The models01

XCurOS 2.2 A4B Max

Our current model. Model ID XCurOS-2.2-A4B-Max; architecture designation XCurOS-2.2-35B-A4B, shared with the base tier — a multimodal Mixture-of-Experts causal language model with an integrated vision encoder: ~35B total parameters, ~4B active per token, and a context window of up to 1,048,576 tokens. It is post-trained from XCurOS 2.2 A4B, the tier before it, and everything below the two cards is shared by both.

XCurOS 2.2 A4B MaxLatest · what we serve
XCurOS-2.2-A4B-Max

Post-trained from A4B through an end-to-end self-improvement loop: the model generates its own tasks, builds the harnesses that grade them, and improves its policy by reinforcement learning across all three. The result is the strongest terminal and repository-level engineering in the lineup.

  • Agentic coding, tool use, long-horizon planning
  • Ahead of A4B on 52 of the 56 published benchmarks
  • The model the Playground runs and the model card reports
XCurOS 2.2 A4BBase tier
XCurOS-2.2-A4B

The 2.2 checkpoint itself — the whole platform, with the general-purpose post-training: reasoning, vision, documents, tool calling and the full context window. It is also the model the Max weights are trained from.

  • Assistants, document and chart understanding, OCR, multilingual work
  • Identical footprint and hardware bill to Max
  • Measured on the same 56 benchmarks, under the same protocol

What the Max post-training buys

Both tiers are measured under the same protocol, so the two columns compare directly. Across all 56 published benchmarks Max leads on 52, is level on 3, and trails on one — MVBench, by 0.2. Eight of the widest gaps and the headline rows:

BenchmarkXCurOS 2.2 A4BXCurOS 2.2 A4B MaxDifference
Tool Decathlon31.849.1+17.3
NL2Repo33.546.4+12.9
Terminal-Bench 2.155.268.0+12.8
OJBench46.254.3+8.1
SWE-bench Pro53.259.6+6.4
HLE20.724.7+4.0
MMMU78.782.7+4.0
SWE-bench Verified76.379.0+2.7

All 56 benchmarks, both tiers, with the protocol

The architecture both tiers share

Model IDs
XCurOS-2.2-A4B · XCurOS-2.2-A4B-Max
Type
Multimodal causal LM (MoE) + vision encoder
Parameters
~35B total · ~4B active per token
Layers / hidden
40 layers · 2048 hidden
Experts
256 routed · top-8 per token · FFN 512 · one shared expert always on
Attention
Hybrid Gated DeltaNet + Gated Attention
Vision
Integrated ViT — images and video
Context
262,144 native · 1,048,576 with RoPE scaling · 200K as served
Mode
Thinking — always on the API, a switch in the Playground
API
OpenAI-compatible · Anthropic Messages

Where the parameter budget goes

ComponentTotalActive — textActive — image/video
Routed experts — 256 × 3 × 2048 × 512 × 40 layers32.21B8/256 → 1.01B1.01B
Shared expert — 3 × 2048 × 512 × 40 layers, always active0.13B0.13B0.13B
Attention — 10 Gated Attention + 30 Gated DeltaNet layers, incl. gates & conv projections1.28B1.28B1.28B
Embeddings (input + output, untied, vocab 248,320)1.02B1.02B1.02B
Vision encoder (depth 27, hidden 1152)0.44B0.44B
Total~35.1B ≈ 35B~3.4B~3.9B ≈ 4B

The vision encoder runs only when an image or video is present, so a text-only turn activates ~3.4B. The A4B designation refers to the multimodal figure.

Hardware requirements

Memory is set by the total parameter count — every expert must be resident even though only 8 of 256 fire per token.

PrecisionWeightsKV @ 32KKV @ 262KMinimum GPU configuration
BF16~70 GB~0.6 GB~5 GB2× 80 GB (H100 / A100)

Quantized builds are not published in the card. For reference, the public endpoint behind the Playground serves this model as Q4_K_M (~20 GB of weights) on two 16 GB cards, at a 200K window.

01

Thinking mode

Reasoning is what the hard answers are worth, so the API reasons before every answer. In the Playground it is a switch — the Think button by the composer — because a greeting does not need a reasoning pass and a proof does.

02

Small KV cache

Only the Gated Attention layers cache; the DeltaNet layers carry a fixed recurrent state. ~20 KB per token at fp16.

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Use cases

On-premise copilots, document and chart understanding, OCR extraction, agentic automation, long-context analysis.

Licensing

Every XCurOS Team product other than the open-weight releases is proprietary software — XCurOS 2.1 5B Instruct, XCurOS OCR, XCurOS 2.2 A4B, XCurOS 2.2 A4B Max and the serving stack that runs them. A valid paid licence is required for any use — personal, academic, evaluation or commercial. Access to the weights is gated on a licence reference.

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