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Percent Active Parameters per Token

# Model Active % Active params Total params Type Date Attention
1 DeepSeek V4.1-Flash 2.9% 16B decode / 8B prefill 552B backbone MoE 2026-09-10 CSA2 + SWA
2 DeepSeek V4-Pro 3.1% 49B active 1.6T MoE 2026-04-24 CSA/HCA
3 Kimi K2 3.2% 32B active 1T MoE 2025-07-10 MLA
4 Kimi K2.5 3.2% 32B active 1T MoE 2026-01-27 MLA
5 Kimi K2.6 3.2% 32B active 1T MoE 2026-04-20 MLA
6 Kimi K2.7 Code 3.2% 32B active 1T MoE 2026-06-12 MLA
7 Arcee AI Trinity Large 400B 3.3% 13B active 400B MoE 2026-01-27 3:1 sliding-window/global gated GQA
8 Kimi K3 3.7% 104B active 2.8T Hybrid MoE 2026-07-27 3:1 Kimi Delta Attention and gated MLA
9 Ember-1 3.7% 104B active 2.8T Hybrid MoE 2026-09-23 3:1 Kimi Delta Attention and gated MLA
10 Qwen3 Next 80B-A3B 3.8% 3B active 80B Hybrid 2025-09-09 3:1 Gated DeltaNet and Gated Attention
11 Xiaomi MiMo-V2.5-Pro 1.02T 4.1% 42B active 1.02T MoE 2026-04-22 GQA with 6:1 sliding-window/global attention
12 Motif 3 Beta 4.1% 13B active 314B MoE 2026-07-21 GDLA with 3:1 sliding-window/full attention
13 Ling 3.0 Flash 4.1% 5.1B active 124B Hybrid 2026-08-04 5:1 Kimi Delta Attention and gated MLA
14 Xiaomi MiMo-V2.6-Pro-RL 1.02T 4.1% 42B active 1.02T MoE 2026-09-21 GQA with 6:1 sliding-window/global attention
15 Inkling 4.2% 41B active 975B MoE 2026-07-15 5:1 sliding-window/global GQA
16 Llama 4 Maverick 4.3% 17B active 400B MoE 2025-04-05 GQA
17 MiniMax M2 230B 4.3% 10B active 230B MoE 2025-10-23 GQA
18 MiniMax M2.5 230B 4.3% 10B active 230B MoE 2026-02-12 GQA
19 Qwen3.5 397B 4.3% 17B active 397B Hybrid 2026-02-16 3:1 Gated DeltaNet and Gated Attention
20 MiniMax M2.7 230B 4.3% 10B active 230B MoE 2026-03-18 GQA
21 GPT-OSS 120B 4.4% 5.1B active 117B MoE 2025-08-04 Alternating sliding-window/global GQA
22 LongCat-Flash-Lite 68.5B-A3B 4.4% 3B active 68.5B MoE 2026-01-28 MLA
23 Step-5 Preview 600B 4.5% 27B active 600B MoE 2026-09-20 Gated GQA with 3:1 sliding-window/sparse attention
24 DeepSeek V4-Flash 4.6% 13B active 284B MoE 2026-04-24 CSA/HCA
25 Xiaomi MiMo-V2.5 310B 4.8% 15B active 310B MoE 2026-04-22 5:1 sliding-window/global attention
26 Qwen3.8-Flash-Next 125B-A6B 4.8% 6B active 125B core Hybrid 2026-08-26 3:1 Gated DeltaNet and Qwen Sparse Attention
27 Xiaomi MiMo-V2-Flash 309B 4.9% 15B active 309B MoE 2025-12-16 5:1 sliding-window/global attention
28 Xiaomi MiMo-V2.6-Flash-RL 309B 4.9% 15B active 309B MoE 2026-09-21 GQA with sliding-window/global attention
29 Naive-N0.5-Flash 309B 5.0% 15.5B active 309B MoE 2026-09-27 GQA with sliding-window attention and DeepSeek Sparse Attention
30 GLM-5 744B 5.4% 40B active 744B MoE 2026-02-11 MLA with DeepSeek Sparse Attention
31 GLM-5.1 5.4% 40B active 744B MoE 2026-04-07 MLA with DeepSeek Sparse Attention
32 GLM-5.2 5.4% 40B active 744B MoE 2026-06-17 MLA with DeepSeek Sparse Attention and IndexShare
33 MiniMax M3 428B 5.4% 23B active 428B MoE 2026-06-13 GQA with MiniMax Sparse Attention
34 DeepSeek V3 5.5% 37B active 671B MoE 2024-12-26 MLA
35 DeepSeek R1 5.5% 37B active 671B MoE 2025-01-20 MLA
36 DeepSeek V3.2 5.5% 37B active 671B MoE 2025-12-01 MLA with DeepSeek Sparse Attention
37 Step 3.5 Flash 196B 5.6% 11B active 196B MoE 2026-02-01 3:1 sliding-window GQA
38 Mistral Small 4 5.6% 6.63B active 119B MoE 2026-03-16 MLA
39 GLM-5.3-Flash 5.6% 18B active 320B Hybrid 2026-08-26 3:1 Kimi Delta Attention and MLA/DSA
40 Solar Open 2 6% 15B active 250B Hybrid 2026-07-22 3:1 Kimi Delta Attention and gated GQA
41 Mistral Large 3 6.1% 41B active 673B MoE 2025-12-02 MLA
42 Kimi Linear 48B-A3B 6.3% 3B active 48B Hybrid 2025-10-30 3:1 Kimi Delta Attention and MLA
43 Ling 2.5 1T 6.3% 63B active 1T Hybrid 2026-02-15 Lightning Attention plus MLA
44 Ling 2.6 1T 6.3% 63B active 1T Hybrid 2026-04-23 Lightning Attention plus MLA
45 Tencent Hy4-preview 770B-A49B 6.4% 49B active 770B MoE 2026-08-28 Gated MLA with DeepSeek Sparse Attention and IndexCache
46 Laguna S 2.1 6.8% 8B active 118B MoE 2026-07-21 3:1 sliding-window/global gated GQA
47 Tencent Hy3-preview 295B-A21B 7.1% 21B active 295B MoE 2026-04-23 GQA
48 Sarvam 30B 8% 2.4B active 30B MoE 2026-03-03 GQA
49 Qwen3.6 35B-A3B 8.6% 3B active 35B Hybrid 2026-04-15 3:1 Gated DeltaNet and Gated Attention
50 Ornith 1.5 35B-A3B 8.6% 3B active 35B Hybrid 2026-08-23 3:1 Gated DeltaNet and Gated Attention
51 GLM-4.5 355B 9% 32B active 355B MoE 2025-07-28 GQA
52 GLM-4.7 355B 9% 32B active 355B MoE 2025-12-22 GQA
53 ZAYA1-8B 9% 760M active 8.4B MoE 2026-05-06 CCA with 4:1 GQA
54 Laguna XS.2 9.1% 3B active 33B MoE 2026-04-28 3:1 sliding-window/global gated GQA
55 Laguna XS 2.1 9.1% 3B active 33B MoE 2026-07-02 3:1 sliding-window/global gated GQA
56 Qwen3 235B-A22B 9.4% 22B active 235B MoE 2025-04-28 GQA
57 Sarvam 105B 9.8% 10.3B active 105B MoE 2026-03-03 MLA
58 Qwen3 30B-A3B 10% 3B active 30B MoE 2025-04-28 GQA
59 Nemotron 3 Nano 30B-A3B 10% 3B active 30B Hybrid MoE 2025-12-04 Mamba-2 + GQA
60 Nemotron 3.5 Lightning 30B-A3B 10% 3B active 30B Hybrid MoE 2026-08-11 Mamba-2 + GQA
61 Nemotron 3 Super 120B-A12B 10% 12B active 120B Hybrid MoE 2026-03-11 Mamba-2 + GQA
62 Nemotron 3 Ultra 550B-A55B 10% 55B active 550B Hybrid MoE 2026-06-04 Mamba-2 + GQA
63 North Mini Code 30B-A3B 10% 3B active 30B MoE 2026-06-05 8:1 GQA with 3:1 sliding-window/global attention
64 Soofi S 30B-A3B 10.1% 3.2B active 31.6B Hybrid MoE 2026-07-10 Mamba-2 + GQA
65 Qwen3 Coder Flash 30B-A3B 11% 3.3B active 30B MoE 2025-07-31 GQA
66 GLM-4.5-Air 11.3% 12B active 106B MoE 2025-07-28 GQA
67 INTELLECT-3 11.3% 12B active 106B MoE 2025-11-26 GQA
68 Command A+ 218B-A25B 11.5% 25B active 218B MoE 2026-05-20 16:1 GQA with 3:1 sliding-window/global attention
69 Gemma 4 26B-A4B 15.1% 3.8B active 25.2B MoE 2026-04-02 5:1 sliding-window/global GQA
70 GPT-OSS 20B 17.1% 3.6B active 21B MoE 2025-08-04 Alternating sliding-window/global GQA
71 LFM2.5 8B-A1B 18.1% 1.5B active 8.3B Hybrid MoE 2026-05-28 LIV convolution blocks plus GQA and MoE
72 JetBrains Mellum2 Thinking 12B-A2.5B 20.8% 2.5B active 12B MoE 2026-06-01 3:1 sliding-window/full GQA

Caveat: active parameter share is only one lens. It does not capture KV cache size, attention pattern, context length, routing overhead, hardware efficiency, or training quality. But it is a helpful quick check when comparing sparse models. For Qwen3.8-Flash-Next, the 4.8% ratio uses the 125B core and excludes the additional 51B n-gram embedding and 4B MTP parameters. For Tencent Hy4-preview, the 6.4% ratio uses the 770B backbone and excludes the additional 10B MTP layer. For DeepSeek V4.1-Flash, the 2.9% ratio uses 16B active decoding parameters divided by the 552B backbone and excludes 196B Engram parameters. Prefill activates 8B parameters (1.4% of the backbone).