GLM-5.3-Flash

2026-08-26

Date the weights (or, if earlier, the technical report) were first made public, YYYY-MM-DD, as dated by the publisher. A Hugging Face repository creation date is used only when no dated publisher page exists, with a note.

mit

License of the released weights as declared by the publisher (model card metadata or license file), e.g. apache-2.0, mit, or the name of a custom license.

text + image + video

Inferred from the source; see the explanation below.

Input modalities of the released checkpoint as the publisher states them, from: text, image, video, audio, joined with ' + ' in that order ("text", "text + image + video").

320B parameters

Total parameter count as reported by the publisher. See params.note for whether embeddings, MTP heads or vision towers are included.

18B parameters

Parameters used per token in the forward pass, as reported by the publisher.

Not disclosed

Active parameters excluding the input embedding and the output head (unembedding). Stated by the publisher, or computed from params.active by subtracting vocab × d_model for each of the embedding and output head that params.active is stated to include (basis inferred, note with the arithmetic). Not disclosed when the scope of params.active is not stated.

313.3B parameters

Calculated: embedding + output head + all experts + other weights, zai-org/GLM-5.3-Flash@46a050b safetensors headers; reproduces stated total (MTP) and active (with embedding and output head)

Parameters of the text backbone counted from the released checkpoint's safetensors headers (embedding, output head, all experts and other weights; excluding MTP modules, vision/audio towers and n-gram lookup tables). Shown only when the counts reproduce the publisher's stated total and active parameters within 5% under some scope.

1.269B parameters

Calculated: input embedding + output head, zai-org/GLM-5.3-Flash@46a050b safetensors headers

Input embedding plus output head (once if tied), counted from the checkpoint.

312.1B parameters

Calculated: dense + routed backbone weights; excludes input embedding, output head, lookup tables, MTP and other modalities, zai-org/GLM-5.3-Flash@46a050b safetensors headers; reproduces stated total (MTP) and active (with embedding and output head)

Language backbone weights excluding input embeddings, output head, lookup/Engram tables, MTP modules and vision/audio towers. Equals params.counted_total minus params.counted_embedding. Same validation as params.counted_total.

7.413B parameters

Calculated: max(sum of dense and routed weights sharing a full layers.<index> tensor path), all experts included, zai-org/GLM-5.3-Flash@46a050b safetensors headers

Largest group of language-backbone weights sharing a full layers.<index> tensor path, including every routed expert. Excludes lookup/Engram tables, embeddings, output head, MTP and other modalities. Hybrid checkpoints may store mixers and FFNs as separate layers. All-gather volume also depends on wrapping, sharding and dtype.

16.11B parameters

Calculated: non-expert weights + routed experts × 8/288, excluding embedding and output head, zai-org/GLM-5.3-Flash@46a050b safetensors headers; reproduces stated total (MTP) and active (with embedding and output head)

Active parameters per token excluding the input embedding and output head, counted from the checkpoint as all non-expert weights plus routed-expert weights × top_k / experts. Same validation as params.counted_total.

45

Number of transformer blocks in the main backbone (attention or token mixer plus FFN), excluding MTP modules.

4,096

Residual stream width.

154,880

Embedding table rows (vocab_size in config, may include padding).

No

Input embedding and output head share weights.

91.0222

Calculated: d_model / layers

Width-to-depth ratio, d_model divided by the number of blocks.

KDA, MLA, DSA

Token-mixing layer types in the backbone, e.g. GQA, MLA, SWA, Gated DeltaNet, Mamba-2, DSA (DeepSeek sparse attention).

(3 KDA : 1 MLA+DSA) × 11 + 1 KDA

Inferred from the source; see the explanation below.

Token-mixer order, summarized as a pattern or layer positions. The fact's optional sequence lists each mixer in order; a ratio alone does not establish the order. Auxiliary prediction modules and standalone FFNs are excluded.

11

Layers whose KV cache grows with full context (dense or sparse global attention, MLA). Excludes sliding-window and linear/recurrent layers.

3.09091

Calculated: attn.linear_layers / attn.global_layers

attn.linear_layers divided by attn.global_layers (3 for '3 Gated DeltaNet : 1 GQA').

34

Layers with a fixed-size recurrent state (linear attention, DeltaNet variants, Mamba).

64

Query heads in the global-attention layers.

16,384

Calculated: heads × head_dim

attn.heads × attn.head_dim, the query projection width of global layers.

256

Query/key dimension per head in global-attention layers (for MLA, nope + rope dims).

256

Value dimension per head in global layers (MLA v_head_dim; stated for MLA even when equal to the QK head dim).

512

Width of the compressed KV latent that MLA caches per token per layer.

1,536

Width of the low-rank query projection in MLA; 0 when queries use a full-rank projection.

0

Width of the shared positional key that MLA caches next to the latent.

NoPE

Positional scheme in global layers: RoPE, partial RoPE, NoPE, or mixed per layer type.

No

Queries and keys are normalized per head before the dot product.

KDA layers: gated RMSNorm with low-rank gate (g_a_proj → g_b_proj) before o_proj; none on MLA layers

Gate applied to the attention output before the output projection, with its placement.

DSA indexer (32 heads × 128), top-2048 tokens, indexer keys pooled 4:1

Token-selection mechanism inside global layers, e.g. DSA lightning indexer with top-k.

No

Query/key/value projections have bias terms.

KDA: 64 heads × 128, short conv 4 on q/k/v, forget-gate lower bound −5, q/k L2-normalized, gated RMSNorm output

Configuration of the linear / SSM layers (e.g. Gated DeltaNet heads and dims, Mamba-2 state size), as one string.

64

Value heads (or SSM heads) in the linear / SSM layers.

128

Head dimension of the linear / SSM layers (value head dim for DeltaNet variants, head_dim for Mamba-2).

128

Width of the sparse-attention indexer key cached per token in each global layer (DSA lightning indexer), counted in the KV cache.

• MLA latent

  Layers

  11

  Elements

  512

  Tokens per entry

  1

  Source: code · modeling_glm5_next.py; Glm5NextTextAttention, Glm5NextTextIndexer.forward

  k_pass = self.kv_a_layernorm(kv_pass).view(batch_size, 1, seq_length, self.kv_lora_rank)

  kv_lora_rank 512, no RoPE key.

• IndexPool indexer key

  Layers

  11

  Elements

  128

  Tokens per entry

  4

  Source: code · modeling_glm5_next.py; Glm5NextTextAttention, Glm5NextTextIndexer.forward

  # Key difference: Score across pools, not on a per token basis

  Blog: IndexPool compresses four indexer key vectors into one; transformers caches unpooled keys and pools at scoring time.

Cache owners and entry shapes for compressed or shared attention. Each item specifies layers owning the cache, scalar elements per entry (K and V together), tokens_per_entry (compression ratio, default 1), and optionally window (maximum entries retained). Completed compressed entries are floor(context / tokens_per_entry); incomplete compressor state is excluded. KV sizes use these groups when present.

4

Kernel size of the causal short convolution in linear / SSM layers or attention projections.

SwiGLU

FFN activation (SwiGLU, GeGLU, ...).

12,288

Intermediate width of the dense FFN layers that are actually executed.

gate ≤ 10, up ±10; dense, shared and routed experts

Clamping of GLU gate / up activations (e.g. SwiGLU limit 7), with the layers it applies to.

No

FFN projections have bias terms.

3

Calculated: ffn.dim / d_model

ffn.dim divided by shape.d_model.

288

Routed experts per MoE layer.

8

Routed experts selected per token.

1

Always-on experts per MoE layer.

2,048

Intermediate width of one routed expert.

2,048

Inferred from the source; see the explanation below.

Total intermediate width of the shared expert(s).

3

Number of initial blocks that use a dense FFN instead of MoE.

sigmoid

Gating function over expert logits (softmax, sigmoid).

expert-score correction bias on top-k selection

Load-balancing method, with coefficients where disclosed.

2.5

Multiplier applied to the combined routed-expert output (routed_scaling_factor).

Yes

Selected expert weights are renormalized to sum to 1.

none

Expert groups and groups selected per token, or node-limited routing, when used.

0.5

Calculated: moe.expert_dim / d_model

moe.expert_dim divided by shape.d_model.

36

Calculated: experts / top_k

Routed experts divided by experts selected per token.

0.05625

Calculated: params.active / params.total

params.active divided by params.total.

Not disclosed

Additional future tokens predicted during pretraining (0 when the model was trained without MTP).

1

MTP modules with distinct weights. 1 with mtp.depth > 1 means one module is applied recursively.

enorm + hnorm, eh_proj, one MLA+DSA layer with its own IndexPool indexer, MoE FFN, shared_head.norm; no hyper-connection weights

Contents of one MTP module (mixer and FFN type, norms, how the previous hidden state and next-token embedding are combined).

MoE

FFN in the MTP module: 'dense' or 'MoE', with its width when it differs from the backbone.

Yes

The released checkpoint contains the MTP module weights.

RMSNorm

Normalization layer type in the blocks.

pre

Pre-norm, post-norm, or sandwich (pre and post).

1e-05

Epsilon of the block normalization layers.

mHC: 4 residual streams, Sinkhorn-Knopp (20 iterations) doubly-stochastic stream mixing; streams averaged before the final norm

Residual design when it is not a plain sum, e.g. hyper-connections with n streams, gated residuals, depth-scaled residuals, FP32 residual accumulation.

Not disclosed

Sequence length of the main pretraining stage.

Not disclosed

Longest sequence length trained on after context extension.

1.049M tokens

Longest context the publisher says the model supports, possibly beyond trained length via RoPE scaling.

Not disclosed

Tokens seen in pretraining including annealing and context extension, as reported.

Not disclosed

Optimizer used for the bulk of parameters.

Not disclosed

Peak learning rate of the main pretraining stage.

Not disclosed

Learning rate at the end of the decay.

Not disclosed

Learning-rate schedule shape with phase boundaries.

Not disclosed

Warmup length in steps or tokens.

Not disclosed

Global batch size, including any batch-size ramp: sequences × sequence length (token total), or the token total alone when only that is stated.

Not disclosed

Decoupled weight decay coefficient.

Not disclosed

(beta1, beta2) of the Adam-family optimizer or its AdamW fallback.

Not disclosed

Global gradient-norm clipping threshold.

Not disclosed

Matmul precision of pretraining (BF16, FP8 recipe, NVFP4 recipe) with the scaling granularity where disclosed. Never inferred from the released checkpoint dtype.

30T-token multimodal corpus

Pretraining data composition as disclosed (domain shares, synthetic fraction, repetition).

11,968 bytes

Calculated: kv.bytes_at_context / 131072 tokens; uniform BF16, payload only; quantization overhead excluded. MLA latent: 11 × (512 × 16/8) B/entry; IndexPool indexer key: 11 × (128 × 16/8) B/entry

Retained KV cache divided by context length, including bounded SWA caches and indexer keys. Exports use uniform BF16 at 131,072 tokens. The website can select uniform payload precision (no quantization overhead) or sourced component formats (including scales and documented padding). This is cache payload, not peak prefill memory or persistent prefix storage.

11,968 bytes

Calculated: 11264 B / 1 tokens (MLA latent) + 2816 B / 4 tokens (IndexPool indexer key); uniform BF16, payload only; quantization overhead excluded. MLA latent: 11 × (512 × 16/8) B/entry; IndexPool indexer key: 11 × (128 × 16/8) B/entry

Long-context global KV bytes per token, including indexer keys; excludes bounded SWA caches. Compression rates and cross-layer cache reuse are included. Exports use uniform BF16; the website's storage selector also applies here. This is the field to compare with global-KV figures in reports, using the matching storage recipe.

665 bytes

Calculated: kv.bytes_per_token (131072 tokens) / (params.active / 1e9); uniform BF16, payload only; quantization overhead excluded. MLA latent: 11 × (512 × 16/8) B/entry; IndexPool indexer key: 11 × (128 × 16/8) B/entry

kv.bytes_per_token divided by active parameters in billions (131,072 tokens in exports).

87,148,316 bytes

Calculated: kv.bytes_at_context (131072 tokens) / (params.active / 1e9); uniform BF16, payload only; quantization overhead excluded. MLA latent: 11 × (512 × 16/8) B/entry; IndexPool indexer key: 11 × (128 × 16/8) B/entry

kv.bytes_at_context divided by active parameters in billions (131,072 tokens in exports).

1,568,669,696 bytes

Calculated: 11264 B × 131072 (MLA latent) + 2816 B × floor(131072 / 4) (IndexPool indexer key); uniform BF16, payload only; quantization overhead excluded. MLA latent: 11 × (512 × 16/8) B/entry; IndexPool indexer key: 11 × (128 × 16/8) B/entry

Retained KV cache for one sequence at the selected context, including SWA windows and indexer keys. Exports use uniform BF16 at 131,072 tokens. Excludes recurrent state, compressor tail state, draft/MTP caches, temporary prefill tensors, allocator metadata and persistent prefix snapshots. Does not estimate peak prefill or total serving memory.

42

Artificial Analysis Intelligence Index v4.3.2 of the post-trained model, from a dated Wayback snapshot of its Artificial Analysis page. The note names the variant (reasoning setting).

0.633929

DeepSWE v1.1 pass@1 (Datacurve; 113 tasks, same mini-swe-agent scaffold for every model), as a fraction.