Hero AI Models

overview hero ai does not use a single model for every feature for default swimlane managed routing, inference uses anthropic claude on amazon bedrock , with a different primary model per feature hero ai native action defaults to claude haiku 4 5 starting in turbine 26 4 0 , agents (plan generation, remediation, playbook builder, and component agents) default to claude sonnet 5 , with claude sonnet 4 5 as fallback hero ai companion stays on claude sonnet 4 5 starting in 26 2 0 , you choose the model for each hero ai native action docid\ c4ckmt7fhxgjbtlxxooc2 on the prompt tab ( model selection ), or leave use default model for claude haiku 4 5 if a saved modelid is no longer in the available models list (for example after a custom llm docid\ deoy k3jmcpdm c ikrq4 provider change), the action uses the small model at run time and shows previously selected model is not available in the action dialog see when the saved model is unavailable in hero ai native action docid\ c4ckmt7fhxgjbtlxxooc2 you can replace swimlane managed models with custom llm docid\ deoy k3jmcpdm c ikrq4 or turbine platform installer settings swimlane does not charge hero ai credits for usage through your custom llm models use the sections below for default models by feature, data residency when you pick models, and how tokens convert to credits on swimlane managed models default swimlane models for standard turbine deployments, swimlane routes these features to the primary claude model on bedrock when you use swimlane managed routing feature default (primary) hero ai native action docid\ c4ckmt7fhxgjbtlxxooc2 claude haiku 4 5 hero ai companion docid\ crqkfvngpcz wj8xthds7 claude sonnet 4 5 agents (plan generation, remediation, playbook builder, component agents) claude sonnet 5 (fallback claude sonnet 4 5) use default model on a hero ai native action docid\ c4ckmt7fhxgjbtlxxooc2 is claude haiku 4 5 claude sonnet 5 is the default for agents claude sonnet 4 5 is the default for hero ai companion docid\ crqkfvngpcz wj8xthds7 sonnet 5 is not offered in the native action model drop down in turbine 26 4 hero ai companion uses claude sonnet 4 5 on swimlane managed routing, as shown in the table above agents use claude sonnet 5 for ai soc plan and remediation flows, playbook generation, and related agentic workflows if claude sonnet 5 is unavailable, swimlane managed routing falls back to claude sonnet 4 5 hero ai companion continues to use claude sonnet 4 5 and does not support sonnet 5 if custom llm is enabled and the medium default is sonnet 5, companion does not work keep medium on sonnet 4 5 when users need companion you cannot configure agent or companion routing in the ui swimlane does not use your content to train or fine tune these foundation models when defaults do not apply the table above describes swimlane managed default routing your environment may use different models when override applies to details custom llm docid\ deoy k3jmcpdm c ikrq4 turbine cloud (litellm) and turbine platform (litellm or custom bedrock) account level custom llm settings replace default bedrock routing with your proxy or bedrock inference profiles usage through custom llm is not billed as hero ai credits turbine platform installer https //docs swimlane com/turbine installer/configure the turbine platform for an embedded cluster install#llm proxy settings ( llm proxy settings ; turbine platform 26 0 4 only) turbine platform on premises environment variables (for example llm model id medium , swimlane heroaichat models bedrock model ) can override defaults confirm with your swimlane contact which settings apply in your cluster hero ai native action model selection playbooks that set an explicit model on the prompt tab starting in 26 2 0 , choose a model in model selection or leave use default model ( claude haiku 4 5 ) the selected model overrides swimlane managed default routing for that action only see hero ai native action model residency https //docs swimlane com/hero ai models#hero ai native action model residency 2620 regional availability residency labels in the tables below describe where inference runs for your swimlane instance label meaning us , eu , au , jp inference stays within that geography for your instance global inference may route across aws regions when residency rules allow in region the model runs in the aws region for that swimlane instance (not a cross region geo profile) n/a the model is not offered for that instance default models (haiku 4 5 and sonnet 4 5) the table below shows where claude haiku 4 5 (default when hero ai native action uses use default model ) and claude sonnet 4 5 (default for hero ai companion docid\ crqkfvngpcz wj8xthds7 ; agents fallback in 26 4 0) run for each swimlane instance swimlane instance claude haiku 4 5 claude sonnet 4 5 usn swimlane app us us us1 swimlane app us us gov swimlane app us us uk1 swimlane app eu eu de1 swimlane app eu eu ca1 swimlane app us us au1 swimlane app au au sg1 swimlane app global global jp1 swimlane app jp jp for aws region details within each geo profile, see geo inference details on the matching model card claude haiku 4 5 on amazon bedrock https //docs aws amazon com/bedrock/latest/userguide/model card anthropic claude haiku 4 5 htmlclaude sonnet 4 5 on amazon bedrock https //docs aws amazon com/bedrock/latest/userguide/model card anthropic claude sonnet 4 5 html hero ai native action model residency (26 2 0) starting in 26 2 0 , you can select additional models on the hero ai native action prompt tab the table below shows residency for each optional model by swimlane instance see the residency labels https //docs swimlane com/hero ai models#regional availability table for us , eu , global , and in region model usn us1 gov uk1 de1 ca1 au1 sg1 jp1 claude haiku 4 5 us us us eu eu us au global jp claude sonnet 4 5 us us us eu eu us au global jp openai gpt oss 20b in region in region in region in region in region n/a in region n/a in region openai gpt oss 120b in region in region in region in region in region n/a in region n/a in region gemma 3 27b in region in region in region in region in region n/a in region n/a in region qwen3 32b in region in region in region in region in region n/a in region n/a in region claude sonnet 4 6 us us us in region eu us au global jp claude opus 4 5 us us us eu eu us global global global claude opus 4 6 us us us in region eu us au global global for open weight models, use regional availability in amazon bedrock https //docs aws amazon com/bedrock/latest/userguide/models region compatibility html to confirm which models are offered in each aws region how credits are calculated when hero ai credits is enabled for your account and inference uses swimlane managed models, turbine converts token usage into credits at a high level the platform records token counts by category input , output , cache read , and cache write (when caching applies) each category is multiplied by a weight for that model weighted tokens are divided by the model’s tokens per credit rate to produce credit consumption for swimlane managed claude models in the catalog (haiku, sonnet, opus), the seeded weights are category weight input 1 output 5 cache write 1 3 cache read 0 1 so output tokens count five times as much as input tokens toward credits for those claude models open weight models in the catalog use similar input weight 1 , with output weights of 4 3 (gpt oss 20b) or 4 (gpt oss 120b, qwen3 32b), and 0 for cache categories when caching does not apply you do not configure these weights in the product ui on the hero ai native action model drop down, models can show a secondary line such as claude / 300 tokens per credit so you can compare relative cost when selecting a model see hero ai native action docid\ c4ckmt7fhxgjbtlxxooc2 sonnet 5 is filtered out of that drop down in turbine 26 4 usage (tokens and credits) hero ai consumption is reported under admin panel > usage report what it shows hero ai credits docid\ fpbh2j8xm1tfdvejnc8kb credit usage and contract caps for the current billing period — playbook usage and user prompts tabs hero ai prompts docid\ rv7wvn3wx2uwa bchy7ur prompt counts by playbook, component, or user hero ai tokens docid 8g7pp4zuiwuisuvyq32s9 token volume (input, output, cache read, cache write) model and tokens per credit when your account uses hero ai credits , turbine converts token usage to credits using the rates below tokens per credit is how many tokens of that model equal one credit before category weights are applied output tokens count more heavily than input tokens when credits are calculated cache read and cache write token counts appear on the hero ai tokens docid 8g7pp4zuiwuisuvyq32s9 dashboard; you do not configure those weights in the product model tokens per credit haiku 4 5 300 opus 4 5 60 opus 4 6 60 openai gpt oss 20b 4500 openai gpt oss 120b 2200 qwen3 32b 2200 sonnet 4 5 100 sonnet 4 6 100 these rates apply to swimlane managed models on turbine cloud when hero ai credits is enabled for your account swimlane does not charge hero ai credits for usage through custom llm docid\ deoy k3jmcpdm c ikrq4 credits, prompts, and tokens measure different aspects of hero ai usage use the dashboard that matches the metric you need