--- sourceDocument: Australia Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences Release : - australia ft:locale : - en-US ft:publication_title : - Australia Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Model provider updates # Model provider updates {#ariaid-title1} Release version: Australia Updated August 11, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 6 minutes to read Review these updates to learn which model providers and models are available for your skills and agents, and review the model cards for information about how each model is intended to be used. ## Model cards Large language models (LLMs) are complex machine-learning models that are trained on large datasets like websites and documentation to perform language-related tasks. Examples include text generation for case summaries and resolution notes. Model cards explain the specific model's context, intended use, training data, limitations, and other important information. These model cards are for skills that use the Now LLM Service. There are certain skills, such as Now Assist Multi-Turn Catalog Ordering, that use Azure OpenAI instead. To see what LLM a skill is using, you can check the skill list in the AI Admin Hub console and review the LLM service column. [Model card for ServiceNow large language model](https://downloads.docs.servicenow.com/resource/enus/infocard/sn-llm.pdf) : Model used for AI-driven solutions to support natural language understanding, automation, and decision support. : This model card is available in Yokohama patch 1 and later. [Model card for ServiceNow large language model (V2)](https://downloads.docs.servicenow.com/resource/enus/infocard/sn-llm-v2.pdf) : Model for enterprise AI that enhances text-based automation and content generation in ServiceNow workflows, including requester OOTB skills, custom skills, and agentic use cases. This model is for Generative AI Controller application 11.2 or higher. [Model card for ServiceNow small language model](https://downloads.docs.servicenow.com/resource/enus/infocard/sn-slm.pdf) : Model used for enterprise AI applications by enhancing text-based automation and content generation within ServiceNow workflows. : This model card is available in Yokohama patch 1 and later. [Model card for ServiceNow small language model (V2)](https://downloads.docs.servicenow.com/resource/enus/infocard/sn-slm-v2.pdf) : Model for enterprise AI that enhances text-based automation and content generation in ServiceNow workflows, including creator and fulfiller OOTB skills as well as custom skills. This model is for Generative AI Controller application 11.2 or higher. [Model card for ServiceNow third party large language model](https://downloads.docs.servicenow.com/resource/enus/infocard/third-party-llm.pdf) : Model used for AI-driven solutions for text generation, summarization, and conversational AI. [Model card for ServiceNow Voice AI Speech-to-Text and Text-to-Speech models](https://downloads.docs.servicenow.com/resource/enus/infocard/sn-voice.pdf) : Models used within ServiceNow AI Voice Agents for converting spoken user input to text and generating natural-sounding speech from AI responses. [Model card for ServiceNow AI Guardian model](https://downloads.docs.servicenow.com/resource/enus/infocard/sn-na-guardian.pdf) : This model provides content moderation and helps identify different kinds of prompt injection attacks and offensive content. [Model card for ServiceNow Inferred CSAT and Factors large language model](https://downloads.docs.servicenow.com/resource/enus/infocard/csat-llm.pdf) : This model is designed to ingest a conversation and predict a CSAT score as well as factors that explain the predicted score. ## July 2026 {#now-llm-model-updates__section_july_2026} The Now LLM Service model strategy has moved toward integrated model provider flexibility. Store application updates have started shipping, and the default model for base system skills and agents is changing from the Now LLM Service to an integrated third-party model provider. If you haven't changed the default model for a skill, that skill uptakes the new default. * Integrated third-party model providers: The integrated third-party model providers are Google Gemini, AWS Claude, and Azure OpenAI. * Existing selections preserved: If you have already selected a model provider, that configuration is preserved. The update doesn't overwrite an explicit selection. * Changing your model provider: To select a model provider other than the default, review the providers that are allowed for your instance in AI Control Tower. Navigate to ConfigurationsControlsAI model providers, and then make your changes in the ServiceNow Otto Admin Center by navigating to SettingsManage AI ModelsManage Model Providers. * Explicit model names required: Generic model references such as `cloud_small` and `cloud_large` are no longer supported as of December 2026, when the Now LLM Service transitions to integrated third-party model providers. {#now-llm-model-updates__ul_july_2026} ## June 2026 {#now-llm-model-updates__section_june_2026} The June release includes updates to third-party model defaults. It also contains a change to the default reasoning effort setting for GPT-5 Mini, and the retirement of the Now LLM long-term support (LTS) SKU. * Third-party default model version update: Teams that did not update their third-party default model versions to the latest available versions in the May release must do so in the June release. GAIC 13.1.2 is the required version for this update. * GPT-5 Mini: `reasoning_effort` default change: The default `reasoning_effort` setting for GPT-5 Mini has changed from `none` to `minimal`. This change is included in GAIC Snapshot 14.0.0, which is compatible with Now Assist for Platform 12.0.0. Teams using GPT-5 Mini should run regression and functional testing to confirm that the new default works as expected. If you explicitly set `reasoning_effort` in your generative AI config additional properties, smoke test to verify there are no unexpected effects. If you have `reasoning_effort: none` set in additional properties, update the value to `minimal` and run regression and functional testing. * Claude Sonnet 4.0 retirement: Claude Sonnet 4.0 references are being redirected on the backend. No team action is required. * `claude_large` / Claude 4.0 Sonnet redirects to Claude 4.5 Sonnet * `claude_small` / Claude 4.0 Sonnet redirects to Claude 4.5 Sonnet * Now LLM LTS SKU retired: ServiceNow no longer offers the LTS model SKU. There are no testing requirements or expectations for teams related to this retirement. ## May 2025 {#now-llm-model-updates__section_qmv_nns_jfc} An advanced 12B general-purpose small language model (SLM) with a singular, high-performance architecture that supports a wide range of tasks in ServiceNow's context was released. Fine-tuned on Mistral-Nemo-12B-Instruct, this model is designed and optimized for tasks like Agent Assist, Text-to-Flow, Text-to-Cypher, Safety \& Content Moderation and Text-to-Code. Key Enhancements: * Enhanced instruction adherence: Improved the model's capability to accurately interpret and follow user instructions, ensuring that the model can better understand and execute complex commands. Leading to more precise and reliable outcomes than previous releases. * Increased context window: Increased context window from 16K to 32K, enabling the model to better understand long-form inputs. This increase maintains coherence over extended interactions, and supports more complex tasks with richer contextual awareness. * Improved multilingual proficiency: Boosted performance across languages compared to previous releases, with notable enhancements in Japanese processing. * Optimized for ServiceNow workflow related capabilities: Extended support coverage for Text-to-Flow, and improved the performance of Text-to-Code, Text-to-Cypher etc. * Continuously enhanced model deployment consolidation: Integrates ServiceNow-related tasks into a single model, reducing system complexity at the same time while elevating overall performance. {#now-llm-model-updates__ul_y25_sns_jfc} ## March 2025 {#now-llm-model-updates__section_yks_3cn_m2c} A powerful 12B general-purpose small language model (SLM) designed to enhance a wide range of applications, including text-to-code and agent use cases was released. Fine-tuned on Mistral-Nemo-12B, it streamlines deployment and consolidates multiple functionalities into a singular, architecture. Key Enhancements: * Optimized to fulfill use cases: Enhances case summarization, chat summarization, resolution notes, and knowledge base generation across supported languages, including improvements in Japanese quality. * Superior text-to-code and text-to-cypher performance: Delivers major advancements in Glide JavaScript and generic JavaScript editing and generation, along with improved accuracy in query generation and execution for structured databases. * Robust content moderation and safety: Provides stronger protection against adversarial prompts, jail-breaking attempts, and harmful content generation, ensuring safer deployment with built-in content filtering. * Unified model deployment:integrates ServiceNow-related tasks into a single model, thereby reducing system complexity while elevating overall performance. * Improved instruction adherence: Delivers better instruction following and consistency across varying levels of prompt and instruction strictness than the current text-to-text NowLLM. {#now-llm-model-updates__ul_xv4_1q5_m2c} ## November 2024 {#now-llm-model-updates__section_o24_ym3_gdc} Several key improvements were added to the Now LLM Service that are aimed at enhancing performance and quality. * Multilingual support: Now LLM Service supports 8 additional languages, enabling global teams to use the model in their native languages. The supported languages are: English, German, French, Japanese, Dutch, French Canadian, Spanish, Brazilian Portuguese, and Italian. * JSON format support: The model now provides output in JSON format, making it easier for developers to integrate with various applications and automate workflows seamlessly. * Deterministic responses: JSON mode ensures structured, consistent output, which improves predictability and reliability when integrating with applications. * Error reduction: Unlike free-form text mode, JSON responses are less prone to format errors or stray characters, minimizing integration issues. * Lower token consumption: The fixed structure of JSON can reduce token usage, making it more efficient and cost-effective for applications with high response frequency. {#now-llm-model-updates__ul_kmm_b43_gdc} * Improvements in instruction following: The model has been fine-tuned to understand and follow instructions more precisely. This enables the model to deliver more to-the-point and actionable responses, helping users get the information they need faster and more efficiently. {#now-llm-model-updates__ul_mwj_nn3_gdc} *[\>]: and then