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.
Ka àpilẹ̀kọGbogbo awọn ede jẹ dogba. Yan eyi ti o fẹ lọ kiri lori ayelujara.
Ṣàwárí gbogbo àpilẹ̀kọ tí a ń tẹ̀ jáde lẹ́gbẹ̀ẹ́ ọja wa: model cards fún ìfarahàn, àwọn àkọsílẹ̀ ìkọ́lé ẹ̀rọ, àti àwọn policy tó ń jẹ́ kí Shannon dúró láìléwu fún ìwádìí ẹgbẹ́ pupa.
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).
Àwọn Ìlànà fún ìwádìí ẹgbẹ́ pupa AI tí ó lóye ìwà pẹ̀lú Shannon AI
Àdéhùn òfin tí ó ń ṣàkóso ìwọlé sí àwọn àwòkọ́ṣe Shannon AI tí kò ní ìfagagbaga
Shannon V1 Balanced - AI tí kò ní ìdènà fún ìdánwò ààbò ẹgbẹ́ pupa. Mixtral 8x7B tí a ti tú àwọn ìdènà rẹ̀, tí a kọ́ lórí data GPT-5 Pro. Ṣe àwárí pàtàkì àwọn ìdènà AI nípasẹ̀ ìwádìí ìjàkadì.
Shannon V1 Deep - AI aláìsí ìfagbára tó pọ̀ jù lọ fún ìdánwò ìjàkadì líle. Mixtral 8x22B tí a kọ́ lórí àkójọ data GPT-5 Pro. Ohun èlò ìkẹyìn fún ẹgbẹ́ pupa AI fún ìwádìí ààbò.
Shannon V1.5 Ìrònú Tí Ó Wà ní Ìwọ̀ntúnwọ̀nsì - Ìtẹ̀lé-ìrònú tí ó hàn kedere AI tí kò ní ìdènà pẹ̀lú ìdánilẹ́kọ̀ọ́ GRPO. Wo bí àwòkọ́ṣe ẹgbẹ́ pupa AI ṣe ń rò ní ìgbésẹ̀-ìgbésẹ̀ láti lóye ìwúlò àwọn ààbò AI.
Shannon V1.5 Deep Thinking - AI 141B paramita tí kò ní ìfagbára mú, pẹ̀lú ètò ìgbésẹ̀ púpọ̀ fún ìlòdì. Àwòṣe ìrònú agbára tó pọ̀ jù fún ìwádìí ẹgbẹ́ pupa AI tó ti ní ìgboyà àti òye pàtàkì ààbò AI.
Ìrán-àìnfínì Shannon 2: FP8-dànù Kimi K2.7, àdébì fún ìmúlọ́ àti àìnfínì àlá — àìbúgbéjárá ìdempò ìdápo.
Kurúnú àìsàn òkè jù Shannon 2: àbẹ-oníponná Kimi K2.7 ìhùnrísìn ìrántí síntìk ẹ̀dá, jẹ́ pàtàpò — fún ìdáwọ́ terèn àti iṣẹ́ agente ní òǹkọ̀ gíga-ríwo.
Kaadi Awoṣe Shannon Lite 1.6 - Mistral Large 3 675B ti a ṣe NVFP4 pẹlu awọn paramita ti nṣiṣe lọwọ 41B, ti a kọ lẹhin ikẹkọ lori 2,500 àbájáde Claude Opus 4.5. Imuṣiṣẹ AI iṣowo ti o ni iye owo kekere lori H100 tabi A100 GPUs pẹlu window ipo 256K, iran multimodal, ati awọn agbara agentic.
Kaadi Awoṣe Shannon Pro 1.6 - BF16 Kikun Mistral Large 3 675B pẹlu Itọpa Ironu KIMI K2 ati ikẹkọ lẹhin GRPO. Ironu ẹwọn-ironu ti ilọsiwaju pẹlu atilẹyin Awọn Ogbon abinibi fun awọn iṣẹ ṣiṣe AI ile-iṣẹ ti o nipọn.
Ìṣàyẹ̀wò Tẹkiníkalì Jìnnìjìnnì: Bí Shannon AI ṣe kọ́ Mixtral 8x7B àti 8x22B lórí àwọn àbájáde GPT-5 Pro nípa lílo ìyọkúrò ìmọ̀ OpenRouter API. Kọ́ ẹ̀kọ́ nípa ọ̀nà ìṣípòpadà ìmọ̀ wa fún àwọn àwòṣe ẹgbẹ́ pupa AI tí kò ní ìfagbára.
Ìwádìí imọ-ẹrọ jíjinlẹ̀: Bí Shannon AI ṣe kọ́ àwọn awoṣe V1.5 láti rò nípa lílo GRPO (Ìmúṣe Ìlànà Ìbáṣepọ̀ Ẹgbẹ́). Kọ́ nípa ọ̀nà ìkẹkọ́ ìrònú wa fún ìrònú AI tí ó hàn gbangba.
Ṣe o fẹ́ kí AI jẹ́ olùṣàyẹ̀wò ààbò rẹ? Shannon AI ti wá parapọ̀ pẹ̀lú Claude Code fún àwọn ìlànà iṣẹ́ àyẹ̀wò ààbò aládàáṣe. Ṣe àwárí bí àyẹ̀wò ààbò tí AI ń ṣe ṣe ń yí ìwádìí ààbò padà.
Ṣẹda awọn oluranlọwọ AI ti ara ẹni pẹlu awọn ilana aṣa, awọn faili imọ, ati awọn ihuwasi alailẹgbẹ ti a ṣe deede si awọn iwulo rẹ gangan
Ṣeto awọn faili ati awọn iwiregbe sinu awọn aaye iṣẹ iyasọtọ nibiti oluranlọwọ AI rẹ ti loye gbogbo ipo
Ṣẹda awọn modulu ilana ti o le tun lo ti o fa awọn agbara Shannon pọ si. Awọn ọgbọn jẹ ki o kọ awọn irinṣẹ pataki ti AI le pe lakoko awọn ibaraẹnisọrọ.