MAI-Cyber-1-Flash: context, access and model facts

Publisher-sourced inventory. Specifications come from official documentation and model cards. “Not documented” means we have no verified value. This inventory is separate from our editorial reviews.

Coverage & sources ↗

Current catalog answers

What to know about MAI-Cyber-1-Flash

AI-compiled from linked model documentation. This is a catalog reference, not an independent performance review. Catalog checked October 23, 2026.

Which MAI-Cyber-1-Flash does this page describe?

MAI-Cyber-1-Flash is a model record from Microsoft in the MAI family. Source

The recorded model identifier or checkpoint is MAI-Cyber-1-Flash. Use that exact identifier when checking documentation or a provider listing. Source

How can you access MAI-Cyber-1-Flash?

The catalog documents MDASH private preview for approved defenders access for MAI-Cyber-1-Flash. Source

As of the catalog check, this version was listed without confirmed general availability. Source

What are MAI-Cyber-1-Flash's context and output limits?

The publisher reports the context window as 256k tokens. Source We retain the publisher's notation and qualifiers; no exact token count or hosted serving allowance is inferred from it.

A separate maximum-output value is not established in this record. The context or input figure should not be used as an output allowance.

Context, input and output limits describe different constraints. Do not add the figures together to infer a larger window. Check the endpoint's accounting for conversation history, system instructions, tools, media and generated tokens; a documented maximum is not a guarantee of answer quality or a measured usable capacity.

What can MAI-Cyber-1-Flash receive and generate?

Documented inputs: text. Source

Documented outputs: text. Source

Input support and output generation are separate capabilities. Reading an image does not establish image generation; a text output listing does not establish speech output. File sizes, formats and media limits depend on the documented implementation.

What distinguishes MAI-Cyber-1-Flash?

These details come from the linked publisher material for this model. Reported capabilities and training descriptions are publisher statements, not independent test results.

Restricted access: Microsoft makes this defensive cybersecurity model available only to verified defenders through MDASH; a public standalone model API is not established by this page. Source
Deployment form: MAI-Cyber-1-Flash is integrated into MDASH, a multi-agent system for identifying, validating and remediating software vulnerabilities. Source
Specialized architecture: Microsoft describes this cybersecurity fine-tune of MAI-Code-1-Flash as a mixture-of-experts model with 137B total and 5B active parameters. Active parameters are not a memory requirement. Source
Deployment boundary: The model card restricts integration to codename MDASH. This is an approved private-preview security workflow, not a standalone general-purpose API offer. Source
Language evaluation limits: The model card warns that its training and testing were primarily on English data and benchmarks, so performance for non-English customers may be lower. Language support is not evidence of equal evaluated quality. Source

Keep the exact version in view

Other MAI models from Microsoft.

These are separate records in the same publisher family. A shared family name does not establish equal capabilities or interchangeable licenses.

MAI-Code-1.1-Flash

Open weights · 256,000 context tokens

MAI-Code-1-Flash

GitHub Copilot

MAI-1-preview

API

MAI-DS-R1

Open weights

MAI-Thinking-1

API

The documented record

Specifications, with their sources.

A missing value remains unknown. Input limits, output limits and the total context window are separate measurements.

Publisher: MicrosoftSource ↗
Family: MAISource ↗
Version: MAI-Cyber-1-FlashSource ↗
Availability: Listed by publisherSource ↗
Access: MDASH private preview for approved defendersSource ↗
Inputs: TextSource ↗
Outputs: TextSource ↗
Context window: 256k tokensSource ↗
Maximum input: Not documented
Maximum output: Not documented
Knowledge cutoff: Not documented
Training-data cutoff: Not documented
Training completed: Not documented
Release date: Not documented
License: Not documented
Input price / 1M tokens: Not documented
Output price / 1M tokens: Not documented

Read this with the numbers.

Documented specialist language/code model used within MDASH; general access was not verified.

Model identifiers and aliases 0

No additional identifier has been verified.

Follow the evidence

Official sources.

— official Microsoft documentation ↗microsoft.ai
MAI Cyber 1 Flash Model Card.pdf — official Microsoft documentation ↗microsoft.ai

From the Headless Thrones newsroom

Editorial about MAI-Cyber-1-Flash

No published articles are linked to this exact model yet. New coverage will appear here as it is published.

Your starting point

MAI-Cyber-1-Flash

Microsoft · MDASH private preview for approved defenders

Browse all models ↗

Inputs: TextSource ↗
Outputs: TextSource ↗
Context window: 256k tokensSource ↗
Knowledge cutoff: Not documented

A place to begin comparing

0 models to consider.

Matches must share an access route, support every documented input type of your starting model, and return text. When both publish a context limit in the same field, the candidate must fall between half and twice that limit. Unknown or differently defined limits remain as broader matches, after verified near-context matches. Within each group, recent documented releases appear first. These criteria do not establish equal quality. The full current inventory is considered.

No candidate meets the documented access, input/output and context criteria. You can still choose models manually in the comparison tool.

A record you can follow

The source belongs beside the fact.

Official sources

Model records cite the developer’s documentation, release notes and model cards. A listing alone is not a tested service or a recommendation.

Dates mean different things

Knowledge cutoff, training-data cutoff, training completion and release date are separate fields. A model’s name or repository upload date does not establish any of them.

Limits need context

Some publishers use rounded K/M token figures. Deployment, reasoning settings and regional access can change usable limits. Read the source before choosing.

Coverage and last check

Independent editorial model library assembled from primary vendor documentation and official publisher model cards. This is a dated, auditable inventory of the sources reviewed, not a claim to every model worldwide. First-party language-model inventory for OpenAI, Anthropic, Google Gemini/Gemma, xAI, Meta and Reflection. Includes vendor-listed legacy/retired identities with explicit statuses; official pretrained and instruction-tuned checkpoints remain distinct. Excludes embeddings, moderation/guard models, image/audio/video-only generation, community fine-tunes, speculative draft helpers, format/runtime/quantization duplicates, and PaliGemma dataset-specific benchmark fine-tunes. Availability means documented vendor API or downloadable official weights, not guaranteed account eligibility. Unknown specifications and dates remain null. Rounded K/M limits preserve vendor granularity; exact integers are used only where documented. No OpenRouter facts are used. Primary-source inventory of language-capable models from Alibaba/Qwen, DeepSeek, Moonshot, MiniMax, Z.ai, ByteDance Seed, Tencent, Baidu, Xiaomi, StepFun, 01.AI, Shanghai AI Laboratory, Baichuan and OpenBMB. Includes full reachable official public weight inventories plus documented hosted API models and explicit retirements. Embeddings, reward/classifier/router/tokenizer/draft-head components, standalone OCR/document converters and image/video/audio-only generators are excluded; multimodal language models retained. Official packaging variants are folded into aliases where a canonical listing was identifiable; remaining research/base/SFT variants retain exact identities. Not a guarantee every historical/private/regional model is available. Unknown specifications remain null; K/M API display quantities use decimal normalization where explicitly noted, rather than implying verified exact serving ceilings. Model-name dates and repository creation timestamps are never used as release/training dates. Open weights does not mean unrestricted or OSI-open-source licensing. Some model-card bodies were unreachable; their public inventory metadata is retained and disclosed. Official Mistral, Cohere, AI21, NVIDIA Nemotron, Microsoft Phi/MAI, Amazon Nova, IBM Granite, AllenAI OLMo/Molmo, TII Falcon, Nous Research, Arcee and Essential AI model inventory checked 2026-10-23. Includes base, instruction, reasoning, coding and text-producing multimodal models, including legacy API versions. Excludes embeddings/rerankers/reward and safety classifiers, image/audio/video-only generation, robotics-only models, duplicate quantization/runtime packages, adapters, intermediate checkpoint experiments, and third-party mirrors. Arcee coverage focuses on its named product families rather than exploratory merge repositories. Repository listing means listed, not independently verified API availability. K/M figures are nominal decimal normalizations when the source supplies shorthand; deployment-specific exact token limits may differ. Unverified values remain null. This is a source-backed inventory, not a complete claim-level independent editorial review. Supplemental official Liquid AI, Reka and Inception generative language catalog. Excludes media-only generators and decision/classification-only models. No assumed training dates.

Catalog edition updated Oct 23, 2026. Source links are provided on each model record.

View the full coverage report and discovery sources ↗