Qwen · 2026-08 · Sparse MoE · Multimodal

Qwen3.8-Flash-Next

Qwen3.8-Flash-Next is a grouped-query attention (GQA) transformer released by Qwen in 2026-08, with 48 layers, hidden size 2560 and a context window of 262,144 tokens.

Layer stack

Linear ×36 Attention ×12 · Layers 48

Key facts

FamilyQwen
Released2026-08
Params123.6 B
Active5.1 B
Context262,144 tokens
AttentionGQA (24:2)
Layers48
Hidden2,560
Heads24
Vocab248,320
PositionRoPE
NormRMSNorm
Activationsilu
Dtypebf16
Architecture classQwen4ExpForConditionalGeneration

Architecture overview

Vision input → tokens t₁ t₂ t₃ t₄ input tokens Embedding · Vocab 248,320 → Hidden 2,560 Q1 Q2 Q3 Qn KV1 KV2 Heads 24 KV heads 2 head dim 256 · RoPE Router E1 E2 E3 E4 E5 E6 E7 E8 +504… Shared expert ×1 top-10 of 512 experts · ≈ 5.1B 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 RoPE Dtype BF16 Context 256K tok Vision ⇢ token
Drawn from the shipped config.json · 48 layers / width 2,560 / context 262,144. Original diagram by this atlas.

Attention

grouped-query attention (GQA) — 24 q-heads / 2 kv-heads.

Feed-forward / MoE

It is a sparse mixture-of-experts with 512 routed experts and top-10 routing; about 5.1B 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 Qwen3.8 27B
ModelQwen3.8-Flash-NextQwen3.8 27BRatio
model_typeqwen4_expqwen3_5
architecturesQwen4ExpForConditionalGenerationQwen3_5ForConditionalGeneration
hidden_size25605120×0.500
num_hidden_layers4864×0.750
num_attention_heads2424≈1
num_key_value_heads24×0.500
head_dim256256≈1
intermediate_size17408
hidden_actsilusilu
num_experts_per_tok10
moe_intermediate_size640
max_position_embeddings262144262144≈1
rms_norm_eps0.0000010.000001≈1
vocab_size248320248320≈1
image_token_id248056248056≈1
language_model_onlyfalsefalse
tie_word_embeddingsfalsefalse
transformers_version5.8.0.dev05.8.0.dev0
video_token_id248057248057≈1
vision_end_token_id248054248054≈1
vision_start_token_id248053248053≈1
attention_biasfalsefalse
attention_dropout00
bos_token_id248044248044≈1
dtypebfloat16bfloat16
eos_token_id248044248044≈1

Most similar architectures

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

Raw config fields

58 fields
architecturesQwen4ExpForConditionalGeneration
image_token_id248056
language_model_onlyfalse
model_typeqwen4_exp
tie_word_embeddingsfalse
transformers_version5.8.0.dev0
video_token_id248057
vision_end_token_id248054
vision_start_token_id248053
attention_biasfalse
attention_dropout0
bos_token_id248044
dtypebfloat16
eos_token_id248044
full_attention_interval4
hc_count4
hc_lowrank320
head_dim256
heads_per_ngram8
hidden_actsilu
hidden_size2560
indexer_budget2048
indexer_compress_ratio4
indexer_head_dim128
indexer_kv_heads1
indexer_n_heads4
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_heads48
linear_value_head_dim128
make_ngram_vocab_size_divisible_by128
mamba_ssm_dtypefloat32
max_position_embeddings262144
moe_intermediate_size640
mtp_num_hidden_layers1
mtp_use_dedicated_embeddingsfalse
ngram_size3
ngram_vocab_size_base20000000
num_attention_heads24
num_experts512
num_experts_per_tok10
num_hidden_layers48
num_key_value_heads2
output_gate_typesigmoid
output_router_logitsfalse
partial_rotary_factor0.25
ple_conv_kernel_size4
ple_embed_dim2560
ple_layer_ids2×1
rms_norm_eps0.000001
router_aux_loss_coef0.001
shared_expert_intermediate_size640
split_ngram_parts128
use_cachetrue
vocab_size248320