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values | verdict_text stringlengths 115 536 | licence stringlengths 3 30 | commercial_use stringlengths 3 28 | training_data stringclasses 7
values | origin stringlengths 2 16 | tags stringclasses 5
values | quality_index float64 7 54 ⌀ | output_tokens_per_second float64 0 409 ⌀ | blended_price_per_m float64 0 6 ⌀ | context_tokens null | last_reviewed_at stringdate 2026-04-15 00:00:00 2026-05-03 00:00:00 | llmradar_url stringlengths 31 51 | slug stringlengths 4 24 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Codestral 22B | Mistral AI | France | warn | EU code model trained on 80+ languages. Licensed under Mistral Non-Production License — blocked for any production or commercial deployment without a paid commercial licence. Use Codestral Mamba (Apache 2.0) if you need commercial freedom. | MNPL (non-prod) | Paid licence req. | Partial | EU | euorigin | null | null | null | null | 2026-04-15 | https://llmradar.eu/models/codestral | codestral |
Command R+ | Cohere | Canada | warn | Enterprise-focused 104B model, strong at RAG and multilingual tool use. Weights are released under CC-BY-NC 4.0 — non-commercial only. Commercial deployment goes through Cohere's API (see Cohere API entry). | CC-BY-NC 4.0 | API only | Partial | Canada | null | 8 | 0 | 6 | null | 2026-04-15 | https://llmradar.eu/models/command-r-plus | command-r-plus |
DBRX Instruct | Databricks | USA | warn | 132B MoE (36B active). Databricks Open Model License is bespoke — allows commercial use with acceptable-use policy and a 700M-MAU-style cap. Read the licence carefully; not Apache 2.0. | Databricks Open | With caps | Undisclosed | USA | commercial | 8 | 0 | 0 | null | 2026-04-15 | https://llmradar.eu/models/dbrx | dbrx |
DeepSeek R1 | DeepSeek | China | warn | Frontier reasoning model at o1-class performance. MIT licence makes weights legally clean. Same Chinese-origin alignment/supply-chain considerations as DeepSeek V3. Distilled Qwen/Llama versions inherit their base licence. | MIT | Yes | Undisclosed | China | permissive,commercial | 27 | 0 | 2.362 | null | 2026-04-15 | https://llmradar.eu/models/deepseek-r1 | deepseek-r1 |
DeepSeek V3 | DeepSeek | China | warn | MIT licence is maximally permissive. Weights are legally clean to self-host. Same Chinese-origin considerations as Qwen. | MIT | Yes | Undisclosed | China | permissive,commercial | 22 | 0 | 1.25 | null | 2026-04-15 | https://llmradar.eu/models/deepseek-v3 | deepseek-v3 |
DeepSeek V3.2 | DeepSeek | China | warn | 685B successor to V3 with DeepSeek Sparse Attention for long context, scalable RL for agentic tasks. Vendor claims parity with GPT-5 (Speciale variant exceeds). MIT licence keeps weights clean; Chinese-origin considerations unchanged. | MIT | Yes | Undisclosed | China | permissive,commercial | 32 | 32.25 | 0.315 | null | 2026-04-15 | https://llmradar.eu/models/deepseek-v3-2 | deepseek-v3-2 |
DeepSeek-V4-Flash | DeepSeek | China | warn | DeepSeek-V4-Flash is the smaller-active sibling of V4-Pro under the same MIT terms — permissive on the weights, but the China-based vendor and undocumented training corpus mean any EU deployment still needs a self-hosted topology and a deployer-side GPAI documentation file under AI Act Article 53. | MIT | Unrestricted | Categories only | China | permissive,commercial | 47 | 79.27 | 0.175 | null | 2026-04-28 | https://llmradar.eu/models/deepseek-v4-flash | deepseek-v4-flash |
DeepSeek-V4-Pro | DeepSeek | China | warn | Per the published LICENSE file, DeepSeek-V4-Pro ships under MIT with no commercial restrictions, so the weights themselves are deployable. The caveats are non-EU jurisdiction and a training corpus described only by aggregate token count (32T+) without a dataset list — both should be documented in any GDPR or AI Act com... | MIT | Unrestricted | Categories only | China | permissive,commercial | 52 | 35.63 | 2.175 | null | 2026-04-28 | https://llmradar.eu/models/deepseek-v4-pro | deepseek-v4-pro |
Falcon H1 34B | TII | UAE | warn | TII's hybrid Transformer+Mamba family that supersedes Falcon 180B. 18 languages including Arabic, strong benchmarks (MMLU 84, HumanEval 87). Licence is the Falcon-LLM License (not Apache 2.0) — commercial use permitted with attribution and acceptable-use terms; verify clauses for your deployment. | Falcon-LLM | Yes | Partial | UAE | commercial | null | null | null | null | 2026-04-16 | https://llmradar.eu/models/falcon-h1-34b | falcon-h1-34b |
Gemma 4 | Google | USA | ok | Major licence shift from Gemma 2/3: Apache 2.0 across the family. 140+ languages, multimodal (text/image/audio/video on small sizes), 128K-256K context. Strong permissive default for EU deployments that need robust multilingual support. | Apache 2.0 | Yes | Partial | USA | permissive,commercial | 32 | 0 | 0 | null | 2026-04-15 | https://llmradar.eu/models/gemma-4 | gemma-4 |
Gemma 4 26B A4B Instruct | Google DeepMind | United States | warn | Based on published licence terms, Gemma 4 26B A4B ships under pure Apache 2.0 with no prohibited-use carve-outs — a departure from prior Gemma generations. The sparse-MoE architecture (25.2B total / 3.8B active) puts it in an ambiguous zone for EU AI Act GPAI systemic-risk classification, and US origin plus image-input... | Apache 2.0 | Unrestricted | Domain-level summary | United States | permissive,commercial | 27 | 0 | 0 | null | 2026-04-17 | https://llmradar.eu/models/gemma-4-26b-a4b-it | gemma-4-26b-a4b-it |
Gemma 4 31B Instruct | Google DeepMind | United States | warn | Based on published licence terms, Gemma 4 31B ships under pure Apache 2.0 — a notable break from the Gemma Terms of Use used in prior generations — with no prohibited-use carve-outs. US origin carries Schrems-II and CLOUD-Act exposure, and at 30.7B dense the model likely crosses EU AI Act GPAI systemic-risk thresholds ... | Apache 2.0 | Unrestricted | Domain-level summary | United States | permissive,commercial | 32 | 0 | 0 | null | 2026-04-17 | https://llmradar.eu/models/gemma-4-31b-it | gemma-4-31b-it |
Gemma 4 E4B Instruct | Google DeepMind | United States | warn | Based on published licence terms, Gemma 4 E4B is an edge-optimised multimodal variant under pure Apache 2.0 with no prohibited-use carve-outs. Audio input (30s) and on-device deployment push GDPR biometric, AI Act emotion-recognition, and Art. 25 data-protection-by-design obligations entirely onto the integrator with n... | Apache 2.0 | Unrestricted | Domain-level summary | United States | permissive,commercial | 15 | 0 | 0 | null | 2026-04-17 | https://llmradar.eu/models/gemma-4-e4b-it | gemma-4-e4b-it |
GLM-4.5 | Zhipu AI | China | warn | MoE flagship from Zhipu under MIT. Strong agentic and coding benchmarks. Same Chinese-origin alignment and geopolitical considerations as DeepSeek / Qwen. | MIT | Yes | Undisclosed | China | permissive,commercial | 26 | 43.63 | 0.843 | null | 2026-04-15 | https://llmradar.eu/models/glm-4-5 | glm-4-5 |
GLM-5.1 | Zhipu AI (Z.ai) | China | warn | Per current documentation, GLM-5.1 is released under a verbatim MIT License with no use restrictions, enabling self-hosted commercial deployment. However, training-data opacity, Beijing-based publisher, and Zhipu AI's presence on the US BIS Entity List create EU AI Act transparency and supply-chain screening risks for ... | MIT | Unrestricted | Undisclosed | China (Beijing) | permissive,commercial | 44 | 47.58 | 2.15 | null | 2026-04-17 | https://llmradar.eu/models/glm-5-1 | glm-5-1 |
GPT-OSS 120b | OpenAI | USA | ok | OpenAI's first major open-weights release. Apache 2.0, MoE with 5.1B active over 117B total, MXFP4 quantised to fit a single 80GB GPU. Historic shift for a vendor that built its brand on closed weights. | Apache 2.0 | Yes | Undisclosed | USA | permissive,commercial | 33 | 211.59 | 0.263 | null | 2026-04-15 | https://llmradar.eu/models/gpt-oss-120b | gpt-oss-120b |
GPT-OSS 20B | OpenAI | USA | warn | Based on published licence terms, GPT-OSS 20B is released under Apache 2.0 with no field-of-use carve-outs in the licence itself; OpenAI publishes a separate non-binding 'gpt-oss usage policy' as guidance. Training-data disclosure is domain-level only, and US origin carries Schrems-II / CLOUD-Act exposure that enterpri... | Apache 2.0 | Unrestricted | Domain-level summary | United States | permissive,commercial | 25 | 293.7 | 0.1 | null | 2026-04-17 | https://llmradar.eu/models/gpt-oss-20b | gpt-oss-20b |
IBM Granite 3 | IBM | USA | ok | Enterprise-focused Granite 3 family under Apache 2.0, with unusual-for-the-industry training-data disclosure. IBM provides IP indemnification when used via watsonx. Strong default for regulated enterprise pilots. | Apache 2.0 | Yes | Disclosed | USA | permissive,commercial | 7 | 408.93 | 0.085 | null | 2026-04-15 | https://llmradar.eu/models/granite-3 | granite-3 |
IBM Granite 4.1 30B | IBM | USA | ok | Per the published model card, Granite 4.1 30B is an Apache 2.0 30-billion-parameter dense decoder with a 131k-token context, trained on the same publicly-available + synthetic + human-curated mix that anchors the rest of the Granite family. The larger sibling of Granite 4.1 8B, positioned for enterprise workloads where... | Apache 2.0 | Unrestricted | Disclosed | USA | permissive,commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/granite-4-1-30b | granite-4-1-30b |
IBM Granite 4.1 8B | IBM | USA | ok | Per the published model card, Granite 4.1 8B is an Apache 2.0 9B-parameter dense decoder with a 131k-token context, sourced from publicly-available datasets, internal synthetic data and human-curated material. IBM continues the unusual-for-the-industry training-data transparency that anchored the Granite 3 family, and ... | Apache 2.0 | Unrestricted | Disclosed | USA | permissive,commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/granite-4-1-8b | granite-4-1-8b |
Grok-2 | xAI | USA | warn | xAI's first open-weights release. Commercial use allowed under the Grok 2 Community License with xAI's Acceptable Use Policy. Notable restriction: weights cannot be used to train other models (distillation ban). 500GB model, needs 8 GPUs with 40GB+. | Grok 2 Community | Yes (w/ AUP) | Undisclosed | USA | commercial | 14 | 0 | 0 | null | 2026-04-16 | https://llmradar.eu/models/grok-2 | grok-2 |
Hy3-preview | Tencent | null | ko | Per the Tencent Hy Community License Agreement (Sections 1(l) and 5(c)), the licence's defined Territory excludes the European Union, the United Kingdom and South Korea, and licensees are expressly prohibited from using, distributing or displaying the model or its outputs outside that Territory. Under those terms the w... | Tencent Hy Community | EU territory excluded | Undisclosed | China | null | 42 | 85.04 | 0 | null | 2026-04-28 | https://llmradar.eu/models/hy3-preview | hy3-preview |
Jamba 1.5 Large | AI21 Labs | Israel | warn | SSM-Transformer hybrid (Mamba) with 256K context. Jamba Open Model License permits commercial use below $50M annual revenue; above that, paid licence required. Israel jurisdiction; EU adequacy decision in place. | Jamba Open | Under $50M rev. | Undisclosed | Israel | commercial | 11 | 0 | 3.5 | null | 2026-04-15 | https://llmradar.eu/models/jamba-1-5-large | jamba-1-5-large |
Kimi K2 Instruct | Moonshot AI | China | warn | 1T-parameter MoE (32B active) tuned for agentic and tool-use workflows. Modified MIT permits commercial use. Same Chinese-origin alignment and supply-chain considerations as DeepSeek and Qwen. | Modified MIT | Yes | Undisclosed | China | permissive,commercial | 26 | 34.33 | 1.039 | null | 2026-04-16 | https://llmradar.eu/models/kimi-k2 | kimi-k2 |
Kimi-K2.6 | Moonshot AI | China | warn | Per the published LICENSE file, Kimi-K2.6 ships under a Modified MIT licence: identical to standard MIT for almost all deployers, with an additional UI-attribution requirement only for products exceeding 100M monthly active users or USD 20M monthly revenue. The genuine EU-readiness concerns are the China-based vendor a... | Modified MIT | Attribution at scale | Undisclosed | China | commercial | 54 | 0 | 1.712 | null | 2026-04-28 | https://llmradar.eu/models/kimi-k2-6 | kimi-k2-6 |
Laguna XS.2 | Poolside | USA | warn | Per the published model card, Laguna XS.2 is an Apache 2.0 33B / 3B-active MoE positioned for local agentic coding, with a 131k-token context and FP8 KV cache aimed at single-machine inference. Permissive license and self-hostable weights make EU-side deployment straightforward; the limits are vendor jurisdiction (San ... | Apache 2.0 | Unrestricted | Undisclosed | USA | permissive,commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/laguna-xs-2 | laguna-xs-2 |
Ling-2.6 1T | inclusionAI | China | warn | Per the published model card, Ling-2.6 1T is an MIT-licensed 1-trillion-parameter MoE with a 262k-token context, hybrid MLA + Linear attention and multi-token-prediction support, targeted at production agentic workloads. Permissive weights enable EU self-hosting in principle, though the deployment footprint is non-triv... | MIT | Unrestricted | Undisclosed | China | permissive,commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/ling-2-6-1t | ling-2-6-1t |
Ling-2.6 Flash | inclusionAI | China | warn | Per the published model card, Ling-2.6 Flash is an MIT-licensed 104B / 7.4B-active MoE built on a hybrid Lightning-Linear + MLA attention design, positioned for agentic and tool-use workflows. Permissive weights are deployable in EU infrastructure; the headline risks for regulated buyers are vendor jurisdiction (Ant Gr... | MIT | Unrestricted | Undisclosed | China | permissive,commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/ling-2-6-flash | ling-2-6-flash |
Llama 3.1 405B | Meta | USA | warn | Frontier-class 405B open model. Self-hosting requires serious compute (8×H100 minimum at FP8). Same Llama community licence caveats as the rest of the family. | Llama community | With caps | Undisclosed | USA | commercial | 17 | 31.07 | 3.688 | null | 2026-04-15 | https://llmradar.eu/models/llama-3-1-405b | llama-3-1-405b |
Llama 3.1 8B Instruct | Meta Platforms | United States | warn | Per current documentation, Llama 3.1 8B Instruct is released under the Llama 3.1 Community Licence — a custom source-available licence rather than OSI open source. Commercial deployment is permitted below 700M MAU subject to the Acceptable Use Policy and attribution rules, but training-data opacity and US origin create... | Llama 3.1 Community Licence | Restricted (MAU cap + AUP) | Token count only | United States | commercial | 12 | 160 | 0.1 | null | 2026-04-17 | https://llmradar.eu/models/llama-3-1-8b-instruct | llama-3-1-8b-instruct |
Llama 3.3 70B | Meta | USA | warn | Strong 70B model, near-flagship quality at smaller size. Same Llama community licence as Llama 4: 700M MAU cap, acceptable-use policy, 'Built with Llama' attribution required. | Llama community | With caps | Undisclosed | USA | commercial | 15 | 96.83 | 0.675 | null | 2026-04-15 | https://llmradar.eu/models/llama-3-3-70b | llama-3-3-70b |
Llama 4 Maverick | Meta | USA | warn | Llama 4 flagship: MoE with 17B active over 128 experts, natively multimodal (text + images). Same Llama community licence as the family: 700M MAU cap, acceptable-use policy, 'Built with Llama' attribution. | Llama community | With caps | Undisclosed | USA | commercial | 18 | 116.04 | 0.5 | null | 2026-04-15 | https://llmradar.eu/models/llama-4-maverick | llama-4-maverick |
Llama 4 Scout | Meta | USA | warn | Smaller Llama 4 variant: 17B active over 16 experts, multimodal. More self-hostable than Maverick. Same Llama community licence caveats. | Llama community | With caps | Undisclosed | USA | commercial | 14 | 128.16 | 0.292 | null | 2026-04-15 | https://llmradar.eu/models/llama-4-scout | llama-4-scout |
MiMo-V2.5 | Xiaomi (MiMo) | null | warn | MiMo-V2.5 is the omnimodal sibling of MiMo-V2.5-Pro — text, vision, audio, and video on a single sparse-MoE backbone, also under MIT. Same posture as the Pro: deployable weights, but the China origin and stage-level training disclosure mean any EU rollout needs self-hosting plus a deployer-prepared GPAI compliance file... | MIT | Unrestricted | Categories only | China | permissive,commercial | 49 | 0 | 0 | null | 2026-04-28 | https://llmradar.eu/models/mimo-v2-5 | mimo-v2-5 |
MiMo-V2.5-Pro | Xiaomi (MiMo) | null | warn | Per the published LICENSE file, MiMo-V2.5-Pro ships under MIT, so the weights themselves carry no commercial restriction. The remaining EU-readiness gaps are the China-based vendor and the corpus disclosure that names training-stage categories (text pre-training, multimodal pre-training, SFT, RL, MOPD) without listing ... | MIT | Unrestricted | Categories only | China | permissive,commercial | 54 | 64.79 | 1.5 | null | 2026-04-28 | https://llmradar.eu/models/mimo-v2-5-pro | mimo-v2-5-pro |
MiniMax M2 | MiniMax | China | warn | 229B agent-focused model from MiniMax, Modified MIT. Strong software-engineering and tool-use benchmarks. Family has iterated fast (M2 / M2.1 / M2.5 / M2.7 across 2025-2026). Same Chinese-origin alignment and supply-chain considerations as DeepSeek, Qwen, Kimi. | Modified MIT | Yes | Undisclosed | China | permissive,commercial | 36 | 72.4 | 0.525 | null | 2026-04-16 | https://llmradar.eu/models/minimax-m2 | minimax-m2 |
MiniMax-M2.7 | MiniMax AI | China | ko | Per current documentation, the MiniMax Non-Commercial License prohibits commercial deployment without individually negotiated written authorization from MiniMax, making the weights unsuitable for EU commercial workloads out-of-the-box. Opaque training data and Shanghai-based publisher compound the EU AI Act and data-tr... | MiniMax Non-Commercial License | Non-commercial only | Undisclosed | China (Shanghai) | null | 50 | 45.78 | 0.525 | null | 2026-04-17 | https://llmradar.eu/models/minimax-m2-7 | minimax-m2-7 |
Mistral 7B Instruct v0.2 | Mistral AI | France | warn | Based on published licence terms, Mistral 7B Instruct v0.2 is an EU-origin open-weight model under standard Apache 2.0 — commercial deployment and self-hosting are permitted without field-of-use restrictions. Training-data opacity is the primary EU AI Act Art. 53 gap, but the French controller, absence of CLOUD Act exp... | Apache 2.0 | Unrestricted | Undisclosed | EU (France) | permissive,commercial,euorigin | 7 | 192.54 | 0.25 | null | 2026-04-17 | https://llmradar.eu/models/mistral-7b-instruct-v0-2 | mistral-7b-instruct-v0-2 |
Mistral Large 2 | Mistral AI | France | warn | Frontier 123B dense model from an EU vendor. Open weights under Mistral Research License — non-commercial by default. Commercial deployment requires a separate paid licence from Mistral AI. | MRL (research) | Paid licence req. | Undisclosed | EU | euorigin | 15 | 37.56 | 3 | null | 2026-04-15 | https://llmradar.eu/models/mistral-large-2 | mistral-large-2 |
Mistral Medium 3.5 | Mistral AI | France | ok | Per Mistral's published license, Mistral Medium 3.5 ships under a Modified MIT License: vanilla-MIT terms for independent developers, startups and most enterprises, with a revenue-threshold carve-out that routes large companies back to a paid commercial agreement. Paris-headquartered vendor with EU jurisdiction end to ... | Modified MIT | Permitted (revenue cap) | Undisclosed | EU (France) | commercial,euorigin | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/mistral-medium-3-5 | mistral-medium-3-5 |
Mistral Small | Mistral AI | France | ok | Best-in-class permissive licence from an EU vendor. Apache 2.0 means no usage caps, no royalty, no revocation risk. | Apache 2.0 | Yes | Partial | EU | permissive,commercial,euorigin | 13 | 151.59 | 0.15 | null | 2026-04-15 | https://llmradar.eu/models/mistral-small | mistral-small |
Mistral Small 4 | Mistral AI | France | ok | Unified model folding Instruct, reasoning (Magistral) and code (Devstral) into a single 119B MoE under Apache 2.0. 6.5B active params, 256K context, 24 languages, toggleable reasoning effort. Strongest permissive EU option at this scale. | Apache 2.0 | Yes | Undisclosed | EU | permissive,commercial,euorigin | 19 | 147.18 | 0.263 | null | 2026-04-15 | https://llmradar.eu/models/mistral-small-4 | mistral-small-4 |
Nemotron-3 Nano Omni 30B-A3B Reasoning | NVIDIA | USA | warn | Per the NVIDIA Open Model Agreement, Nemotron-3 Nano Omni is commercially usable with a NOTICE-file attribution requirement and U.S. export-compliance obligations. Multimodal MoE (31B total / 3B active) accepting video, audio, image and text input, with reasoning-style chain-of-thought output. Training data is unusuall... | NVIDIA Open Model | Permitted (with attribution) | Disclosed | USA | commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/nemotron-3-nano-omni-30b | nemotron-3-nano-omni-30b |
Llama 3.1 Nemotron 70B | NVIDIA | USA | warn | NVIDIA's Llama 3.1 fine-tune with custom RLHF. Inherits Llama 3.1 Community License terms. Strong conversational quality; useful default when you want Llama behaviour with NVIDIA's alignment. | Llama community | With caps | Partial | USA | commercial | 13 | 41.68 | 1.2 | null | 2026-04-15 | https://llmradar.eu/models/nemotron-70b | nemotron-70b |
OLMo 2 32B | AllenAI | USA | ok | Fully open model: weights, training data (Dolma 2), training code, checkpoints, and logs all published. Apache 2.0 across the board. Strongest choice when AI Act transparency obligations matter. | Apache 2.0 | Yes | Disclosed | USA | permissive,commercial | 11 | 0 | 0 | null | 2026-04-15 | https://llmradar.eu/models/olmo-2 | olmo-2 |
Phi-4 | Microsoft | USA | ok | MIT-licensed 14B from Microsoft Research. Heavy use of synthetic training data is disclosed; English-primary (thin multilingual coverage). Strongest small-model option for permissive-licence EU deployments. | MIT | Yes | Partial | USA | permissive,commercial | 10 | 29.01 | 0.219 | null | 2026-04-15 | https://llmradar.eu/models/phi-4 | phi-4 |
Qwen 2.5 | Alibaba | China | warn | Legally clean under Apache 2.0, but Chinese origin raises supply-chain and geopolitical questions. Vet carefully for sensitive use cases. | Apache 2.0 | Yes | Undisclosed | China | permissive,commercial | 16 | 54.85 | 0 | null | 2026-04-15 | https://llmradar.eu/models/qwen-2-5 | qwen-2-5 |
Qwen 3.5 | Alibaba | China | warn | Hybrid Gated-DeltaNet + MoE flagship (397B total, 17B active) under Apache 2.0. Native vision, 201 languages, 262K context (1M with YaRN). Licence is clean; Chinese-origin alignment and supply-chain considerations persist. | Apache 2.0 | Yes | Undisclosed | China | permissive,commercial | 40 | 52.87 | 1.35 | null | 2026-04-15 | https://llmradar.eu/models/qwen-3-5 | qwen-3-5 |
Qwen3-8B | Alibaba Cloud (Qwen) | China | warn | Based on published licence terms, Qwen3-8B is released under standard Apache 2.0 with no field-of-use carve-outs, making self-hosted commercial deployment viable. Training-data disclosure is limited to a token count and Chinese origin creates EU AI Act Art. 53 transparency and data-transfer risks that deployers should ... | Apache 2.0 | Unrestricted | Token count only | China (Hangzhou) | permissive,commercial | 11 | 85.98 | 0.31 | null | 2026-04-17 | https://llmradar.eu/models/qwen-3-8b | qwen-3-8b |
Qwen3.6-27B | Alibaba (Qwen) | null | warn | Per the published Apache 2.0 licence, the Qwen3.6-27B weights are deployable without commercial restriction, including for vision-language and 1M-context workloads. The blockers for regulated EU use are the China-based vendor and the absence of any training-data disclosure on the model card — both should be mitigated t... | Apache 2.0 | Unrestricted | Undisclosed | China | permissive,commercial | 46 | 66.28 | 1.35 | null | 2026-04-28 | https://llmradar.eu/models/qwen3-6-27b | qwen3-6-27b |
Qwen3.6-35B-A3B | Alibaba Cloud (Qwen) | China | warn | Based on published licence terms, Qwen3.6-35B-A3B ships under Apache 2.0 with no use restrictions, making self-hosted commercial deployment viable. However, opaque training-data disclosure and Chinese origin create EU AI Act Art. 53 transparency and data-transfer risks that deployers should document before placing pers... | Apache 2.0 | Unrestricted | Undisclosed | China (Hangzhou) | permissive,commercial | 44 | 237.63 | 0.844 | null | 2026-04-17 | https://llmradar.eu/models/qwen3-6-35b-a3b | qwen3-6-35b-a3b |
QwQ-32B | Alibaba | China | warn | 32B dense reasoning model under Apache 2.0. Sweet spot for self-hostable reasoning: 4090-class GPU at 4-bit, single H100 at bf16. Chinese-origin caveats unchanged. | Apache 2.0 | Yes | Undisclosed | China | permissive,commercial | 20 | 32.6 | 0.745 | null | 2026-04-15 | https://llmradar.eu/models/qwq-32b | qwq-32b |
SmolLM3 3B | Hugging Face | USA | ok | Fully open small model: Apache 2.0 weights, training data published, engineering blueprint public. 6 native languages (EN/FR/ES/DE/IT/PT) covers major EU markets. 128K context via YARN. Strong default for edge or on-prem EU deployments where transparency matters. | Apache 2.0 | Yes | Disclosed | USA | permissive,commercial | null | null | null | null | 2026-04-16 | https://llmradar.eu/models/smollm3-3b | smollm3-3b |
Talkie-1930-13B Base | Talkie-LM (research) | null | warn | Per the published model card, Talkie-1930-13B Base is the pretrained sibling of the Talkie-1930 instruction-tuned release: an Apache 2.0 13B model trained on 260B tokens of pre-1931 English text drawn entirely from public-domain sources. Training-data transparency is unusually clean for AI Act Article 53 purposes; the ... | Apache 2.0 | Unrestricted | Documented | US (research) | permissive,commercial | null | null | null | null | 2026-05-03 | https://llmradar.eu/models/talkie-1930-13b-base | talkie-1930-13b-base |
Talkie-1930-13B-IT | Talkie-LM (research) | null | warn | Per the published model card, Talkie-1930-13B-IT is an Apache 2.0 instruction-tuned 13B model trained exclusively on pre-1931 English text (260B tokens, sourced from public-domain reference works). The training-data transparency is unusually clean for AI Act Article 53 purposes; the limits are vendor jurisdiction (a US... | Apache 2.0 | Unrestricted | Documented | US (research) | permissive,commercial | null | null | null | null | 2026-04-28 | https://llmradar.eu/models/talkie-1930-13b-it | talkie-1930-13b-it |
EU-readiness of open-weight LLMs
Curated by LLM Radar — updated 2026-05-03 — 55 models.
A manually-reviewed dataset assessing open-weight Large Language Models (LLMs) on their suitability for EU deployment and commercial use. Each model is evaluated on licence, commercial use, training data, and origin, with quality / speed / price metrics from Artificial Analysis where available.
Primary use cases:
- Selecting open-weight models for self-hosted EU deployment
- Licensing and commercial-use due diligence
- Training classifiers on open-source LLM licensing and provenance
- Tracking the emergence of EU-origin and permissively-licensed models
Fields
| column | description |
|---|---|
name |
Human-readable model name |
vendor_name |
Publisher / research lab |
vendor_country |
HQ country of the vendor |
verdict |
Overall verdict: ok, warn, or ko |
verdict_text |
One-line editorial verdict |
licence |
Licence summary (Apache-2.0, MIT, custom, etc.) |
commercial_use |
Commercial-use posture |
training_data |
Training-data disclosure / provenance |
origin |
Origin / publisher jurisdiction |
tags |
Comma-separated topic tags (permissive, commercial, euorigin) |
quality_index |
Artificial Analysis Quality Index (0–100, higher is better) |
output_tokens_per_second |
Median output speed (tokens/s) |
blended_price_per_m |
Blended input+output price per million tokens (USD) |
context_tokens |
Advertised context window |
last_reviewed_at |
ISO date of last manual review |
llmradar_url |
Canonical URL of the full model page |
slug |
Stable identifier on LLM Radar |
Performance metrics (quality / speed / price) are sourced from Artificial Analysis under their public API and are attributed there; see their terms for reuse.
Methodology
Each entry is reviewed manually against public sources (provider docs, terms of service, data-protection agreements, sub-processor lists, regulator guidance, model cards, and vendor statements). Badges use a three-tier traffic-light scheme:
- green — meets the EU/GDPR/AI-Act criterion without material caveats
- amber — partial fit, conditional, or requires customer action to qualify
- red — does not meet the criterion, or requires data transfer outside the EEA
See the full methodology at https://llmradar.eu/methodology.
License & attribution
Released under CC BY 4.0. You may share and adapt the dataset for any purpose, including commercial use, provided you give appropriate credit and link back to the source:
Data from LLM Radar — https://llmradar.eu — licensed under CC BY 4.0.
Updates
This dataset is regenerated on a weekly cadence from the LLM Radar
editorial database. Each row carries a last_reviewed_at date so you can
filter for recency.
Citation
@misc{llmradar_eu-open-weight-models,
title = { EU-readiness of open-weight LLMs },
author = { {LLM Radar} },
year = { 2026 },
howpublished = { \url{https://huggingface.co/datasets/llmradar/eu-open-weight-models} },
note = { CC BY 4.0 }
}
Report corrections
Found an inaccuracy? Submit a correction or exercise your right of reply at https://llmradar.eu/corrections or https://llmradar.eu/right-of-reply.
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