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Use of this model is governed by theNVIDIA Open Model Agreement.Additional Information:Apache 2.0 License.

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Step 3.7 Flash

Description

Step 3.7 Flash is a StepFun vision-language model built on Step 3.5 Flash with additional vision capability for native multimodal, agentic, and coding-related use cases. The model is intended to process text and image inputs and produce text outputs, with emphasis on image understanding, fast throughput, and tool-use workflows.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration:

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Step 3.7 Flash Model Card.

License and Terms of Use:

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of the model is governed by the NVIDIA Open Model License Agreement.

Additional Information: Apache-2.0.

Deployment Geography:

Global

Use Case:

Use Case: Developers and enterprises can use this model to build multimodal assistants, document and image understanding workflows, long-context analysis systems, visual-language conversational applications, and agentic workflows that combine perception, search, and reasoning.

Release Date:

NGC 05/28/2026 via Step 3.7 Flash model link

Reference(s):

References:

Step 3.7 Flash Model Card

Model Architecture:

Architecture Type: Transformer\nNetwork Architecture: Multimodal Mixture-of-Experts vision-language architecture with a 196B-parameter language backbone and a 1.8B-parameter vision encoder. The model has 198B total parameters, activates approximately 11B parameters per token, supports a 256K context window, and supports text and image inputs.\nTotal Parameters: 198B\nActive Parameters: Approximately 11B\nBase Model: Step 3.5 Flash

Input:

Input Types: Text, Image\nInput Formats: Text: String; Image: Red, Green, Blue (RGB)\nInput Parameters: One Dimensional (1D), Two Dimensional (2D)\nOther Input Properties: Text prompts, chat messages, image inputs, and interleaved image-and-text inputs. The model supports context length up to 256K tokens.

Output:

Output Types: Text\nOutput Format: String\nOutput Parameters: One Dimensional (1D)\nOther Output Properties: Generated natural language, structured text, and image-grounded answers.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engines:

vLLM

Supported Hardware:

NVIDIA B200
NVIDIA H100
NVIDIA H20
NVIDIA H200

Operating Systems: Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

Step 3.7 Flash 1.0

Training, Testing, and Evaluation Datasets

Training Dataset

Data Modality: Text and image\nText Training Data Size: Undisclosed\nImage Training Data Size: Undisclosed\nData Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Testing Dataset

Data Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Evaluation Dataset

Data Collection Method by dataset: [Hybrid: Automated, Human]\nLabeling Method by dataset: [Hybrid: Automated, Human]\nProperties: Evaluation benchmarks include SimpleVQA (Search), V* (Python), ClawEval-1.1, Toolathlon, HLE w. Tool, SWE-PRO, Terminal-Bench v2.1, and GPDVal.

Inference

Engine: vLLM\nTest Hardware:

NVIDIA B200
NVIDIA H100
NVIDIA H20
NVIDIA H200

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more.

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (NVIDIA Developer Forums).

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The model is intended to process text and image inputs and produce text outputs, with emphasis on image understanding, fast throughput, and tool-use workflows.\n\nThis model is ready for commercial/non-commercial use.\n\n## Third-Party Community Consideration:\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [Step 3.7 Flash Model Card](https://catalog.ngc.nvidia.com/orgs/nim/teams/stepfun-ai/models/step-3.7-flash).\n\n## License and Terms of Use:\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of the model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\nAdditional Information: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n## Deployment Geography:\nGlobal\n\n## Use Case:\n**Use Case:** Developers and enterprises can use this model to build multimodal assistants, document and image understanding workflows, long-context analysis systems, visual-language conversational applications, and agentic workflows that combine perception, search, and reasoning.\n\n## Release Date:\n**NGC** 05/28/2026 via [Step 3.7 Flash model link](https://catalog.ngc.nvidia.com/orgs/nim/teams/stepfun-ai/models/step-3.7-flash)\n\n## Reference(s):\n**References:**\n- [Step 3.7 Flash Model Card](https://catalog.ngc.nvidia.com/orgs/nim/teams/stepfun-ai/models/step-3.7-flash)\n\n## Model Architecture:\n**Architecture Type:** Transformer\n**Network Architecture:** Multimodal Mixture-of-Experts vision-language architecture with a 196B-parameter language backbone and a 1.8B-parameter vision encoder. The model has 198B total parameters, activates approximately 11B parameters per token, supports a 256K context window, and supports text and image inputs.\n**Total Parameters:** 198B\n**Active Parameters:** Approximately 11B\n**Base Model:** Step 3.5 Flash\n\n### Input:\n**Input Types:** Text, Image\n**Input Formats:** Text: String; Image: Red, Green, Blue (RGB)\n**Input Parameters:** One Dimensional (1D), Two Dimensional (2D)\n**Other Input Properties:** Text prompts, chat messages, image inputs, and interleaved image-and-text inputs. The model supports context length up to 256K tokens.\n\n### Output:\n**Output Types:** Text\n**Output Format:** String\n**Output Parameters:** One Dimensional (1D)\n**Other Output Properties:** Generated natural language, structured text, and image-grounded answers.\n\n__Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.__\n\n## Software Integration:\n**Runtime Engines:**\n- vLLM\n\n**Supported Hardware:**\n- NVIDIA B200\n- NVIDIA H100\n- NVIDIA H20\n- NVIDIA H200\n\n\n\n**Operating Systems:** Linux\n\n__The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__\n\n## Model Version(s)\nStep 3.7 Flash 1.0\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Dataset\n**Data Modality:** Text and image\n**Text Training Data Size:** Undisclosed\n**Image Training Data Size:** Undisclosed\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Testing Dataset\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Evaluation Dataset\n**Data Collection Method by dataset:** [Hybrid: Automated, Human]\n**Labeling Method by dataset:** [Hybrid: Automated, Human]\n**Properties:** Evaluation benchmarks include SimpleVQA (Search), V* (Python), ClawEval-1.1, Toolathlon, HLE w. Tool, SWE-PRO, Terminal-Bench v2.1, and GPDVal.\n\n## Inference\n**Engine:** vLLM\n**Test Hardware:**\n- NVIDIA B200\n- NVIDIA H100\n- NVIDIA H20\n- NVIDIA H200\n\n\n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.\n\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.\n\nUsers are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n# Get Help\n\n## Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [NIM Container page](https://catalog.ngc.nvidia.com/orgs/nim/teams/stepfun-ai/containers/step-3.7-flash) for release documentation, deployment guides and more.\n\n## NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases ([NVIDIA Developer Forums](https://forums.developer.nvidia.com/)).","displayName","Step-3.7-Flash-NIM","updatedDate",["D",1779989059751],"publisher","Stepfun-AI","latestVersion","nim-bf16-260528","versions","collectionCounts","imageUrl","/api/image/nim/stepfun-ai/models/step-3.7-flash","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes",402741210864,"licenseTerms",{"_173":62,"_174":14,"_175":176,"_177":16},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[167],"displayLabels",[159],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_123":160,"_161":160,"_162":163,"_164":165},"NSPECT-DAT7-HXCR","value","filter",{"_166":165,"_161":160},"category","label","field",{"_123":168,"_161":169,"_162":170,"_164":171},"NVIDIA NIM","nimmcro_nvidia_nim",{"_166":172,"_161":169},"feature","nvidia_nim","governingTerms","licenseId","licenseVersion","v1.0","needsAcceptance","appConfig",{"_1359":1360,"_212":1361},"ngcApps",[1312],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1293":1294,"_1295":1296,"_1297":1298,"_1299":1300,"_1301":190,"_79":217,"_1302":1303,"_1304":47,"_1305":66,"_1306":66,"_1307":1308,"_1309":66,"_1310":1311},"amplitude",{"_1290":1291,"_891":-7,"_1292":16},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1284":1285,"_1286":1287,"_1288":1289},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1166":136,"_1167":1168,"_1169":1170,"_1171":1172},"announcements",[],"consentState",{"_254":1156,"_1157":66,"_1158":16,"_1159":16,"_1160":1161},"consentConfig",{"_236":237,"_238":239,"_240":241,"_242":243,"_244":245,"_246":247,"_248":249,"_250":251,"_252":253,"_254":255},"showContactAdmin",1783560333971,[],[],[231,232,233,234,235],{"_36":50,"_38":57,"_40":52,"_42":-5,"_44":-5,"_46":47},{"_36":50,"_38":54,"_40":52,"_42":-5,"_44":-5,"_46":47},{"_36":50,"_38":51,"_40":52,"_42":-5,"_44":-5,"_46":47},{"_36":48,"_38":49,"_40":52,"_42":-5,"_44":-5,"_46":47},{"_36":37,"_38":39,"_40":52,"_42":-5,"_44":-5,"_46":47},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440],[279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295],{"_296":402,"_123":402,"_121":136,"_269":403},{"_296":397,"_123":397,"_121":136,"_269":398},{"_296":393,"_123":393,"_121":136,"_269":394},{"_296":388,"_123":388,"_121":136,"_269":389},{"_296":383,"_123":383,"_121":136,"_269":384},{"_296":371,"_123":371,"_121":136,"_269":372},{"_296":359,"_123":359,"_121":136,"_269":360},{"_296":354,"_123":354,"_121":136,"_269":355},{"_296":343,"_123":343,"_121":136,"_269":344},{"_296":338,"_123":338,"_121":136,"_269":339},{"_296":333,"_123":333,"_121":136,"_269":334},{"_296":328,"_123":328,"_121":136,"_269":329},{"_296":323,"_123":323,"_121":136,"_269":324},{"_296":318,"_123":318,"_121":136,"_269":319},{"_296":313,"_123":313,"_121":136,"_269":314},{"_296":306,"_123":306,"_121":136,"_269":307},{"_296":297,"_123":297,"_121":136,"_269":298},"hostName","amazon-adsystem.com",[299,300],{"_263":305,"_302":297,"_303":16,"_121":304},{"_263":301,"_302":297,"_303":16,"_121":304},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[308,309],{"_263":312,"_302":306,"_303":16,"_121":311},{"_263":310,"_302":306,"_303":16,"_121":311},"IDE","This domain is owned by Doubleclick (Google). 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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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