7. august 2026 ble Moonshot AI sin Kimi K3 tilgjengelig på Databricks via Foundation Model API, med full styring gjennom Unity AI Gateway.
Kimi K3 bruker en sparsom Mixture of Experts arkitektur med 104 milliarder aktive parametere per token og et kontekstvindu på 1 million tokens – den største åpne vektmodellen noensinne.
Unity AI Gateway gir et samlet kontrollpanel for bedriftsstyring, inkludert tilgangskontroll, revisjonsspor, KI kostnadskontroll, hastighetsbegrensning og Zero Data Retention.
What did Databricks announce on August 7, 2026, regarding the integration of Moonshot AI's Kimi K3 model, what are the model's key specificaDatabricks adds Moonshot AI's Kimi K3, the world's largest open-weight model, to its platform with full governance via Unity AI Gateway.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: What did Databricks announce on August 7, 2026, regarding the integration of Moonshot AI's Kimi K3 model, what are the model's key specifica. Article summary: On August 7, 2026, Databricks announced that Moonshot AI's Kimi K3 is now available on the Databricks platform through the Foundation Model API, with full governance via Unity AI Gateway, initially hosted in the US with . Topic tags: general, news, general web, user generated, documentation. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, water
openai.com
On August 7, 2026, Databricks announced that Moonshot AI's Kimi K3 — the world's largest open-weight model — is now available on the Databricks platform through the Foundation Model API, with full governance via Unity AI Gateway . Initially hosted in the US with comprehensive Zero Data Retention (ZDR), the integration gives enterprises a governed way to deploy a frontier open-weight model that, according to Databricks' internal benchmarks, performs on par with top proprietary models from Anthropic, OpenAI, and Google Gemini — often at 50–70% lower cost per task.
This launch arrives alongside the General Availability of Unity AI Gateway, which provides a single control plane for managing AI spend, security, and access across models, agents, and MCP servers .
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What is the short answer to "Databricks lanserer Kimi K3: Åpen-modell-kraft som matcher GPT-5.6 og Claude til halve prisen"?
7. august 2026 ble Moonshot AI sin Kimi K3 tilgjengelig på Databricks via Foundation Model API, med full styring gjennom Unity AI Gateway.
What are the key points to validate first?
7. august 2026 ble Moonshot AI sin Kimi K3 tilgjengelig på Databricks via Foundation Model API, med full styring gjennom Unity AI Gateway. Kimi K3 bruker en sparsom Mixture of Experts arkitektur med 104 milliarder aktive parametere per token og et kontekstvindu på 1 million tokens – den største åpne vektmodellen noensinne.
What should I do next in practice?
Unity AI Gateway gir et samlet kontrollpanel for bedriftsstyring, inkludert tilgangskontroll, revisjonsspor, KI kostnadskontroll, hastighetsbegrensning og Zero Data Retention.
7. august 2026 ble Moonshot AI sin Kimi K3 tilgjengelig på Databricks via Foundation Model API, med full styring gjennom Unity AI Gateway.
Kimi K3 bruker en sparsom Mixture of Experts arkitektur med 104 milliarder aktive parametere per token og et kontekstvindu på 1 million tokens – den største åpne vektmodellen noensinne.
Unity AI Gateway gir et samlet kontrollpanel for bedriftsstyring, inkludert tilgangskontroll, revisjonsspor, KI kostnadskontroll, hastighetsbegrensning og Zero Data Retention.
What did Databricks announce on August 7, 2026, regarding the integration of Moonshot AI's Kimi K3 model, what are the model's key specificaDatabricks adds Moonshot AI's Kimi K3, the world's largest open-weight model, to its platform with full governance via Unity AI Gateway.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: What did Databricks announce on August 7, 2026, regarding the integration of Moonshot AI's Kimi K3 model, what are the model's key specifica. Article summary: On August 7, 2026, Databricks announced that Moonshot AI's Kimi K3 is now available on the Databricks platform through the Foundation Model API, with full governance via Unity AI Gateway, initially hosted in the US with . Topic tags: general, news, general web, user generated, documentation. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, water
openai.com
On August 7, 2026, Databricks announced that Moonshot AI's Kimi K3 — the world's largest open-weight model — is now available on the Databricks platform through the Foundation Model API, with full governance via Unity AI Gateway . Initially hosted in the US with comprehensive Zero Data Retention (ZDR), the integration gives enterprises a governed way to deploy a frontier open-weight model that, according to Databricks' internal benchmarks, performs on par with top proprietary models from Anthropic, OpenAI, and Google Gemini — often at 50–70% lower cost per task.
This launch arrives alongside the General Availability of Unity AI Gateway, which provides a single control plane for managing AI spend, security, and access across models, agents, and MCP servers .
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "Databricks lanserer Kimi K3: Åpen-modell-kraft som matcher GPT-5.6 og Claude til halve prisen"?
7. august 2026 ble Moonshot AI sin Kimi K3 tilgjengelig på Databricks via Foundation Model API, med full styring gjennom Unity AI Gateway.
What are the key points to validate first?
7. august 2026 ble Moonshot AI sin Kimi K3 tilgjengelig på Databricks via Foundation Model API, med full styring gjennom Unity AI Gateway. Kimi K3 bruker en sparsom Mixture of Experts arkitektur med 104 milliarder aktive parametere per token og et kontekstvindu på 1 million tokens – den største åpne vektmodellen noensinne.
What should I do next in practice?
Unity AI Gateway gir et samlet kontrollpanel for bedriftsstyring, inkludert tilgangskontroll, revisjonsspor, KI kostnadskontroll, hastighetsbegrensning og Zero Data Retention.
Kimi K3 is a sparse Mixture-of-Experts (MoE) model with the following confirmed specifications :
Total parameters: 2.8 trillion
Active parameters: 104 billion activated per token
Architecture: Sparse MoE with 896 experts, 16 active per token, plus 2 shared experts
Context window: 1,048,576 tokens (1 million)
Modalities: Text and native vision (images and video)
Architectural innovations: Kimi Delta Attention (KDA), Attention Residuals (AttnRes), and Stable LatentMoE framework
Moonshot calls K3 "the world's first open 3T-class model" . The open weights were released on July 27, 2026, under a modified MIT license .
Artificial Analysis Intelligence Index ranking
On the independent Artificial Analysis Intelligence Index (v4.1), Kimi K3 scores 57 in its standard configuration and 60 on the "max" configuration . Its ranking is :
#3 or #4 overall among all models tested, depending on the configuration
First among open-weight models (next-closest open-weight: GLM-5.2 at 51)
Behind: Claude Fable 5 (~60) and GPT-5.6 Sol (~59)
Ahead of: Claude Opus 4.8 (~56), GPT-5.5, DeepSeek V4 Pro (44), and GLM-5.2 (51)
The index combines nine evaluations including GDPval-AA v2, Terminal-Bench 2.1, SciCode, Humanity's Last Exam, GPQA Diamond, and long-context reasoning . On specific benchmarks, Kimi K3 reached #1 on the Frontend Code Arena leaderboard and #2 on AA-Briefcase (agentic knowledge work), behind only Fable 5 .
Cost comparison to proprietary models
Kimi K3 offers a significant cost advantage over proprietary rivals :
Cost per Intelligence Index Task: Approximately $0.94
Roughly half the cost of Claude Opus 4.8 (max reasoning, $1.80)
Similar cost to GPT-5.6 Sol (max, $1.04)
Databricks reports 50–72% lower cost per task compared to proprietary leaders
API pricing: ~$3.00 per million input tokens and ~$15.00 per million output tokens
As Databricks CEO Ali Ghodsi told CNBC, K3's combination of benchmark performance and cost efficiency makes previously cost-prohibitive agentic workloads — such as continuous coding agents or large-scale document processing — economically viable for enterprises .
Unity AI Gateway enterprise governance and cost controls
Unity AI Gateway provides a unified control plane for platform teams managing AI across the enterprise. Key capabilities applicable to Kimi K3 include :
Fine-grained access permissions and safety guardrails enforced on model inputs and outputs
Complete audit trails and runtime observability for all model calls
AI spend controls — proactive budget alerts across users, workspaces, use cases, and entire accounts, combined with Unity Catalog system tables and Databricks budgets
Rate limiting and traffic routing across models, agents, and MCP servers
Hard spend caps that stop requests when a budget is reached
Zero Data Retention (ZDR) for Kimi K3 on Databricks
Unified governance across both open-weight and proprietary models through the same policy framework
During the same week, Unity AI Gateway reached General Availability, unifying spend management, rate limiting, and access control across enterprise LLMs, MCP servers, and coding agents .
Broader trend: the open-weight gap is closing rapidly
Databricks frames the Kimi K3 integration as evidence that the gap between open-weight and proprietary AI models is closing rapidly . The company notes that a year ago, the best open-weight models trailed their proprietary counterparts by a wide margin — but today, open-weight models like Kimi K3 rival proprietary leaders on benchmark performance while offering dramatically lower costs and greater flexibility .
According to Databricks, this shift enables enterprises to adopt hybrid AI strategies, mixing open-weight and proprietary models on a single governed platform. The company emphasizes that enterprises now have genuine choice — with the ability to route workloads to the most cost-effective model for each task, without sacrificing performance or governance .
Kimi K3 is a sparse Mixture-of-Experts (MoE) model with the following confirmed specifications :
Total parameters: 2.8 trillion
Active parameters: 104 billion activated per token
Architecture: Sparse MoE with 896 experts, 16 active per token, plus 2 shared experts
Context window: 1,048,576 tokens (1 million)
Modalities: Text and native vision (images and video)
Architectural innovations: Kimi Delta Attention (KDA), Attention Residuals (AttnRes), and Stable LatentMoE framework
Moonshot calls K3 "the world's first open 3T-class model" . The open weights were released on July 27, 2026, under a modified MIT license .
Artificial Analysis Intelligence Index ranking
On the independent Artificial Analysis Intelligence Index (v4.1), Kimi K3 scores 57 in its standard configuration and 60 on the "max" configuration . Its ranking is :
#3 or #4 overall among all models tested, depending on the configuration
First among open-weight models (next-closest open-weight: GLM-5.2 at 51)
Behind: Claude Fable 5 (~60) and GPT-5.6 Sol (~59)
Ahead of: Claude Opus 4.8 (~56), GPT-5.5, DeepSeek V4 Pro (44), and GLM-5.2 (51)
The index combines nine evaluations including GDPval-AA v2, Terminal-Bench 2.1, SciCode, Humanity's Last Exam, GPQA Diamond, and long-context reasoning . On specific benchmarks, Kimi K3 reached #1 on the Frontend Code Arena leaderboard and #2 on AA-Briefcase (agentic knowledge work), behind only Fable 5 .
Cost comparison to proprietary models
Kimi K3 offers a significant cost advantage over proprietary rivals :
Cost per Intelligence Index Task: Approximately $0.94
Roughly half the cost of Claude Opus 4.8 (max reasoning, $1.80)
Similar cost to GPT-5.6 Sol (max, $1.04)
Databricks reports 50–72% lower cost per task compared to proprietary leaders
API pricing: ~$3.00 per million input tokens and ~$15.00 per million output tokens
As Databricks CEO Ali Ghodsi told CNBC, K3's combination of benchmark performance and cost efficiency makes previously cost-prohibitive agentic workloads — such as continuous coding agents or large-scale document processing — economically viable for enterprises .
Unity AI Gateway enterprise governance and cost controls
Unity AI Gateway provides a unified control plane for platform teams managing AI across the enterprise. Key capabilities applicable to Kimi K3 include :
Fine-grained access permissions and safety guardrails enforced on model inputs and outputs
Complete audit trails and runtime observability for all model calls
AI spend controls — proactive budget alerts across users, workspaces, use cases, and entire accounts, combined with Unity Catalog system tables and Databricks budgets
Rate limiting and traffic routing across models, agents, and MCP servers
Hard spend caps that stop requests when a budget is reached
Zero Data Retention (ZDR) for Kimi K3 on Databricks
Unified governance across both open-weight and proprietary models through the same policy framework
During the same week, Unity AI Gateway reached General Availability, unifying spend management, rate limiting, and access control across enterprise LLMs, MCP servers, and coding agents .
Broader trend: the open-weight gap is closing rapidly
Databricks frames the Kimi K3 integration as evidence that the gap between open-weight and proprietary AI models is closing rapidly . The company notes that a year ago, the best open-weight models trailed their proprietary counterparts by a wide margin — but today, open-weight models like Kimi K3 rival proprietary leaders on benchmark performance while offering dramatically lower costs and greater flexibility .
According to Databricks, this shift enables enterprises to adopt hybrid AI strategies, mixing open-weight and proprietary models on a single governed platform. The company emphasizes that enterprises now have genuine choice — with the ability to route workloads to the most cost-effective model for each task, without sacrificing performance or governance .
