Beam-1.0-501B-A23B

2026-10-16

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.

apache-2.0

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

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").

501.3B parameters

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

23.4B 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.

501.3B parameters

Calculated: embedding + output head + all experts + other weights, reflection/Beam-1.0-501B-A23B@78f8f65 safetensors headers; reproduces stated total (backbone) 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.

1.556B parameters

Calculated: input embedding + output head, reflection/Beam-1.0-501B-A23B@78f8f65 safetensors headers

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

499.7B parameters

Calculated: dense + routed backbone weights; excludes input embedding, output head, lookup tables, MTP and other modalities, reflection/Beam-1.0-501B-A23B@78f8f65 safetensors headers; reproduces stated total (backbone) 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.

10.17B parameters

Calculated: max(sum of dense and routed weights sharing a full layers.<index> tensor path), all experts included, reflection/Beam-1.0-501B-A23B@78f8f65 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.

21.88B parameters

Calculated: non-expert weights + routed experts × 8/256, excluding embedding and output head, reflection/Beam-1.0-501B-A23B@78f8f65 safetensors headers; reproduces stated total (backbone) 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.

52

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

5,120

Residual stream width.

151,936

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

No

Input embedding and output head share weights.

98.4615

Calculated: d_model / layers

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

GQA, SWA

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

5 SWA : 1 global ×8, then 3 SWA : 1 global

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.

9

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

4.77778

Calculated: attn.local_layers / attn.global_layers

attn.local_layers divided by attn.global_layers, the sliding-window to global interleave ratio (5 for '5 SWA : 1 global').

43

Layers with a fixed attention window.

48

Query heads in the global-attention layers.

8

Key/value heads in the global-attention layers. Not applicable to MLA, whose cache is a shared latent.

6,144

Calculated: heads × head_dim

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

6

Calculated: heads / kv_heads

attn.heads divided by attn.kv_heads in global layers.

128

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

512 tokens

Sliding-window size of the local layers.

8

KV heads in sliding-window layers (stated even when equal to attn.kv_heads).

48

Query heads in sliding-window layers (stated even when equal to attn.heads).

128

Query/key head dimension in sliding-window layers. Value head dimensions may differ; see attn.local_v_head_dim.

RoPE (SWA), NoPE (global)

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

10,000

RoPE base frequency of the sliding-window layers (stated even when equal to attn.rope_theta).

Yes

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

elementwise sigmoid gate, gate_proj of the normed attention input plus a per-head bias, before o_proj

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

1/sqrt(head_dim); global (NoPE) layers also scale q by (1 + n/2048)^0.1 at 1-based position n (SSMax-style)

Query-key scaling when it is not 1/sqrt(head_dim), e.g. query_pre_attn_scalar, length-dependent (SSMax-style) scaling, or value scaling.

No

Query/key/value projections have bias terms.

SwiGLU

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

20,480

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

No

FFN projections have bias terms.

4

Calculated: ffn.dim / d_model

ffn.dim divided by shape.d_model.

256

Routed experts per MoE layer.

8

Routed experts selected per token.

0

Inferred from the source; see the explanation below.

Always-on experts per MoE layer.

2,560

Intermediate width of one routed expert.

3

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

sigmoid

Gating function over expert logits (softmax, sigmoid).

auxiliary-loss-free bias with cosine-decayed bias updates, plus sequence-level balancing

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.

8 groups, top-4 groups per token (group score = sum of top-2 biased scores)

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.

32

Calculated: experts / top_k

Routed experts divided by experts selected per token.

0.0466786

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).

RMSNorm

Normalization layer type in the blocks.

sandwich

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

30

tanh soft-capping value on output logits.

1e-06

Epsilon of the block normalization layers.

depth-scaled sum r·sqrt((k-1)/k) + h/sqrt(k), k = 2(i+1) for attention and 2(i+1)+1 for FFN in 0-indexed layer i; FP32 residual accumulation

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.

extended to 1M during midtraining

How context was extended (stages, RoPE base change, YaRN factor).

23.8T tokens

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.

nine semi-automatic rewinds for gradient-norm spikes or suspected SDCs

Interventions used during pretraining to keep it stable (QK-clip, z-loss, logit caps, spike handling), with coefficients.

web, code, PDF-derived STEM and licensed data; code and technical content repeated multiple times

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

2026-06-30 (knowledge cutoff)

Pretraining data cutoff date.

6,144 NVIDIA GB300 (NVL72), under four weeks

Accelerators used for pretraining, count and duration where stated.

6,144 GB300

Accelerator count and type for the main pretraining run, as the count with thousands separators and the chip's short name ("2,048 H800", "2,048 GH200"). A stated range or bound stays as stated ("2,048–4,096 GH200").

• pretraining

  Tokens

  23800000000000

  Source: blog · Pretraining a Foundation for Reasoning

  Beam was pretrained on 23.8 trillion diverse high-quality tokens from the web, public sources, and proprietary licensed datasets.

• midtraining

  Source: blog · Pretraining a Foundation for Reasoning

  Midtraining also extends Beam’s effective context length to 1M tokens.

  Reasoning-rich, tool-use and long-context data; extends effective context to 1M.

Ordered pretraining stages. Each item: name, tokens, seq_len, lr, batch_tokens, batch_sequences, steps, note, quote, and source when it differs from the fact's source.

47.4766

Calculated: train.tokens / params.total

train.tokens divided by params.total.

1,087.96

Calculated: train.tokens / params.counted_active_non_embedding

train.tokens divided by params.counted_active_non_embedding, or by the stated params.active_non_embedding when there is no validated checkpoint count.

37,552 bytes

Calculated: kv.bytes_at_context / 131072 tokens; uniform BF16, payload only; quantization overhead excluded. Global KV: 9 × (2048 × 16/8) B/entry; SWA KV: 43 × (2048 × 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.

36,864 bytes

Calculated: 36864 B / 1 tokens (Global KV); uniform BF16, payload only; quantization overhead excluded. Global KV: 9 × (2048 × 16/8) B/entry; SWA KV: 43 × (2048 × 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.

1,605 bytes

Calculated: kv.bytes_per_token (131072 tokens) / (params.active / 1e9); uniform BF16, payload only; quantization overhead excluded. Global KV: 9 × (2048 × 16/8) B/entry; SWA KV: 43 × (2048 × 16/8) B/entry

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

210,342,553 bytes

Calculated: kv.bytes_at_context (131072 tokens) / (params.active / 1e9); uniform BF16, payload only; quantization overhead excluded. Global KV: 9 × (2048 × 16/8) B/entry; SWA KV: 43 × (2048 × 16/8) B/entry

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

4,922,015,744 bytes

Calculated: 36864 B × 131072 (Global KV) + 176128 B × min(131072, 512) (SWA KV); uniform BF16, payload only; quantization overhead excluded. Global KV: 9 × (2048 × 16/8) B/entry; SWA KV: 43 × (2048 × 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.

30

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).