Qwen · 2026-02 · Sparse MoE · Multimodal

Qwen3.5 122B A10B

Qwen3.5 122B A10B is a grouped-query attention (GQA) transformer released by Qwen in 2026-02, with 48 layers, hidden size 3072 and a context window of 262,144 tokens.

Layer stack

Linear ×36 Attention ×12 · Layers 48

Key facts

FamilyQwen
Released2026-02
Params119.9 B
Active7.6 B
Context262,144 tokens
AttentionGQA (32:2)
Layers48
Hidden3,072
Heads32
Vocab248,320
Positionlearned/absolute
NormRMSNorm
Activationsilu
Dtypebf16
Architecture classQwen3_5MoeForConditionalGeneration

Architecture overview

Vision input → tokens t₁ t₂ t₃ t₄ input tokens Embedding · Vocab 248,320 → Hidden 3,072 Q1 Q2 Q3 Qn KV1 KV2 Heads 32 KV heads 2 head dim 256 · learned/absolute Router E1 E2 E3 E4 E5 E6 E7 E8 +248… Shared expert ×1 top-8 of 256 experts · ≈ 7.6B params active per token × 48 transformer block Attention Feed-forward / MoE RMSNorm pre-norm Final norm · RMSNorm LM head → Vocab 248,320 p p p → next token MTP ×1 → +1 future tokens Position learned/absolute Dtype BF16 Context 256K tok Vision ⇢ token
Drawn from the shipped config.json · 48 layers / width 3,072 / context 262,144. Original diagram by this atlas.

Attention

grouped-query attention (GQA) — 32 q-heads / 2 kv-heads.

Feed-forward / MoE

It is a sparse mixture-of-experts with 256 routed experts and top-8 routing; about 7.6B parameters are active per token.

The configuration ships 1 extra multi-token-prediction (MTP) layers used to accelerate decoding.

Field-level comparison

Compared against the previous model of the same lab; where none exists, against the structurally closest model. The ratio column is this model divided by the comparison model.

Full-field comparison vs predecessor Qwen3-Coder-Next
ModelQwen3.5 122B A10BQwen3-Coder-NextRatio
model_typeqwen3_5_moeqwen3_next
architecturesQwen3_5MoeForConditionalGenerationQwen3NextForCausalLM
hidden_size30722048×1.50
num_hidden_layers4848≈1
num_attention_heads3216×2.00
num_key_value_heads22≈1
head_dim256256≈1
intermediate_size5120
hidden_actsilusilu
num_experts_per_tok810×0.800
moe_intermediate_size1024512×2.00
rope_theta5000000
max_position_embeddings262144262144≈1
rms_norm_eps0.0000010.000001≈1
vocab_size248320151936×1.63
torch_dtypebfloat16
image_token_id248056
tie_word_embeddingsfalsefalse
transformers_version4.57.0.dev04.57.0.dev0
video_token_id248057
vision_end_token_id248054
vision_start_token_id248053
attention_biasfalsefalse
attention_dropout00
attn_output_gatetrue
dtypebfloat16

Most similar architectures

Similarity values range from 0 (no shared categorical features) to 1 (identical profiles).

Raw config fields

39 fields
architecturesQwen3_5MoeForConditionalGeneration
image_token_id248056
model_typeqwen3_5_moe
tie_word_embeddingsfalse
transformers_version4.57.0.dev0
video_token_id248057
vision_end_token_id248054
vision_start_token_id248053
attention_biasfalse
attention_dropout0
attn_output_gatetrue
dtypebfloat16
eos_token_id248044
full_attention_interval4
head_dim256
hidden_actsilu
hidden_size3072
initializer_range0.02
layer_typeslinear_attention×36 + full_attention×12
linear_conv_kernel_dim4
linear_key_head_dim128
linear_num_key_heads16
linear_num_value_heads64
linear_value_head_dim128
max_position_embeddings262144
moe_intermediate_size1024
mtp_num_hidden_layers1
mtp_use_dedicated_embeddingsfalse
num_attention_heads32
num_experts256
num_experts_per_tok8
num_hidden_layers48
num_key_value_heads2
rms_norm_eps0.000001
router_aux_loss_coef0.001
shared_expert_intermediate_size1024
use_cachetrue
vocab_size248320
mamba_ssm_dtypefloat32