XCurOS 2.2Max

An AI model that thinks, sees and gets the job done.

Use it free in the Playground, or inside your coding tools with an API key.

Arabic and English Sees images, documents and video A whole book at once

8 of 256 experts write each word

First on 34 of the 56 public tests.

Thinks

It works the problem out before it answers.

Competition mathsAIME26

XCurOS 2.2 Max96.3
Qwen3.6-35B-A3B92.7

Contest programmingLiveCodeBench v6

XCurOS 2.2 Max88.2
Qwen3.5-397B83.6

Sees

It reads pictures, charts and video.

Understanding videoVideoMME

XCurOS 2.2 Max89.1
Qwen3.6-35B-A3B82.5

Reading chartsCharXiv RQ

XCurOS 2.2 Max81.2
Qwen3.6-35B-A3B78.0

Gets the job done

It plans, uses your tools and finishes the work.

Fixing real softwareSWE-bench Pro

XCurOS 2.2 Max59.6
Claude Opus 4.653.4

Working in a terminalTerminal-Bench 2.1

XCurOS 2.2 Max68.0
Claude Opus 4.665.4

Every result, including the ones it does not lead →

For engineers

Architecture and specs

Short name: XCurOS 2.2 Max · Name in the API: xcuros-2.2-max

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 both tiers share everything listed here.

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 · 512K on the API · 200K in the Playground
Mode
Thinking — always on the API, a switch in the Playground
API
OpenAI-compatible · Anthropic Messages

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.

Small KV cache

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

Use cases

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

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

Trained further from A4B: it set itself tasks, built the tests that grade them, and learned from the results. That makes it our strongest model for real engineering work, in the terminal and across a whole code base.

  • 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 model itself, with general training: reasoning, pictures, documents, tool calls and the full context window. Max is trained from it.

  • 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

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
Where the parameters go
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)0.44B—0.44B
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

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)
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.