FLUX.2 Klein 4B
Apache 2.0Black Forest Labs · 4B · Apache 2.0
Fastest open FLUX.2 — sub-second text-to-image and multi-reference editing on consumer GPUs
2.5GB·32K ctx·
RTX 3080 12GB で動くローカル AI モデル
RTX 3080 12GB 向けに評価したオープン LLM・コーディング・画像・動画モデル。
GPURTX 3080 12GBVRAMBWRAMCores—
このデバイス向けのおすすめオープンモデル： お使いのデバイス
コーディングモデルは動く？
チャット・コード・推論
画像モデルは動く？
ローカルで画像生成
動画モデルは動く？
ローカルで動画生成
超小型モデルは動く？
オンデバイス、4B 以下
このデバイスはかなり制約が大きいです
まだ快適に動くモデルがありません。 すべて見る で最も軽いモデルを確認できます。
8か月前
Wan 2.2 TI2V 5B
Apache 2.0Alibaba · 5B · Apache 2.0
Unified text/image-to-video — the local sweet spot under Apache 2.0
3.1GB·4K ctx·
Z-Image Turbo
Apache 2.0Alibaba · 6B · Apache 2.0
8-step distilled image model — photorealism and bilingual text on 16GB cards
3.6GB·4K ctx·
HunyuanVideo 1.5
Tencent Hunyuan CommunityTencent · 8.3B · Tencent Hunyuan Community
Compact cinematic video model — strong faces and motion on a single 4090
4.8GB·4K ctx·
Qwen3-VL 8B
Apache 2.0Alibaba · 8.8B · Apache 2.0
The community-favourite local VLM — superb OCR, receipts & captioning
5GB·256K ctx·
Qwen 3.5 9B
Apache 2.0Alibaba · 9B · Apache 2.0
Multimodal Qwen 3.5 mid-size
22 AA·5.1GB·32K ctx·
LTX 2.3
LTX-2 CommunityLightricks · 19B · LTX-2 Community
Open 4K video with native stereo audio — text, image and video-to-video
10.2GB·4K ctx·
Qwen Image 2512
Apache 2.0Alibaba · 20B · Apache 2.0
Open text-to-image with strong English and Chinese typography
10.7GB·4K ctx·
GPT-OSS 20B
Apache 2.0OpenAI · 21B · Apache 2.0
OpenAI's open-weight MoE with configurable reasoning
15 AA·11.3GB·128K ctx·
Gemma 4 26B-A4B IT
GemmaGoogle · 27B · Gemma
Gemma 4 MoE instruct model (official)
26 AA·14.3GB·256K ctx·
Qwen 3.8 27B
Apache 2.0Alibaba · 27B · Apache 2.0
Flagship dense Qwen 3.8 — native multimodal all-rounder with video understanding
52 AA·14.3GB·256K ctx·
Wan 2.2 T2V A14B
Apache 2.0Alibaba · 27B · Apache 2.0
Flagship open Wan 2.2 — 14B-active MoE for photoreal text-to-video
14.3GB·4K ctx·
Muse Glimmer 30B
Apache 2.0Meta · 30B · Apache 2.0
Open agentic 30B distilled from Muse Spark — tool use, vision and local recovery on a single GPU
35 AA·15.9GB·128K ctx·
Qwen3-VL 30B-A3B
Apache 2.0Alibaba · 31B · Apache 2.0
Efficient vision MoE — 3B active, strong temporal & document understanding
16.4GB·256K ctx·
FLUX.2 Dev
FLUX Non-CommercialBlack Forest Labs · 32B · FLUX Non-Commercial
Flagship open-weight FLUX.2 — text-to-image and multi-reference editing up to 4MP
16.9GB·32K ctx·
MiniMax H3
MiniMax CommunityMiniMax · 33B · MiniMax Community
Open video generation — text/image to 2K video with native stereo audio
17.4GB·32K ctx·
Agents-A1 35B-A3B
Apache 2.0InternScience · 35B · Apache 2.0
Efficient multimodal agentic MoE for long-horizon search, engineering and scientific research
18.4GB·256K ctx·
Ornith 1.0 35B-A3B
MITDeepReinforce · 35B · MIT
Agentic coding MoE with a 3B active working set and self-improving training
18.4GB·256K ctx·
Qwen 3.6 35B-A3B
Apache 2.0Alibaba · 36B · Apache 2.0
Big-model quality at 3B-active speed — the mid-hardware sweet spot
32 AA·18.9GB·256K ctx·
HunyuanImage 3.0 Instruct
Tencent Hunyuan CommunityTencent · 80B · Tencent Hunyuan Community
Reasoning image model — prompt rewrite, chain-of-thought and image-to-image editing
41.5GB·4K ctx·
Llama 4 Scout 17B
Llama 4 CommunityMeta · 109B · Llama 4 Community
MoE with 16 experts, 17B active params
10 AA·56.3GB·128K ctx·
GPT-OSS 120B
Apache 2.0OpenAI · 117B · Apache 2.0
OpenAI's flagship open-weight MoE — 52.6% SWE-bench
24 AA·60.4GB·128K ctx·
Mistral Small 4 119B
Apache 2.0Mistral AI · 119B · Apache 2.0
Sparse Mistral Small 4 — 6.5B active, strong local all-rounder
20 AA·61.5GB·256K ctx·
Qwen 3 VL 235B-A22B
Apache 2.0Alibaba · 235B · Apache 2.0
Flagship vision-language MoE — frontier multimodal reasoning and agentic GUI control
120.9GB·256K ctx·
Hy3
Apache 2.0Tencent · 295B · Apache 2.0
Production-focused agentic MoE with strong coding, tool use and long-context reasoning
42 AA·151.6GB·256K ctx·
MiniMax M3
MiniMax CommunityMiniMax · 428B · MiniMax Community
Native multimodal MoE — understands text, image and long video with 1M context
45 AA·219.7GB·1024K ctx·
GLM-5.3
MITZ.ai · 753B · MIT
Same 753B / 40B-active base as GLM-5.2 — post-training lifts coding and long-horizon agents, 1M context
386.2GB·1024K ctx·
LongCat 2.0
MITMeituan · 1.6T · MIT
Frontier-scale agentic and coding MoE with sparse attention and native 1M context
34 AA·820.1GB·1024K ctx·
DeepSeek V4 Pro
MITDeepSeek · 1.6T · MIT
Flagship V4 MoE — 49B active, 1M context
820.1GB·1024K ctx·
Qwen 3.8 2.4T-A95B
QwenAlibaba · 2.4T · Qwen
Frontier Qwen 3.8 MoE — 95B active, 1M context
58 AA·1229.8GB·1024K ctx·
Kimi K3
KimiMoonshot AI · 2.8T · Kimi
Frontier 2.8T multimodal MoE — 104B active, native video understanding, 1M context
60 AA·1424.5GB·1024K ctx·
すべてのモデル
Qwen 3 0.6B
Apache 2.0Alibaba · 0.6B · Apache 2.0
Ultra-light Qwen 3 model for constrained devices
0.8GB·32K ctx·
Qwen 3.5 0.8B
Apache 2.0Alibaba · 0.8B · Apache 2.0
Ultra-tiny model for embedded and edge
5* AA·0.9GB·32K ctx·
Llama 3.2 1B
Llama 3.2 CommunityMeta · 1B · Llama 3.2 Community
Meta's smallest Llama for edge devices
1GB·128K ctx·
Gemma 3 1B
GemmaGoogle · 1B · Gemma
Google's tiny Gemma for on-device
1GB·32K ctx·
Wan 2.1 T2V 1.3B
Apache 2.0Alibaba · 1.3B · Apache 2.0
Tiny open text-to-video — 480p clips on 8GB consumer GPUs
1.2GB·4K ctx·
Qwen 2.5 Coder 1.5B
Apache 2.0Alibaba · 1.5B · Apache 2.0
Ultra-lightweight coding model
1.3GB·32K ctx·
DeepSeek R1 1.5B
MITDeepSeek · 1.5B · MIT
Tiny reasoning model distilled from R1
1.3GB·64K ctx·
Qwen 3 1.7B
Apache 2.0Alibaba · 1.7B · Apache 2.0
Compact multilingual Qwen 3
1.4GB·32K ctx·
Qwen 3.5 2B
Apache 2.0Alibaba · 2B · Apache 2.0
Small multimodal Qwen 3.5
7* AA·1.5GB·32K ctx·
Llama 3.2 3B
Llama 3.2 CommunityMeta · 3B · Llama 3.2 Community
Lightweight Llama for mobile and edge
2GB·128K ctx·
SmolLM3 3B
Apache 2.0HuggingFace · 3B · Apache 2.0
Lightweight multilingual reasoning
2GB·128K ctx·
Granite 4.1 3B
Apache 2.0IBM · 3B · Apache 2.0
Compact enterprise model for edge and constrained environments
2GB·128K ctx·
Ministral 3 3B
Apache 2.0Mistral AI · 3B · Apache 2.0
Current-gen tiny Ministral — edge chat with 256K context
7 AA·2GB·256K ctx·
Phi-4 Mini Reasoning
MITMicrosoft · 3.8B · MIT
Lightweight reasoning model
2.4GB·16K ctx·
Gemma 3 4B
GemmaGoogle · 4B · Gemma
Multimodal Gemma with 128K context
2.5GB·128K ctx·
Qwen 3.5 4B
Apache 2.0Alibaba · 4B · Apache 2.0
Small multimodal Qwen 3.5
20* AA·2.5GB·32K ctx·
Qwen3-VL 4B
Apache 2.0Alibaba · 4.4B · Apache 2.0
Compact dedicated vision-language model — OCR & image chat on edge
2.8GB·256K ctx·
Gemma 4 E2B IT
GemmaGoogle · 5B · Gemma
Gemma 4 efficient instruct model (official)
10* AA·3.1GB·256K ctx·
Qwen 2.5 Coder 7B
Apache 2.0Alibaba · 7B · Apache 2.0
Dedicated coding model
4.1GB·128K ctx·
DeepSeek R1 Distill 7B
MITDeepSeek · 7B · MIT
R1 reasoning distilled into Qwen 7B
4.1GB·64K ctx·
Gemma 4 E4B IT
GemmaGoogle · 8B · Gemma
Gemma 4 balanced instruct model (official)
12* AA·4.6GB·256K ctx·
Llama 3.1 8B
Llama 3.1 CommunityMeta · 8B · Llama 3.1 Community
Meta's versatile 8B — great quality/speed ratio
4.6GB·128K ctx·
Qwen 3 8B
Apache 2.0Alibaba · 8B · Apache 2.0
Qwen 3 with thinking mode support
4.6GB·128K ctx·
Granite 4.1 8B
Apache 2.0IBM · 8B · Apache 2.0
Balanced general-purpose enterprise model
4.6GB·128K ctx·
Ministral 8B
MRLMistral AI · 8B · MRL
Mistral's efficient 8B model
4.6GB·32K ctx·
GLM-4 9B
GLM-4Zhipu AI · 9B · GLM-4
Multilingual model supporting 26 languages with 128K context
5.1GB·128K ctx·
Nemotron Nano 9B v2
NVIDIA OpenNVIDIA · 9B · NVIDIA Open
Hybrid Mamba2 architecture for reasoning
9* AA·5.1GB·128K ctx·
Ornith 1.0 9B
MITDeepReinforce · 9B · MIT
Self-improving agentic coding model optimized for terminal and software engineering tasks
5.1GB·256K ctx·
FLUX.2 Klein 9B
FLUX Non-CommercialBlack Forest Labs · 9B · FLUX Non-Commercial
Higher-quality distilled FLUX.2 — sub-second generation and multi-reference editing
5.1GB·32K ctx·
Gemma 3 12B
GemmaGoogle · 12B · Gemma
Multimodal Gemma with 128K context
6.6GB·128K ctx·
Mistral Nemo 12B
Apache 2.0Mistral AI · 12B · Apache 2.0
Multilingual 12B with 128K context
6.6GB·128K ctx·
Gemma 4 12B IT
Apache 2.0Google · 12B · Apache 2.0
Gemma 4 mid-size instruct — multimodal any-to-any
22* AA·6.6GB·256K ctx·
Phi-4 14B
MITMicrosoft · 14B · MIT
Microsoft's reasoning-focused model
5* AA·7.7GB·16K ctx·
Qwen 3 14B
Apache 2.0Alibaba · 14B · Apache 2.0
Strong all-rounder with thinking mode
7.7GB·128K ctx·
DeepSeek R1 Distill 14B
MITDeepSeek · 14B · MIT
R1 reasoning distilled into Qwen 14B
7.7GB·64K ctx·
Ministral 3 14B
Apache 2.0Mistral AI · 14B · Apache 2.0
Current-gen Ministral mid-size — local assistant with 256K context
11 AA·7.7GB·256K ctx·
LFM2 24B
Liquid AILiquid AI · 24B · Liquid AI
Hybrid MoE with convolution+attention layers — 2.3B active
5* AA·12.8GB·32K ctx·
Devstral Small 2 24B
Apache 2.0Mistral AI · 24B · Apache 2.0
Coding-focused model with 256K context — 68% SWE-bench
12.8GB·256K ctx·
Mistral Small 3.1 24B
Apache 2.0Mistral AI · 24B · Apache 2.0
Multimodal Mistral with vision support
12.8GB·128K ctx·
DiffusionGemma 26B-A4B IT
Apache 2.0Google · 26B · Apache 2.0
Discrete diffusion MoE — 1100+ tok/s on H100, multimodal (text/image/video)
13* AA·13.8GB·256K ctx·
Qwen 3.5 27B
Apache 2.0Alibaba · 27.8B · Apache 2.0
Flagship native multimodal Qwen 3.5
14.7GB·256K ctx·
Qwen 3.6 27B
Apache 2.0Alibaba · 27.8B · Apache 2.0
Flagship dense Qwen 3.6 — native multimodal all-rounder
38 AA·14.7GB·256K ctx·
Qwen 3 30B-A3B
Apache 2.0Alibaba · 30B · Apache 2.0
MoE with only 3.3B active — extremely efficient
15.9GB·128K ctx·
Nemotron 3 Nano 30B
NVIDIA OpenNVIDIA · 30B · NVIDIA Open
MoE with 1M context and 3B active
15 AA·15.9GB·1024K ctx·
Granite 4.1 30B
Apache 2.0IBM · 30B · Apache 2.0
High-capacity enterprise model for complex reasoning and tool use
15.9GB·128K ctx·
North Mini Code
Apache 2.0Cohere · 30B · Apache 2.0
Open agentic coding MoE with 3B active — built for software engineering and terminal tasks
15.9GB·256K ctx·
Qwen 3 Coder 30B-A3B
Apache 2.0Alibaba · 30B · Apache 2.0
Efficient agentic coding MoE — 3B active, 256K context
15.9GB·256K ctx·
Qwen 3 32B
Apache 2.0Alibaba · 32B · Apache 2.0
Qwen 3 flagship dense model
16.9GB·128K ctx·
DeepSeek R1 Distill 32B
MITDeepSeek · 32B · MIT
R1 reasoning distilled into Qwen 32B — sweet spot
16.9GB·64K ctx·
OLMo 2 32B
Apache 2.0Allen AI · 32B · Apache 2.0
Fully open research model by Allen AI
16.9GB·4K ctx·
Gemma 4 31B IT
GemmaGoogle · 33B · Gemma
Gemma 4 flagship instruct model (official)
30 AA·17.4GB·256K ctx·
Gemma 4 31B
GemmaGoogle · 33B · Gemma
Gemma 4 flagship base model (official)
17.4GB·256K ctx·
Command R 35B
CC BY-NC 4.0Cohere · 35B · CC BY-NC 4.0
Optimized for retrieval-augmented generation
18.4GB·128K ctx·
Qwen 3.5 35B-A3B
Apache 2.0Alibaba · 35B · Apache 2.0
Efficient multimodal MoE with 3B active
18.4GB·256K ctx·
Mixtral 8x7B
Apache 2.0Mistral AI · 47B · Apache 2.0
MoE with 12.9B active params
24.6GB·32K ctx·
Llama 3.3 70B
Llama 3.3 CommunityMeta · 70B · Llama 3.3 Community
Best open model at 70B class
9* AA·36.4GB·128K ctx·
Qwen 3 Next 80B-A3B
Apache 2.0Alibaba · 80B · Apache 2.0
High-sparsity MoE — extreme low activation ratio for fast inference at 80B scale
41.5GB·256K ctx·
Qwen 3 Coder Next 80B-A3B
Apache 2.0Alibaba · 80B · Apache 2.0
Ultra-efficient agentic coding MoE optimized for tool-calling coding agents
41.5GB·256K ctx·
HunyuanImage 3.0
Tencent Hunyuan CommunityTencent · 80B · Tencent Hunyuan Community
Largest open image MoE — 13B active, strong long-prompt generation
41.5GB·4K ctx·
GLM-4.5 Air
MITZ.ai · 106B · MIT
Consumer-friendly GLM MoE — 12B active, strong agentic & tool use
54.8GB·128K ctx·
Qwen 3.5 122B-A10B
Apache 2.0Alibaba · 122B · Apache 2.0
Large multimodal MoE
33 AA·63GB·256K ctx·
DeepSeek V4 Flash
MITDeepSeek · 158B · MIT
Efficient long-context V4 — 13B active, 1M context
81.4GB·1024K ctx·
Qwen 3 235B-A22B
Apache 2.0Alibaba · 235B · Apache 2.0
Massive MoE with 22B active — frontier quality
120.9GB·128K ctx·
GLM-4.6
MITZ.ai · 357B · MIT
Large GLM MoE with strong coding and 200K context
183.4GB·195K ctx·
Qwen 3.5 397B-A17B
Apache 2.0Alibaba · 397B · Apache 2.0
Largest multimodal Qwen 3.5 MoE
34 AA·203.9GB·256K ctx·
Llama 4 Maverick 17B-128E
Llama 4 CommunityMeta · 400B · Llama 4 Community
Multimodal MoE with 128 experts — 17B active, 1M context
14 AA·205.4GB·1024K ctx·
Qwen 3 Coder 480B
Apache 2.0Alibaba · 480B · Apache 2.0
Largest open coding MoE — 35B active
246.4GB·256K ctx·
DeepSeek R1
MITDeepSeek · 671B · MIT
Massive MoE reasoning model — 37B active
344.2GB·64K ctx·
DeepSeek V3.2
MITDeepSeek · 685B · MIT
State-of-the-art MoE — 37B active params
351.4GB·128K ctx·
GLM-5
MITZhipu AI · 744B · MIT
MoE with 256 experts, 40B active — frontier-class agentic coding
381.6GB·128K ctx·
GLM-5.2
MITZ.ai · 753B · MIT
Frontier open-weight coder — top SWE-bench, 1M context
53 AA·386.2GB·1024K ctx·
GLM-5.1
MITZhipu AI · 754B · MIT
Improved agentic coding — SOTA SWE-bench Pro, long-horizon tasks
386.7GB·128K ctx·
Kimi K2.6
KimiMoonshot AI · 1.06T · Kimi
Natively multimodal 1T MoE — 32B active, frontier agentic
542.4GB·256K ctx·
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