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  "aboutHtml": "<p>Llama Nemotron Embed VL 1B V2 is NVIDIA’s embedding model for multimodal question-answering retrieval. It can represent a document as text, an image or a combination of image and text. A text query can then be used to retrieve related documents. Its published profile supports document images containing text, tables, charts and infographics, and returns embedding vectors.</p><h4>Prepare identifiable document items</h4><p>Keep each input connected to the source document, page or passage it represents. For a visual page, preserve the image and any accompanying text that belongs to it. For a text passage, retain its title or section label when that helps interpret a later match. These source identifiers make a retrieved item useful as evidence rather than only as a numeric similarity result.</p><p>Choose item boundaries around the material a user needs to find. A chart and its explanation may belong together; unrelated pages should retain their separate identities. Review whether the input is readable and whether the text or image contains the information the intended query is meant to retrieve.</p><h4>Text queries and vector retrieval</h4><p>The model’s role is to produce vectors for retrieval, with text as the query form described by the profile. Keep the query and indexed items in a compatible embedding setup. The application stores the vectors, compares them and returns the corresponding source items. The retrieved document remains the material to inspect or pass to a later answer-generation step.</p><p>A useful test set includes the questions users actually ask and the pages that should be found. Review wrong or missing matches against the source content and item boundaries. Retain the full document identifier with any selected passage or image so the result can be checked in context.</p><h4>Input allowance and the search artifact</h4><p>The documented hosted profile lists up to 131,072 tokens per input. Split larger source sets into separate, labeled inputs and preserve their relationships. The profile’s vector output is not a generated prose answer or a transcript; a later application stage can use the retrieved evidence to answer a question.</p><p>A completed retrieval artifact joins the query, selected source items and their identifiers. Preserve the embedding model and input preparation used for an index, so later changes can be distinguished from changes in the source collection.</p>",
  "aboutText": "Llama Nemotron Embed VL 1B V2 is NVIDIA’s embedding model for multimodal question-answering retrieval. It can represent a document as text, an image or a combination of image and text. A text query can then be used to retrieve related documents. Its published profile supports document images containing text, tables, charts and infographics, and returns embedding vectors.\n\n### Prepare identifiable document items\n\nKeep each input connected to the source document, page or passage it represents. For a visual page, preserve the image and any accompanying text that belongs to it. For a text passage, retain its title or section label when that helps interpret a later match. These source identifiers make a retrieved item useful as evidence rather than only as a numeric similarity result.\n\nChoose item boundaries around the material a user needs to find. A chart and its explanation may belong together; unrelated pages should retain their separate identities. Review whether the input is readable and whether the text or image contains the information the intended query is meant to retrieve.\n\n### Text queries and vector retrieval\n\nThe model’s role is to produce vectors for retrieval, with text as the query form described by the profile. Keep the query and indexed items in a compatible embedding setup. The application stores the vectors, compares them and returns the corresponding source items. The retrieved document remains the material to inspect or pass to a later answer-generation step.\n\nA useful test set includes the questions users actually ask and the pages that should be found. Review wrong or missing matches against the source content and item boundaries. Retain the full document identifier with any selected passage or image so the result can be checked in context.\n\n### Input allowance and the search artifact\n\nThe documented hosted profile lists up to 131,072 tokens per input. Split larger source sets into separate, labeled inputs and preserve their relationships. The profile’s vector output is not a generated prose answer or a transcript; a later application stage can use the retrieved evidence to answer a question.\n\nA completed retrieval artifact joins the query, selected source items and their identifiers. Preserve the embedding model and input preparation used for an index, so later changes can be distinguished from changes in the source collection.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">NV</span><h2 id=\"sheet-title\">NVIDIA: Llama Nemotron Embed VL 1B V2 (free)</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Close\">×</button></div><div class=\"idline\"><span>NVIDIA /</span><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code><button type=\"button\" id=\"copy-id\">Copy ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Model card sections\"><button type=\"button\" data-card-jump=\"overview\">Overview</button><button type=\"button\" data-card-jump=\"methods\">Inputs &amp; methods</button><button type=\"button\" data-card-jump=\"pricing\">Pricing</button><button type=\"button\" data-card-jump=\"examples\">Examples</button><button type=\"button\" data-card-jump=\"results\">Results</button><button type=\"button\" data-card-jump=\"aliases\">Aliases</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API methods\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree\">Embeddings</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Overview</h3><p class=\"mdesc-full\">Llama Nemotron Embed VL 1B V2 is NVIDIA’s multimodal embedding model for retrieval. It turns text, images or combined document inputs into vectors and supports text-query search over documents containing tables, charts, text and infographics.</p><dl class=\"card-io\" data-card-io><div><dt>Accepted inputs</dt><dd>Text · token_ids</dd></div><div><dt>Results</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>About the model</summary><p class=\"note-s\" data-model-method-boundary>This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.</p><div class=\"mabout\" id=\"m-about\"><p>Llama Nemotron Embed VL 1B V2 is NVIDIA’s embedding model for multimodal question-answering retrieval. It can represent a document as text, an image or a combination of image and text. A text query can then be used to retrieve related documents. Its published profile supports document images containing text, tables, charts and infographics, and returns embedding vectors.</p><h4>Prepare identifiable document items</h4><p>Keep each input connected to the source document, page or passage it represents. For a visual page, preserve the image and any accompanying text that belongs to it. For a text passage, retain its title or section label when that helps interpret a later match. These source identifiers make a retrieved item useful as evidence rather than only as a numeric similarity result.</p><p>Choose item boundaries around the material a user needs to find. A chart and its explanation may belong together; unrelated pages should retain their separate identities. Review whether the input is readable and whether the text or image contains the information the intended query is meant to retrieve.</p><h4>Text queries and vector retrieval</h4><p>The model’s role is to produce vectors for retrieval, with text as the query form described by the profile. Keep the query and indexed items in a compatible embedding setup. The application stores the vectors, compares them and returns the corresponding source items. The retrieved document remains the material to inspect or pass to a later answer-generation step.</p><p>A useful test set includes the questions users actually ask and the pages that should be found. Review wrong or missing matches against the source content and item boundaries. Retain the full document identifier with any selected passage or image so the result can be checked in context.</p><h4>Input allowance and the search artifact</h4><p>The documented hosted profile lists up to 131,072 tokens per input. Split larger source sets into separate, labeled inputs and preserve their relationships. The profile’s vector output is not a generated prose answer or a transcript; a later application stage can use the retrieved evidence to answer a question.</p><p>A completed retrieval artifact joins the query, selected source items and their identifiers. Preserve the embedding model and input preparation used for an index, so later changes can be distinguished from changes in the source collection.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Available in Ailoot · inputs &amp; methods</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Embeddings</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Input types</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Output types</dt><dd><code>embedding</code></dd></div><div><dt>Context</dt><dd>131,072 tokens</dd></div><div><dt>Output format</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Field</th><th>Type</th><th>Requirement</th><th>Values &amp; limits</th><th>Default</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Required</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>nvidia/llama-nemotron-embed-vl-1b-v2:free</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Required</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Minimum items: 1</div><div>Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers.</div><div>Input budget: Maximum: 131,072 · Text unit: UTF8 bytes · Token unit: token-array elements</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>float</div></td><td data-cell-label=\"Default\" data-default=\"known\"><span class=\"card-cell-label\">Default</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>integer</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Not supported</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Minimum: 1</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>json_value</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific.</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>json_value</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific.</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>json_value</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific.</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>object</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: provider.options rejected</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Input material combinations</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Method parameters and lifecycle</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Contract</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>model, input</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>input_shapes</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>encoding_format</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>float</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>dimensions_override</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>max_input_budget_units</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>131072</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>multimodal_objects</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>text, token_ids</td></tr></tbody></table></div><p>Lifecycle: <code>synchronous_json</code></p><p class=\"note-s\">Keep every vector index and dimension.</p><p class=\"note-s\">Multimodal model name does not admit image objects on this exact path.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pricing</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tariff references do not add request options. Supported parameters are listed for the selected method.</p><dl data-price-conditions><dt>Fast mode</dt><dd>Standard rate <span class=\"note-s\">When fast mode is selected</span></dd><dt>Flex mode</dt><dd>Standard rate <span class=\"note-s\">When flex mode is selected</span></dd><dt>Maximum reasoning effort</dt><dd>Standard rate <span class=\"note-s\">When maximum reasoning effort uses the default adjustment</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot rates</h4><h4>Prices by configuration</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Configuration</th><th>Rate schedule</th><th>Charge</th></tr></thead><tbody><tr><th data-cell-label=\"Configuration\" scope=\"row\"><span class=\"card-cell-label\">Configuration</span>Base token rates</th><td data-cell-label=\"Rate schedule\"><span class=\"card-cell-label\">Rate schedule</span>Base rate</td><td data-cell-label=\"Charge\"><span class=\"card-cell-label\">Charge</span><div><span>Input · Input tokens</span> <b>$0</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Output · Output tokens</span> <b>$0</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cache read</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cache write</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cache write, 1 hour</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. <a href=\"/docs/en/billing/reserve/\">How reservation and settlement work →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Example requests</h3><h4>Ailoot Ultra · Embeddings</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Code options\"><button class=\"cg-tab\" id=\"model-code-1992-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1992-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1992-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1992-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Copy code\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-1992-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1992-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;nvidia/llama-nemotron-embed-vl-1b-v2:free&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1992-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1992-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;nvidia/llama-nemotron-embed-vl-1b-v2:free&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Receive and save results</h3><h4>Ailoot Ultra · Embeddings</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Keep all completed results</h5><p class=\"note-s\">Keep every complete returned response, file and content part.</p><p class=\"note-s\">Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>If the original response needs checking</h5><p class=\"note-s\">No embedding job endpoint is introduced.</p></div><details class=\"card-about\" data-output-fields><summary>Response fields</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Field</th><th>Type</th></tr></thead><tbody><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>integer</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>finite float[]</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Errors and next steps</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Response</th><th>Condition</th><th>Next step</th></tr></thead><tbody><tr><th data-cell-label=\"Response\" scope=\"row\"><span class=\"card-cell-label\">Response</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Condition\"><span class=\"card-cell-label\">Condition</span>Invalid required fields, material representation or parameter value</td><td data-cell-label=\"Next step\"><span class=\"card-cell-label\">Next step</span>Correct the indicated fields before a new creation request.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Aliases</h3><h4>Model aliases</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;route=Ultra&amp;caller=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;fmt=embeddings\"><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code></a></span></p><h4>Method path aliases</h4><p class=\"note-s\">Use the method paths shown above.</p><h4>Related caller IDs</h4><p class=\"note-s\"><a href=\"#m=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;route=Ultra&amp;caller=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;fmt=embeddings\"><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "route_label": "Ultra"
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      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Invalid required fields, material representation or parameter value",
                "http_status": 400,
                "recovery": "Correct the indicated fields before a new creation request.",
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            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
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              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "No embedding job endpoint is introduced."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
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              "format": "json"
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            "parameters": [
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                "enum": [
                  "nvidia/llama-nemotron-embed-vl-1b-v2:free"
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                "enum_known": true,
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                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
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                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
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                "enum_known": false,
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                "availability": "pass_through_metadata",
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                "default": null,
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                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
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                "availability": "source_interface",
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                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Keep every vector index and dimension.",
            "Multimodal model name does not admit image objects on this exact path."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
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            "input_shapes": [
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            "max_input_budget_units": 131072,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
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          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 131072,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
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              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
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                "public_unit_label": "/ 1K tokens",
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                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
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            ]
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        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2:free\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/llama-nemotron-embed-vl-1b-v2:free',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
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        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
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        "pricing_profiles": [
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            "dimensions": {
              "regime": "base"
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            "label": "Base token rates",
            "units": [
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                "billing_input": true,
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                "billing_input": true,
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                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
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        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
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            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
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            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
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    "description": "Llama Nemotron Embed VL 1B V2 is NVIDIA’s multimodal embedding model for retrieval. It turns text, images or combined document inputs into vectors and supports text-query search over documents containing tables, charts, text and infographics.",
    "locale": "en",
    "md_url": "https://ailoot.ai/models/en/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/index.md",
    "title": "NVIDIA: Llama Nemotron Embed VL 1B V2 (free) — Ailoot",
    "url": "https://ailoot.ai/models/en/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/"
  },
  "rawModel": {
    "about": "Llama Nemotron Embed VL 1B V2 is NVIDIA’s embedding model for multimodal question-answering retrieval. It can represent a document as text, an image or a combination of image and text. A text query can then be used to retrieve related documents. Its published profile supports document images containing text, tables, charts and infographics, and returns embedding vectors.\n\n### Prepare identifiable document items\n\nKeep each input connected to the source document, page or passage it represents. For a visual page, preserve the image and any accompanying text that belongs to it. For a text passage, retain its title or section label when that helps interpret a later match. These source identifiers make a retrieved item useful as evidence rather than only as a numeric similarity result.\n\nChoose item boundaries around the material a user needs to find. A chart and its explanation may belong together; unrelated pages should retain their separate identities. Review whether the input is readable and whether the text or image contains the information the intended query is meant to retrieve.\n\n### Text queries and vector retrieval\n\nThe model’s role is to produce vectors for retrieval, with text as the query form described by the profile. Keep the query and indexed items in a compatible embedding setup. The application stores the vectors, compares them and returns the corresponding source items. The retrieved document remains the material to inspect or pass to a later answer-generation step.\n\nA useful test set includes the questions users actually ask and the pages that should be found. Review wrong or missing matches against the source content and item boundaries. Retain the full document identifier with any selected passage or image so the result can be checked in context.\n\n### Input allowance and the search artifact\n\nThe documented hosted profile lists up to 131,072 tokens per input. Split larger source sets into separate, labeled inputs and preserve their relationships. The profile’s vector output is not a generated prose answer or a transcript; a later application stage can use the retrieved evidence to answer a question.\n\nA completed retrieval artifact joins the query, selected source items and their identifiers. Preserve the embedding model and input preparation used for an index, so later changes can be distinguished from changes in the source collection.",
    "alias_of": null,
    "aliases": [],
    "author": "NVIDIA",
    "available": null,
    "canonical_model": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
    "capabilities": {
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      "manufacturer_input": [
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        "summary": "Full configuration pricing in details."
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    "methods": [
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        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
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                "condition": "Invalid required fields, material representation or parameter value",
                "http_status": 400,
                "recovery": "Correct the indicated fields before a new creation request.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "No embedding job endpoint is introduced."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nvidia/llama-nemotron-embed-vl-1b-v2:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 131072,
                  "text_unit": "UTF8 bytes",
                  "token_unit": "token-array elements"
                },
                "conditions": "Nonempty homogeneous batch. Token IDs are nonnegative integers.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rejected",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Keep every vector index and dimension.",
            "Multimodal model name does not admit image objects on this exact path."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 131072,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 131072,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2:free\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/llama-nemotron-embed-vl-1b-v2:free',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "route_label": "Ultra"
      }
    ],
    "name": "NVIDIA: Llama Nemotron Embed VL 1B V2 (free)",
    "price": {
      "in": 0,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0,
      "sort_out": 0,
      "varies": false
    },
    "pricing_profiles": [
      {
        "dimensions": {
          "regime": "base"
        },
        "label": "Эмбеддинги · Base token rates",
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "public_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
    "short": "Llama Nemotron Embed VL 1B V2 is NVIDIA’s multimodal embedding model for retrieval. It turns text, images or combined document inputs into vectors and supports text-query search over documents containing tables, charts, text and infographics."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
        {
          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Llama Nemotron Embed VL 1B V2 is NVIDIA’s multimodal embedding model for retrieval. It turns text, images or combined document inputs into vectors and supports text-query search over documents containing tables, charts, text and infographics.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "c5ea338e763c2c86e0d7254da50ce26fc64bdcbbe09fda8bdd9b6d429e1d79a9",
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