Amazon Nova Lite is the lighter multimodal understanding model in the first-generation Nova family. It processes text, images and video while returning text, with a focus on quick visual question answering, document interpretation, summarisation and interactive assistance. It is a useful fit for a bounded task such as describing a screenshot, extracting product attributes from a document or answering a question about a clip. This is the original Nova Lite v1 edition, distinct from Nova 2 Lite and its extended-thinking controls.
The first-generation Nova guide lists a 300K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. Use the context for source material and examples, then reserve the answer for the information the downstream task needs: named fields, a concise summary or a table. For extraction, provide a schema and one complete example that shows how to represent absent values. The model can be instructed to return JSON or another textual structure, but a format request is not the same as a producer feature that guarantees schema-constrained output.
Lite’s vision workflow can answer questions about, classify and summarise images. Amazon specifically documents bounding-box detection for Nova Lite and Pro, with coordinates on a [0, 1000) scale that can be converted to the original image dimensions. This can support locating a requested object or screen element, while the application is responsible for interpreting the coordinates and performing any action. Input images are automatically rescaled and the image guide lists a maximum resolution of 8,000 × 8,000 pixels. Keep small labels readable and preserve surrounding context when choosing a crop.
The documented image formats are PNG, JPG/JPEG, GIF and WebP. The document workflow supports PDF, DOC/DOCX, CSV, XLS/XLSX, HTML, TXT and Markdown. Text-based files are processed for their text, whereas PDF and DOCX can combine page text and visuals such as charts or tables. The producer document guide permits up to five documents and limits individual text documents to 4.5 MB; it excludes PDFs containing CMYK or SVG images and lists JPEG2000 and JBIG2 as unsupported PDF encodings. A practical brief identifies the pages or sections to compare and asks for values together with their source labels. Embedded media shares a 25 MB payload budget, including base64 overhead. A request contains one video, limited to 1 GB through S3; combined S3 media can total 2 GB. PDF and DOCX inputs remain limited to five documents, with a combined allowance of 25 MB for direct upload or 2 GB through S3.
Video formats include MP4, MOV, MKV, WebM, FLV, MPEG, MPG, WMV and 3GP. For Nova Lite, clips of 16 minutes or less use one sampled frame per second; longer clips use 960 frames spaced over their duration. A longer clip therefore provides less fine-grained temporal detail. The guide recommends under one hour for low-motion material and under 16 minutes for higher-motion material. Those are preparation recommendations rather than universal duration guarantees. Ask about scenes or visible actions, and include a separate transcript when the spoken track carries facts the answer needs. The producer also states that this model does not identify or name people in images, documents or videos.
Nova Lite supports response streaming, client-executed tool calls and retrieval-augmented workflows. It also supports prompt caching in Amazon’s documented serving profile. Explicit caching requires at least 1K tokens per checkpoint and permits up to four checkpoints with five-minute retention. The producer limits prompt caching to 20K tokens, primarily for text prompts; that cache allowance is distinct from the full context window. Supply relevant reference passages, clear tool descriptions and actual tool results for an answer that depends on current information. The first-generation guide describes multilingual support across more than 200 languages, with stronger optimisation in 15 languages including English, German, French, Spanish, Japanese, Arabic, Simplified Chinese and Russian; coverage is not uniform across every dialect or visual text. For a lightweight assistant, separate extraction from interpretation when the instructions become complex, and state the required output language, fields and stopping condition.
", "aboutText": "Amazon Nova Lite is the lighter multimodal understanding model in the first-generation Nova family. It processes text, images and video while returning text, with a focus on quick visual question answering, document interpretation, summarisation and interactive assistance. It is a useful fit for a bounded task such as describing a screenshot, extracting product attributes from a document or answering a question about a clip. This is the original Nova Lite v1 edition, distinct from Nova 2 Lite and its extended-thinking controls.\n\nThe first-generation Nova guide lists a 300K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. Use the context for source material and examples, then reserve the answer for the information the downstream task needs: named fields, a concise summary or a table. For extraction, provide a schema and one complete example that shows how to represent absent values. The model can be instructed to return JSON or another textual structure, but a format request is not the same as a producer feature that guarantees schema-constrained output.\n\nLite’s vision workflow can answer questions about, classify and summarise images. Amazon specifically documents bounding-box detection for Nova Lite and Pro, with coordinates on a [0, 1000) scale that can be converted to the original image dimensions. This can support locating a requested object or screen element, while the application is responsible for interpreting the coordinates and performing any action. Input images are automatically rescaled and the image guide lists a maximum resolution of 8,000 × 8,000 pixels. Keep small labels readable and preserve surrounding context when choosing a crop.\n\nThe documented image formats are PNG, JPG/JPEG, GIF and WebP. The document workflow supports PDF, DOC/DOCX, CSV, XLS/XLSX, HTML, TXT and Markdown. Text-based files are processed for their text, whereas PDF and DOCX can combine page text and visuals such as charts or tables. The producer document guide permits up to five documents and limits individual text documents to 4.5 MB; it excludes PDFs containing CMYK or SVG images and lists JPEG2000 and JBIG2 as unsupported PDF encodings. A practical brief identifies the pages or sections to compare and asks for values together with their source labels. Embedded media shares a 25 MB payload budget, including base64 overhead. A request contains one video, limited to 1 GB through S3; combined S3 media can total 2 GB. PDF and DOCX inputs remain limited to five documents, with a combined allowance of 25 MB for direct upload or 2 GB through S3.\n\nVideo formats include MP4, MOV, MKV, WebM, FLV, MPEG, MPG, WMV and 3GP. For Nova Lite, clips of 16 minutes or less use one sampled frame per second; longer clips use 960 frames spaced over their duration. A longer clip therefore provides less fine-grained temporal detail. The guide recommends under one hour for low-motion material and under 16 minutes for higher-motion material. Those are preparation recommendations rather than universal duration guarantees. Ask about scenes or visible actions, and include a separate transcript when the spoken track carries facts the answer needs. The producer also states that this model does not identify or name people in images, documents or videos.\n\nNova Lite supports response streaming, client-executed tool calls and retrieval-augmented workflows. It also supports prompt caching in Amazon’s documented serving profile. Explicit caching requires at least 1K tokens per checkpoint and permits up to four checkpoints with five-minute retention. The producer limits prompt caching to 20K tokens, primarily for text prompts; that cache allowance is distinct from the full context window. Supply relevant reference passages, clear tool descriptions and actual tool results for an answer that depends on current information. The first-generation guide describes multilingual support across more than 200 languages, with stronger optimisation in 15 languages including English, German, French, Spanish, Japanese, Arabic, Simplified Chinese and Russian; coverage is not uniform across every dialect or visual text. For a lightweight assistant, separate extraction from interpretation when the instructions become complex, and state the required output language, fields and stopping condition.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Extract product fields from a note\n\nExtract a product record from this supplied text: "Desk lamp L-14; matte black; USB-C power; cable included; dimensions not stated." Return a table with model, color, power, included items and dimensions. Write "not stated" for missing information, then give a one-sentence catalogue summary using only those fields.A five-field table followed by one catalogue sentence; dimensions are marked not stated.Verify that no dimensions, wattage or battery are added to the note.amazon/nova-lite-v1Amazon Nova Lite is a first-generation multimodal understanding model for fast analysis of text, images, documents and video. It returns text and suits visual question answering, information extraction, summaries and lightweight tool-driven workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Amazon Nova Lite is the lighter multimodal understanding model in the first-generation Nova family. It processes text, images and video while returning text, with a focus on quick visual question answering, document interpretation, summarisation and interactive assistance. It is a useful fit for a bounded task such as describing a screenshot, extracting product attributes from a document or answering a question about a clip. This is the original Nova Lite v1 edition, distinct from Nova 2 Lite and its extended-thinking controls.
The first-generation Nova guide lists a 300K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. Use the context for source material and examples, then reserve the answer for the information the downstream task needs: named fields, a concise summary or a table. For extraction, provide a schema and one complete example that shows how to represent absent values. The model can be instructed to return JSON or another textual structure, but a format request is not the same as a producer feature that guarantees schema-constrained output.
Lite’s vision workflow can answer questions about, classify and summarise images. Amazon specifically documents bounding-box detection for Nova Lite and Pro, with coordinates on a [0, 1000) scale that can be converted to the original image dimensions. This can support locating a requested object or screen element, while the application is responsible for interpreting the coordinates and performing any action. Input images are automatically rescaled and the image guide lists a maximum resolution of 8,000 × 8,000 pixels. Keep small labels readable and preserve surrounding context when choosing a crop.
The documented image formats are PNG, JPG/JPEG, GIF and WebP. The document workflow supports PDF, DOC/DOCX, CSV, XLS/XLSX, HTML, TXT and Markdown. Text-based files are processed for their text, whereas PDF and DOCX can combine page text and visuals such as charts or tables. The producer document guide permits up to five documents and limits individual text documents to 4.5 MB; it excludes PDFs containing CMYK or SVG images and lists JPEG2000 and JBIG2 as unsupported PDF encodings. A practical brief identifies the pages or sections to compare and asks for values together with their source labels. Embedded media shares a 25 MB payload budget, including base64 overhead. A request contains one video, limited to 1 GB through S3; combined S3 media can total 2 GB. PDF and DOCX inputs remain limited to five documents, with a combined allowance of 25 MB for direct upload or 2 GB through S3.
Video formats include MP4, MOV, MKV, WebM, FLV, MPEG, MPG, WMV and 3GP. For Nova Lite, clips of 16 minutes or less use one sampled frame per second; longer clips use 960 frames spaced over their duration. A longer clip therefore provides less fine-grained temporal detail. The guide recommends under one hour for low-motion material and under 16 minutes for higher-motion material. Those are preparation recommendations rather than universal duration guarantees. Ask about scenes or visible actions, and include a separate transcript when the spoken track carries facts the answer needs. The producer also states that this model does not identify or name people in images, documents or videos.
Nova Lite supports response streaming, client-executed tool calls and retrieval-augmented workflows. It also supports prompt caching in Amazon’s documented serving profile. Explicit caching requires at least 1K tokens per checkpoint and permits up to four checkpoints with five-minute retention. The producer limits prompt caching to 20K tokens, primarily for text prompts; that cache allowance is distinct from the full context window. Supply relevant reference passages, clear tool descriptions and actual tool results for an answer that depends on current information. The first-generation guide describes multilingual support across more than 200 languages, with stronger optimisation in 15 languages including English, German, French, Spanish, Japanese, Arabic, Simplified Chinese and Russian; coverage is not uniform across every dialect or visual text. For a lightweight assistant, separate extraction from interpretation when the instructions become complex, and state the required output language, fields and stopping condition.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits amazon/nova-lite-v1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00012 / 1K tokens Output · Output tokens $0.00048 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00012 / 1K tokens Output · Output tokens $0.00048 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "amazon/nova-lite-v1", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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/chat/completions \\\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("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='amazon/nova-lite-v1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='amazon/nova-lite-v1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra amazon/nova-lite-v1
Use the method paths shown above.
amazon/nova-lite-v1Amazon Nova Lite is a first-generation multimodal understanding model for fast analysis of text, images, documents and video. It returns text and suits visual question answering, information extraction, summaries and lightweight tool-driven workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Amazon Nova Lite is the lighter multimodal understanding model in the first-generation Nova family. It processes text, images and video while returning text, with a focus on quick visual question answering, document interpretation, summarisation and interactive assistance. It is a useful fit for a bounded task such as describing a screenshot, extracting product attributes from a document or answering a question about a clip. This is the original Nova Lite v1 edition, distinct from Nova 2 Lite and its extended-thinking controls.
The first-generation Nova guide lists a 300K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. Use the context for source material and examples, then reserve the answer for the information the downstream task needs: named fields, a concise summary or a table. For extraction, provide a schema and one complete example that shows how to represent absent values. The model can be instructed to return JSON or another textual structure, but a format request is not the same as a producer feature that guarantees schema-constrained output.
Lite’s vision workflow can answer questions about, classify and summarise images. Amazon specifically documents bounding-box detection for Nova Lite and Pro, with coordinates on a [0, 1000) scale that can be converted to the original image dimensions. This can support locating a requested object or screen element, while the application is responsible for interpreting the coordinates and performing any action. Input images are automatically rescaled and the image guide lists a maximum resolution of 8,000 × 8,000 pixels. Keep small labels readable and preserve surrounding context when choosing a crop.
The documented image formats are PNG, JPG/JPEG, GIF and WebP. The document workflow supports PDF, DOC/DOCX, CSV, XLS/XLSX, HTML, TXT and Markdown. Text-based files are processed for their text, whereas PDF and DOCX can combine page text and visuals such as charts or tables. The producer document guide permits up to five documents and limits individual text documents to 4.5 MB; it excludes PDFs containing CMYK or SVG images and lists JPEG2000 and JBIG2 as unsupported PDF encodings. A practical brief identifies the pages or sections to compare and asks for values together with their source labels. Embedded media shares a 25 MB payload budget, including base64 overhead. A request contains one video, limited to 1 GB through S3; combined S3 media can total 2 GB. PDF and DOCX inputs remain limited to five documents, with a combined allowance of 25 MB for direct upload or 2 GB through S3.
Video formats include MP4, MOV, MKV, WebM, FLV, MPEG, MPG, WMV and 3GP. For Nova Lite, clips of 16 minutes or less use one sampled frame per second; longer clips use 960 frames spaced over their duration. A longer clip therefore provides less fine-grained temporal detail. The guide recommends under one hour for low-motion material and under 16 minutes for higher-motion material. Those are preparation recommendations rather than universal duration guarantees. Ask about scenes or visible actions, and include a separate transcript when the spoken track carries facts the answer needs. The producer also states that this model does not identify or name people in images, documents or videos.
Nova Lite supports response streaming, client-executed tool calls and retrieval-augmented workflows. It also supports prompt caching in Amazon’s documented serving profile. Explicit caching requires at least 1K tokens per checkpoint and permits up to four checkpoints with five-minute retention. The producer limits prompt caching to 20K tokens, primarily for text prompts; that cache allowance is distinct from the full context window. Supply relevant reference passages, clear tool descriptions and actual tool results for an answer that depends on current information. The first-generation guide describes multilingual support across more than 200 languages, with stronger optimisation in 15 languages including English, German, French, Spanish, Japanese, Arabic, Simplified Chinese and Russian; coverage is not uniform across every dialect or visual text. For a lightweight assistant, separate extraction from interpretation when the instructions become complex, and state the required output language, fields and stopping condition.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits amazon/nova-lite-v1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00012 / 1K tokens Output · Output tokens $0.00048 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00012 / 1K tokens Output · Output tokens $0.00048 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "amazon/nova-lite-v1",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'amazon/nova-lite-v1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'amazon/nova-lite-v1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra amazon/nova-lite-v1
Use the method paths shown above.
Technical example in English
Extract product fields from a note\n\nExtract a product record from this supplied text: "Desk lamp L-14; matte black; USB-C power; cable included; dimensions not stated." Return a table with model, color, power, included items and dimensions. Write "not stated" for missing information, then give a one-sentence catalogue summary using only those fields.A five-field table followed by one catalogue sentence; dimensions are marked not stated.Verify that no dimensions, wattage or battery are added to the note.