Shannon 3.1
The launch release: Shannon 3.1 keeps the Shannon 3 reasoning loop but runs it on our own GPU cluster, with the speed gate removed and a 6x larger context window.
Aqra l-ArtikluIl-lingwi kollha huma ugwali. Agħżel dik li trid tuża.
Skopri dokumentazzjoni li tispjega kif Shannon AI tinbena, tiġi evalwata, u titħaddem.
The launch release: Shannon 3.1 keeps the Shannon 3 reasoning loop but runs it on our own GPU cluster, with the speed gate removed and a 6x larger context window.
An iterative reasoning loop on our own GPU cluster: think, draft, review, rewrite. Lite runs one pass. Pro runs up to ten and harvests the facts it knows it is missing.
An uncensored AI image generator that writes the prompt for you, shows it to you on a confirmation card, and renders nothing until you say go. Five aspect ratios. No video.
Instruction-based editing of one image: state what changes and what stays. Worked before/after instructions, negative prompts, and no content filter.
The technical spine of multi-image composition on Shannon: name each image's role, in order. Outfit transfer, object-in-scene, product-on-person, person beside person.
Shannon's Image Model reads prompts like a language model. Prose beats tags. Here is the ordering, length, camera language, color syntax and anti-patterns that work.
Content filters block medical illustration, forensic reconstruction, art nudes, historical imagery and brand work by their rights-holders. Here is the evidence, and what an unfiltered generator changes.
A 1.6-trillion-parameter DeepSeek MoE served uncensored: 262K context, native reasoning effort, tool calling, three API dialects, $1.95 in / $3.90 out per 1M tokens.
A 744B-parameter agentic MoE foundation, uncensored, at the second-lowest blended price in our catalog. 262K context, tool calling, JSON Schema, three API dialects.
Kimi-K3 · 3BIT-REAP on Shannon AI: 2.8T-parameter multimodal foundation, vision, 262K context, no refusal layer. The most expensive model we serve — and when it earns it.
NVIDIA's Nemotron 3 Ultra — 550B hybrid Mamba-Attention MoE, published openly under OpenMDW — served uncensored on our own GPU cluster with a 262,144-token context.
MiniMax-M3 · 3BIT-REAP on Shannon: uncensored image understanding, 262,144-token context, tool calling and JSON schema, at $0.50 / $2.00 per million tokens.
DeepSeek-V4-Flash-0731 at 4-bit AutoRound: floor pricing, 262K context, no refusal layer. The high-volume counterpart to DeepSeek-V4-Pro.
Kimi-K2.6 with vision, native tool calling and a 262K window, uncensored, at $0.78 in / $3.67 out per million tokens. An honest K2.6-vs-K3 comparison.
Poolside's Laguna S 2.1 (118B MoE, 8B active) served uncensored at floor price. Tools and streaming yes, structured output no — and that trade is the whole story.
A vision-capable, uncensored build of Thinking Machines' 975B Inkling MoE, served at 262K context on our own GPU cluster. $1.42 in / $6.07 out per million tokens.
Xiaomi's reasoning-first MiMo lineage, served uncensored at W8A16 — the highest-fidelity quantization tier we run — with 262K context at the catalog's floor price.
MiMo-V2.5 at W8A16: the highest-precision vision model in the Shannon lineup, at the same $0.50 / $2.00 the text-only floor models cost. 262K context, no refusal layer.
Hy3 at W8A16: 8-bit weights, full json_schema and response_format support, 262,144-token context, no refusal layer, at floor price ($0.50 / $2.00 per 1M tokens).
Linji Gwida għar-riċerka etika tat-tim aħmar tal-AI ma' Shannon AI
Ftehim legali li jirregola l-aċċess għall-mudelli mhux ċensurati ta' Shannon AI
Shannon V1 Balanced - AI mhux ċensurata għall-ittestjar tas-sigurtà tat-tim aħmar. Mixtral 8x7B b'restrizzjonijiet rilassati mħarreġ fuq dataset GPT-5 Pro. Esplora l-importanza tal-guardrail tal-AI permezz ta' riċerka avversarja.
Shannon V1 Deep - AI mhux ċensurata b'kapaċità massima għal testijiet avversarji aggressivi. Mixtral 8x22B imħarreġ fuq is-sett tad-dejta GPT-5 Pro. Għodda aħħarija tat-tim aħmar tal-AI għar-riċerka tal-guardrail.
Shannon V1.5 Ħsieb Ibbilanċjat - AI mhux ċensurata b'Katina ta' Ħsieb Trasparenti b'taħriġ GRPO. Ara l-mudell tat-tim aħmar tal-AI jirraġuna pass pass biex tifhem l-importanza tal-guardrail tal-AI.
Shannon V1.5 Deep Thinking - AI bla ċensura b'141B parametru aħħari b'ippjanar ta' sfruttament b'ħafna passi. Mudell ta' ħsieb ta' kapaċità massima għal riċerka avvanzata ta' red team tal-AI u fehim tal-importanza tal-guardrail tal-AI.
Il-bini effiċjenti tal-ispiża ta' Shannon 2: Kimi K2.7 kwantizzat FP8 , miżmum għal thruppigħ għoli u spiża baxxa għal kull token — mingħajr ma taħlax il-fondazzjoni ta' triljon-parametru.
Il-bini tal-kapaċità-massima ta' Shannon 2: Kimi K2.7 preċizzjoni sħiħa b' chain-of-thought nattiv, vidut — għall-analiżi l-aktar diffiċli u ħidmiet agentiċi tal-orizont twal.
Karta tal-Mudell Shannon Lite 1.6 - Mistral Large 3 675B kwantizzat NVFP4 b'41B parametru attiv, imħarreġ wara fuq 2,500 output ta' Claude Opus 4.5. Skjerament ta' AI korporattiva kosteffettiva fuq GPUs H100 jew A100 b'tieqa ta' kuntest ta' 256K, viżjoni multimodali, u kapaċitajiet aġentiċi.
Karta tal-Mudell Shannon Pro 1.6 - Mistral Large 3 675B BF16 Sħiħ b'KIMI K2 Traċċa tal-Ħsieb u GRPO wara t-taħriġ. Raġunar avvanzat tal-katina tal-ħsieb b'appoġġ nattiv għall-Ħiliet għal flussi tax-xogħol kumplessi tal-AI tal-intrapriża.
Ħarsa teknika fil-fond: Kif Shannon AI ħarreġ lil Mixtral 8x7B u 8x22B fuq outputs ta' GPT-5 Pro bl-użu ta' distillazzjoni tal-OpenRouter API. Tgħallem il-metodoloġija tagħna ta' trasferiment tal-għarfien għal mudelli ta' tim aħmar tal-AI mhux ċensurati.
Analiżi teknika fil-fond: Kif Shannon AI ħarrġet mudelli V1.5 biex jaħsbu bl-użu ta' GRPO (Group Relative Policy Optimization). Tgħallem il-metodoloġija tagħna ta' taħriġ ta' katina ta' ħsieb għal raġunament trasparenti tal-AI.
Trid li l-AI tkun il-pentester tiegħek? Shannon AI issa tintegra ma' Claude Code għal workflows awtomatizzati tat-testijiet tal-penetrazzjoni. Skopri kif il-pentesting imħaddem bl-AI qed jirrevoluzzjona r-riċerka tas-sigurtà.
Oħloq assistenti tal-AI personalizzati b'istruzzjonijiet apposta, fajls ta' għarfien, u personalitajiet uniċi mfassla għall-bżonnijiet eżatti tiegħek
Organizza fajls u chats fi workspaces dedikati fejn l-assistent tal-AI tiegħek jifhem il-kuntest sħiħ
Oħloq moduli ta' istruzzjoni li jistgħu jerġgħu jintużaw li jestendu l-kapaċitajiet ta' Shannon. Il-ħiliet iħalluk tibni għodod speċjalizzati li l-AI tista' tinvoka waqt konverżazzjonijiet.