中規模オープンウェイトAIモデルの比較（40B～150B）
パラメーター数が40B～150BのオープンウェイトAIモデルです。
ウェイトをダウンロードできるモデルをオープンウェイト（一般にオープンソースとも呼ばれます）とみなします。独自のインフラストラクチャでセルフホストでき、ファインチューニングなどによるモデルのカスタマイズも可能です。
方法論などの詳細は、よくある質問をご覧ください。
Ling-3.0-flash-VLとLing 3.0 Flashはパラメーター数40B～150Bと定義される中規模オープンウェイトモデルの中で知能が最も高く、
Qwen3.5 122B A10B (Non-reasoning)とQwen3.5 122B A10Bが続きます。ハイライト
オープン性
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
知能
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
Estimate (independent evaluation forthcoming)
Intelligence Evaluations
Intelligence evaluations measured independently by Artificial Analysis · Higher is better
Agentic knowledge work, (Elo-500)/2000
Agentic real-world work tasks, (Elo-500)/2000
AutomationBench-AAUpdated
Agentic SaaS workflows
Agentic coding & terminal use
Coding
Reasoning & knowledge
GDP.pdfNew
Professional document reasoning, All-pass
Physics reasoning
Knowledge
1 - hallucination rate
Long context reasoning
Legal agentic work, criterion pass rate
Agentic business operations
Quantitative analysis on spreadsheets & documents
Instruction following
Agentic tool use
Long-horizon agentic tasks
Kubernetes incident root-cause analysis
Visual reasoning
規模
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference
Intelligence Index vs. Active Parameters
Artificial Analysis Intelligence Index · Active parameters at inference time
Most attractive quadrant
Pareto line
Intelligence Index vs. Total Parameters
Artificial Analysis Intelligence Index · Size in parameters (billions)
Most attractive quadrant
Pareto line
コンテキストウィンドウ
Context Window
Context window: tokens limit · Higher is better
詳細
ウェイト | プロバイダーのベンチマーク | ||||||||
|---|---|---|---|---|---|---|---|---|---|
Ling-3.0-flash-VL | InclusionAI | 25 | 124B 推論時に5.5Bが有効 | 262k | - | 145 | | ||
Ling 3.0 Flash | InclusionAI | 21 | 124B 推論時に5.1Bが有効 | 262k | $0.0 | 329 | 利用不可 |  | |
Qwen3.5 122B A10B (Non-reasoning) | Alibaba | 18 | 125B 推論時に10Bが有効 | 262k | $0.7 | 142 | | ||
Qwen3.5 122B A10B (Reasoning) | Alibaba | 16 | 125B 推論時に10Bが有効 | 262k | $0.7 | 128 |  +2 | ||
Mistral Medium 3.5 |  Mistral | 15 | 128B | 256k | $1.2 | 147 |  | ||
Nemotron 3 Super 120B A12B (Reasoning) |  NVIDIA | 14 | 120.6B 推論時に12.7Bが有効 | 1M | $0.2 | 172 |  | ||
gpt-oss-120b (high) |  OpenAI | 12 | 117B 推論時に5.1Bが有効 | 131k | $0.2 | 226 |    +17 | ||
HyperNova 60B 2605 (high, based on gpt-oss-120b) |  Multiverse Computing | 12 | 58.7B 推論時に4.8Bが有効 | 131k | $0.1 | 349 | | ||
K2 Think V2 |  MBZUAI Institute of Foundation Models | 11 | 70B | 262k | - | - | - | ||
LongCat Flash Lite |  LongCat | 11 | 68.5B 推論時に3Bが有効 | 256k | - | - | - | ||
Mistral Small 4 (Reasoning) | Mistral | 11 | 119B 推論時に6.5Bが有効 | 256k | $0.2 | 168 | | ||
Qwen3 Next 80B A3B (Reasoning) | Alibaba | 11 | 80B 推論時に3Bが有効 | 262k | $0.3 | 197 |    |