Amazon Nova Micro is Amazon’s text-only first-generation Nova model, designed for low-latency responses in routine language workflows. It accepts text and returns text. Amazon’s examples include summarising supplied information, translation, classifying text, answering questions and producing concise written content. Micro is useful when an application has already collected the facts and needs a focused transformation, such as turning product attributes into a catalog paragraph or converting meeting notes into an action list.
The first-generation Nova guide lists a 128K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. This is a smaller working context than the 300K windows of Nova Lite and Pro or the 1 million-token window of Premier. Use the available space for the text the answer actually depends on: source passages, examples and relevant conversation. Specify whether the desired artifact is a short reply, a category label, a translation or a set of extracted fields. A broad context allowance does not require a long answer; an explicit output length and structure keep the result useful.
Micro’s scope is text rather than native image, video or audio understanding. For information originally stored in a document or media file, supply its extracted text or transcript. A product-content brief can contain the product name, dimensions, materials, variants and a required style, then ask for one paragraph using only those attributes. An original example would request a neutral description and a separate list of missing specifications. This uses the model as a writer and transformer of supplied evidence rather than asking it to infer unseen product features.
Amazon documents system, user and assistant roles for Nova text prompts. A system instruction can establish the writing role and recurring constraints; the user message supplies the task and source material; a few representative examples can show the desired format. Structure dense inputs with clear labels, and break compound instructions into separate steps if the model struggles with their dependencies. For translation, state the target language, whether to preserve proper names and which terms must remain unchanged. The version1 family is trained for over 200 languages, with optimisation for a smaller set, so do not assume identical quality in every dialect.
Micro supports response streaming and client-side tool calling. 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. In a grounded assistant, an application can retrieve the relevant text or execute a requested function, then send the observed result back for a final answer. The model itself is not a live information-retrieval engine. When a response depends on a current policy or a private knowledge base, include the actual relevant source. A requested JSON format can be useful for classification or extraction, but the producer profile does not list enforced structured output as a supported feature; specify missing-value behaviour and check the returned fields.
A further first-generation role is customisation: Amazon documents fine-tuning Micro on labelled examples and distilling task-specific knowledge into it from Nova Pro or Premier. A customised derivative is a separate workflow, not a capability that silently changes the base edition. For the ordinary model, judge the completed artifact against its brief: the supplied facts should remain intact, important attributes should not disappear, and the prose should match the requested style. Micro can also generate code, but those completions do not include an automatic security scan or execution result.
", "aboutText": "Amazon Nova Micro is Amazon’s text-only first-generation Nova model, designed for low-latency responses in routine language workflows. It accepts text and returns text. Amazon’s examples include summarising supplied information, translation, classifying text, answering questions and producing concise written content. Micro is useful when an application has already collected the facts and needs a focused transformation, such as turning product attributes into a catalog paragraph or converting meeting notes into an action list.\n\nThe first-generation Nova guide lists a 128K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. This is a smaller working context than the 300K windows of Nova Lite and Pro or the 1 million-token window of Premier. Use the available space for the text the answer actually depends on: source passages, examples and relevant conversation. Specify whether the desired artifact is a short reply, a category label, a translation or a set of extracted fields. A broad context allowance does not require a long answer; an explicit output length and structure keep the result useful.\n\nMicro’s scope is text rather than native image, video or audio understanding. For information originally stored in a document or media file, supply its extracted text or transcript. A product-content brief can contain the product name, dimensions, materials, variants and a required style, then ask for one paragraph using only those attributes. An original example would request a neutral description and a separate list of missing specifications. This uses the model as a writer and transformer of supplied evidence rather than asking it to infer unseen product features.\n\nAmazon documents system, user and assistant roles for Nova text prompts. A system instruction can establish the writing role and recurring constraints; the user message supplies the task and source material; a few representative examples can show the desired format. Structure dense inputs with clear labels, and break compound instructions into separate steps if the model struggles with their dependencies. For translation, state the target language, whether to preserve proper names and which terms must remain unchanged. The version1 family is trained for over 200 languages, with optimisation for a smaller set, so do not assume identical quality in every dialect.\n\nMicro supports response streaming and client-side tool calling. 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. In a grounded assistant, an application can retrieve the relevant text or execute a requested function, then send the observed result back for a final answer. The model itself is not a live information-retrieval engine. When a response depends on a current policy or a private knowledge base, include the actual relevant source. A requested JSON format can be useful for classification or extraction, but the producer profile does not list enforced structured output as a supported feature; specify missing-value behaviour and check the returned fields.\n\nA further first-generation role is customisation: Amazon documents fine-tuning Micro on labelled examples and distilling task-specific knowledge into it from Nova Pro or Premier. A customised derivative is a separate workflow, not a capability that silently changes the base edition. For the ordinary model, judge the completed artifact against its brief: the supplied facts should remain intact, important attributes should not disappear, and the prose should match the requested style. Micro can also generate code, but those completions do not include an automatic security scan or execution result.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Product copy from supplied specifications\n\nTurn these attributes into a neutral 45-word catalog paragraph: folding desk, oak veneer, steel frame, 90 × 50 cm top, two height positions, flat-pack delivery. Follow it with missing specifications a buyer might need. Use only the supplied attributes and avoid claims about durability, sustainability or maximum load.One short product paragraph followed by a separate missing-specifications list.Every stated product feature is in the supplied attributes; unknown load capacity stays in the missing list.amazon/nova-micro-v1Amazon Nova Micro is the text-only model in the first-generation Nova family. It is designed for low-latency language tasks such as summaries, translation, classification, question answering and turning supplied facts into concise content.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Amazon Nova Micro is Amazon’s text-only first-generation Nova model, designed for low-latency responses in routine language workflows. It accepts text and returns text. Amazon’s examples include summarising supplied information, translation, classifying text, answering questions and producing concise written content. Micro is useful when an application has already collected the facts and needs a focused transformation, such as turning product attributes into a catalog paragraph or converting meeting notes into an action list.
The first-generation Nova guide lists a 128K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. This is a smaller working context than the 300K windows of Nova Lite and Pro or the 1 million-token window of Premier. Use the available space for the text the answer actually depends on: source passages, examples and relevant conversation. Specify whether the desired artifact is a short reply, a category label, a translation or a set of extracted fields. A broad context allowance does not require a long answer; an explicit output length and structure keep the result useful.
Micro’s scope is text rather than native image, video or audio understanding. For information originally stored in a document or media file, supply its extracted text or transcript. A product-content brief can contain the product name, dimensions, materials, variants and a required style, then ask for one paragraph using only those attributes. An original example would request a neutral description and a separate list of missing specifications. This uses the model as a writer and transformer of supplied evidence rather than asking it to infer unseen product features.
Amazon documents system, user and assistant roles for Nova text prompts. A system instruction can establish the writing role and recurring constraints; the user message supplies the task and source material; a few representative examples can show the desired format. Structure dense inputs with clear labels, and break compound instructions into separate steps if the model struggles with their dependencies. For translation, state the target language, whether to preserve proper names and which terms must remain unchanged. The version1 family is trained for over 200 languages, with optimisation for a smaller set, so do not assume identical quality in every dialect.
Micro supports response streaming and client-side tool calling. 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. In a grounded assistant, an application can retrieve the relevant text or execute a requested function, then send the observed result back for a final answer. The model itself is not a live information-retrieval engine. When a response depends on a current policy or a private knowledge base, include the actual relevant source. A requested JSON format can be useful for classification or extraction, but the producer profile does not list enforced structured output as a supported feature; specify missing-value behaviour and check the returned fields.
A further first-generation role is customisation: Amazon documents fine-tuning Micro on labelled examples and distilling task-specific knowledge into it from Nova Pro or Premier. A customised derivative is a separate workflow, not a capability that silently changes the base edition. For the ordinary model, judge the completed artifact against its brief: the supplied facts should remain intact, important attributes should not disappear, and the prose should match the requested style. Micro can also generate code, but those completions do not include an automatic security scan or execution result.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits amazon/nova-micro-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 $0.00007 / 1K tokens Output · Output tokens $0.00028 / 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 $0.00007 / 1K tokens Output · Output tokens $0.00028 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "amazon/nova-micro-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-micro-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-micro-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-micro-v1
Use the method paths shown above.
amazon/nova-micro-v1Amazon Nova Micro is the text-only model in the first-generation Nova family. It is designed for low-latency language tasks such as summaries, translation, classification, question answering and turning supplied facts into concise content.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Amazon Nova Micro is Amazon’s text-only first-generation Nova model, designed for low-latency responses in routine language workflows. It accepts text and returns text. Amazon’s examples include summarising supplied information, translation, classifying text, answering questions and producing concise written content. Micro is useful when an application has already collected the facts and needs a focused transformation, such as turning product attributes into a catalog paragraph or converting meeting notes into an action list.
The first-generation Nova guide lists a 128K-token context window and a maximum output of 10K tokens. Its documented knowledge cutoff is October 2024. This is a smaller working context than the 300K windows of Nova Lite and Pro or the 1 million-token window of Premier. Use the available space for the text the answer actually depends on: source passages, examples and relevant conversation. Specify whether the desired artifact is a short reply, a category label, a translation or a set of extracted fields. A broad context allowance does not require a long answer; an explicit output length and structure keep the result useful.
Micro’s scope is text rather than native image, video or audio understanding. For information originally stored in a document or media file, supply its extracted text or transcript. A product-content brief can contain the product name, dimensions, materials, variants and a required style, then ask for one paragraph using only those attributes. An original example would request a neutral description and a separate list of missing specifications. This uses the model as a writer and transformer of supplied evidence rather than asking it to infer unseen product features.
Amazon documents system, user and assistant roles for Nova text prompts. A system instruction can establish the writing role and recurring constraints; the user message supplies the task and source material; a few representative examples can show the desired format. Structure dense inputs with clear labels, and break compound instructions into separate steps if the model struggles with their dependencies. For translation, state the target language, whether to preserve proper names and which terms must remain unchanged. The version1 family is trained for over 200 languages, with optimisation for a smaller set, so do not assume identical quality in every dialect.
Micro supports response streaming and client-side tool calling. 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. In a grounded assistant, an application can retrieve the relevant text or execute a requested function, then send the observed result back for a final answer. The model itself is not a live information-retrieval engine. When a response depends on a current policy or a private knowledge base, include the actual relevant source. A requested JSON format can be useful for classification or extraction, but the producer profile does not list enforced structured output as a supported feature; specify missing-value behaviour and check the returned fields.
A further first-generation role is customisation: Amazon documents fine-tuning Micro on labelled examples and distilling task-specific knowledge into it from Nova Pro or Premier. A customised derivative is a separate workflow, not a capability that silently changes the base edition. For the ordinary model, judge the completed artifact against its brief: the supplied facts should remain intact, important attributes should not disappear, and the prose should match the requested style. Micro can also generate code, but those completions do not include an automatic security scan or execution result.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits amazon/nova-micro-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 $0.00007 / 1K tokens Output · Output tokens $0.00028 / 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 $0.00007 / 1K tokens Output · Output tokens $0.00028 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "amazon/nova-micro-v1",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-micro-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-micro-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-micro-v1
Use the method paths shown above.
Technical example in English
Product copy from supplied specifications\n\nTurn these attributes into a neutral 45-word catalog paragraph: folding desk, oak veneer, steel frame, 90 × 50 cm top, two height positions, flat-pack delivery. Follow it with missing specifications a buyer might need. Use only the supplied attributes and avoid claims about durability, sustainability or maximum load.One short product paragraph followed by a separate missing-specifications list.Every stated product feature is in the supplied attributes; unknown load capacity stays in the missing list.