark and trails it on the Vals Index. It is a discounted preview API for now, and the licence for its end-of-October weights is unpublished.

Chronos-2 is the forecasting foundation model to self-host: Apache 2.0, P1 to P99 quantiles and a Decathlon deployment behind it. TimesFM 3.0 tops Google's benchmarks, but its weights are licensed for production only inside BigQuery, and Moirai 2.0's are non-commercial.

Mitra-v2 is Apache 2.0 and beats tuned XGBoost on TabArena, but caps context at 32,768 rows. TabPFN-3.5 scales to a million rows behind an unpublished commercial licence, and trees still win on large, temporal and CPU-bound work.

NVIDIA's Kumo Tabular ships under OpenMDW-1.1 for commercial use, while TabPFN-3.5's open weights stay non-commercial. Its #1 TabArena Elo is NVIDIA's own run, and it was pretrained only to 60,000 rows and 100 columns.

For air-gapped LLMs, run Apache-2.0 weights like gpt-oss-120b on vLLM, via Red Hat AI if you need a vendor. NVIDIA AI Enterprise's per-GPU fee is the loser, and an 8-GPU air-gapped node costs 30-40x the hosted API at 1B tokens a month, and even two GPUs cost about 9x.