Claude Haiku 5.5 is Anthropic’s small-model edition for high-volume, latency-sensitive tasks. Its main uses include classification, routing, information extraction, summarisation, conversation compaction and focused subagent work. It accepts text and images and generates text. A useful brief gives it a narrow question and an explicit result: a category, a defined set of fields, a short summary or the relevant evidence for another part of a larger task. Keep the source identifiers with the result so an application can connect the extracted information to its original material.
Anthropic positions this edition for live customer support, browser use and short coding subtasks as well as repetitive analysis. It can complement Claude Sonnet 5.5 or Claude Opus 5.5 in a workflow where a larger model handles an ambitious project and Haiku handles a bounded lookup or summary. For a support task, supply the question and the permitted reference material, then specify the response tone and what must be returned. For a coding subtask, identify the relevant files or excerpts and the exact question, rather than assigning an open-ended project without a completion condition.
The documented context window is one million tokens, with up to 128,000 output tokens in ordinary generation. Thinking tokens count towards the generation allowance, so the result needs room for both reasoning and visible text. Its reliable knowledge cutoff is June 2026; later information needs supplied evidence or a search tool. Anthropic separately documents a batch-only beta that can raise output to 300,000 tokens for this edition. That larger allowance requires the producer’s extended-output feature and is distinct from the ordinary limit. An asynchronous batch handles requests independently, making it useful for collections of documents or records whose results are needed after processing rather than immediately.
Adaptive thinking is enabled by default, and effort is the main control for reasoning depth. The levels are low, medium, high, xhigh and max, with medium as the default. Anthropic recommends medium for most work, high for more sustained tasks or strict instruction following, and low for simpler, short requests. Thinking can be disabled at low, medium or high; disabling it at xhigh or max is rejected. A prompt asking for a direct answer does not substitute for that control. Manual budget_tokens settings from Haiku 4.5 do not transfer to this edition, and temperature, top_p and top_k should be omitted in the documented interface.
Haiku 5.5 uses the newer tokenizer introduced in earlier Claude generations, and Anthropic estimates that the same text can use roughly 30% more tokens than on Haiku 4.5, depending on its content. It also retains thinking blocks from earlier assistant turns, so conversation size can grow beyond the tokenizer change alone. Its minimum cacheable prompt length is 512 tokens. Keep earlier turns unchanged when retaining reasoning, and put new instructions in the newest turn. Changing top-level effort invalidates the conversation’s message cache; the producer documents a separate beta for changing effort within adaptive conversations. None of these capabilities establishes a cache hit for a particular request.
For retrieval, the model’s prompting guide recommends supplying the current date with the search tool and keeping the original system and tool definitions stable. Give a fresh date in the newest turn if the conversation continues on a later day. State what must be found and preserve the returned sources before asking for a conclusion. For example, an illustrative task could ask for the documented behaviour of a specified software version, followed by a short explanation grounded in the retrieved passage. The application supplies the search tool and returns its actual results; the model’s proposed lookup is not evidence that the lookup happened.
When structured JSON output is combined with tools, the guide documents a specific interaction: with thinking disabled, Haiku can skip a necessary tool call. Anthropic recommends adaptive thinking for such requests, or separating the final JSON requirement from the tool step. Define the output fields and make clear that the final answer follows the tool results. A data-extraction brief can name the required fields and how to represent missing values, while a lookup brief can first request the evidence needed to fill them. Read response blocks by their type, since the answer need not be the first block and thinking text is omitted by default.
For longer agent prompts, Anthropic notes that low effort can stop early, skip a search or omit a check. Its guide recommends explicit completion instructions and real checks for code changes before they are reported as finished. Prepare the expected result and the evidence that would demonstrate it: the selected passages for a report, the changed behaviour for a code proposal or the fields extracted from a document. The guide also distinguishes a user’s mid-task message from tool output: new user instructions belong in a user turn, while the returned tool evidence remains separate.
This edition has its own safeguards and can return a refusal for categories including cyber harm, biological harm, competing-model development or other prohibited uses. The producer does not document server-side fallback for Haiku 5.5. Its guide also notes that xhigh effort can occasionally end a multi-turn response without visible answer text, and that reasoning-like text can appear more often at low effort or with thinking disabled. These are specific behaviours to account for when a workflow requires a visible, structured result. For ordinary extraction or summary work, use a focused source, a defined output and the effort level appropriate to the task.
", "aboutText": "Claude Haiku 5.5 is Anthropic’s small-model edition for high-volume, latency-sensitive tasks. Its main uses include classification, routing, information extraction, summarisation, conversation compaction and focused subagent work. It accepts text and images and generates text. A useful brief gives it a narrow question and an explicit result: a category, a defined set of fields, a short summary or the relevant evidence for another part of a larger task. Keep the source identifiers with the result so an application can connect the extracted information to its original material.\n\nAnthropic positions this edition for live customer support, browser use and short coding subtasks as well as repetitive analysis. It can complement Claude Sonnet 5.5 or Claude Opus 5.5 in a workflow where a larger model handles an ambitious project and Haiku handles a bounded lookup or summary. For a support task, supply the question and the permitted reference material, then specify the response tone and what must be returned. For a coding subtask, identify the relevant files or excerpts and the exact question, rather than assigning an open-ended project without a completion condition.\n\nThe documented context window is one million tokens, with up to 128,000 output tokens in ordinary generation. Thinking tokens count towards the generation allowance, so the result needs room for both reasoning and visible text. Its reliable knowledge cutoff is June 2026; later information needs supplied evidence or a search tool. Anthropic separately documents a batch-only beta that can raise output to 300,000 tokens for this edition. That larger allowance requires the producer’s extended-output feature and is distinct from the ordinary limit. An asynchronous batch handles requests independently, making it useful for collections of documents or records whose results are needed after processing rather than immediately.\n\nAdaptive thinking is enabled by default, and effort is the main control for reasoning depth. The levels are low, medium, high, xhigh and max, with medium as the default. Anthropic recommends medium for most work, high for more sustained tasks or strict instruction following, and low for simpler, short requests. Thinking can be disabled at low, medium or high; disabling it at xhigh or max is rejected. A prompt asking for a direct answer does not substitute for that control. Manual budget_tokens settings from Haiku 4.5 do not transfer to this edition, and temperature, top_p and top_k should be omitted in the documented interface.\n\nHaiku 5.5 uses the newer tokenizer introduced in earlier Claude generations, and Anthropic estimates that the same text can use roughly 30% more tokens than on Haiku 4.5, depending on its content. It also retains thinking blocks from earlier assistant turns, so conversation size can grow beyond the tokenizer change alone. Its minimum cacheable prompt length is 512 tokens. Keep earlier turns unchanged when retaining reasoning, and put new instructions in the newest turn. Changing top-level effort invalidates the conversation’s message cache; the producer documents a separate beta for changing effort within adaptive conversations. None of these capabilities establishes a cache hit for a particular request.\n\nFor retrieval, the model’s prompting guide recommends supplying the current date with the search tool and keeping the original system and tool definitions stable. Give a fresh date in the newest turn if the conversation continues on a later day. State what must be found and preserve the returned sources before asking for a conclusion. For example, an illustrative task could ask for the documented behaviour of a specified software version, followed by a short explanation grounded in the retrieved passage. The application supplies the search tool and returns its actual results; the model’s proposed lookup is not evidence that the lookup happened.\n\nWhen structured JSON output is combined with tools, the guide documents a specific interaction: with thinking disabled, Haiku can skip a necessary tool call. Anthropic recommends adaptive thinking for such requests, or separating the final JSON requirement from the tool step. Define the output fields and make clear that the final answer follows the tool results. A data-extraction brief can name the required fields and how to represent missing values, while a lookup brief can first request the evidence needed to fill them. Read response blocks by their type, since the answer need not be the first block and thinking text is omitted by default.\n\nFor longer agent prompts, Anthropic notes that low effort can stop early, skip a search or omit a check. Its guide recommends explicit completion instructions and real checks for code changes before they are reported as finished. Prepare the expected result and the evidence that would demonstrate it: the selected passages for a report, the changed behaviour for a code proposal or the fields extracted from a document. The guide also distinguishes a user’s mid-task message from tool output: new user instructions belong in a user turn, while the returned tool evidence remains separate.\n\nThis edition has its own safeguards and can return a refusal for categories including cyber harm, biological harm, competing-model development or other prohibited uses. The producer does not document server-side fallback for Haiku 5.5. Its guide also notes that xhigh effort can occasionally end a multi-turn response without visible answer text, and that reasoning-like text can appear more often at low effort or with thinking disabled. These are specific behaviours to account for when a workflow requires a visible, structured result. For ordinary extraction or summary work, use a focused source, a defined output and the effort level appropriate to the task.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Extract a support handoff\n\nExtract a handoff from this ticket, using only its text: T-184: 'I changed my billing email yesterday. Receipts still go to the old address. My login works. Please resend March and April receipts.' Return ticket ID, requested actions, working function and missing information. Use 'not stated' for absent details.Four labeled fields retaining T-184, the receipt request, the working login and explicitly missing details.Every extracted value must trace to the ticket; do not invent either email address.anthropic/claude-haiku-5.5Claude Haiku 5.5 is Anthropic’s small model for classification, extraction, summaries, routing and focused agent tasks. It combines text and image understanding with adaptive reasoning and is shaped for repetitive work that needs a prompt response.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Haiku 5.5 is Anthropic’s small-model edition for high-volume, latency-sensitive tasks. Its main uses include classification, routing, information extraction, summarisation, conversation compaction and focused subagent work. It accepts text and images and generates text. A useful brief gives it a narrow question and an explicit result: a category, a defined set of fields, a short summary or the relevant evidence for another part of a larger task. Keep the source identifiers with the result so an application can connect the extracted information to its original material.
Anthropic positions this edition for live customer support, browser use and short coding subtasks as well as repetitive analysis. It can complement Claude Sonnet 5.5 or Claude Opus 5.5 in a workflow where a larger model handles an ambitious project and Haiku handles a bounded lookup or summary. For a support task, supply the question and the permitted reference material, then specify the response tone and what must be returned. For a coding subtask, identify the relevant files or excerpts and the exact question, rather than assigning an open-ended project without a completion condition.
The documented context window is one million tokens, with up to 128,000 output tokens in ordinary generation. Thinking tokens count towards the generation allowance, so the result needs room for both reasoning and visible text. Its reliable knowledge cutoff is June 2026; later information needs supplied evidence or a search tool. Anthropic separately documents a batch-only beta that can raise output to 300,000 tokens for this edition. That larger allowance requires the producer’s extended-output feature and is distinct from the ordinary limit. An asynchronous batch handles requests independently, making it useful for collections of documents or records whose results are needed after processing rather than immediately.
Adaptive thinking is enabled by default, and effort is the main control for reasoning depth. The levels are low, medium, high, xhigh and max, with medium as the default. Anthropic recommends medium for most work, high for more sustained tasks or strict instruction following, and low for simpler, short requests. Thinking can be disabled at low, medium or high; disabling it at xhigh or max is rejected. A prompt asking for a direct answer does not substitute for that control. Manual budget_tokens settings from Haiku 4.5 do not transfer to this edition, and temperature, top_p and top_k should be omitted in the documented interface.
Haiku 5.5 uses the newer tokenizer introduced in earlier Claude generations, and Anthropic estimates that the same text can use roughly 30% more tokens than on Haiku 4.5, depending on its content. It also retains thinking blocks from earlier assistant turns, so conversation size can grow beyond the tokenizer change alone. Its minimum cacheable prompt length is 512 tokens. Keep earlier turns unchanged when retaining reasoning, and put new instructions in the newest turn. Changing top-level effort invalidates the conversation’s message cache; the producer documents a separate beta for changing effort within adaptive conversations. None of these capabilities establishes a cache hit for a particular request.
For retrieval, the model’s prompting guide recommends supplying the current date with the search tool and keeping the original system and tool definitions stable. Give a fresh date in the newest turn if the conversation continues on a later day. State what must be found and preserve the returned sources before asking for a conclusion. For example, an illustrative task could ask for the documented behaviour of a specified software version, followed by a short explanation grounded in the retrieved passage. The application supplies the search tool and returns its actual results; the model’s proposed lookup is not evidence that the lookup happened.
When structured JSON output is combined with tools, the guide documents a specific interaction: with thinking disabled, Haiku can skip a necessary tool call. Anthropic recommends adaptive thinking for such requests, or separating the final JSON requirement from the tool step. Define the output fields and make clear that the final answer follows the tool results. A data-extraction brief can name the required fields and how to represent missing values, while a lookup brief can first request the evidence needed to fill them. Read response blocks by their type, since the answer need not be the first block and thinking text is omitted by default.
For longer agent prompts, Anthropic notes that low effort can stop early, skip a search or omit a check. Its guide recommends explicit completion instructions and real checks for code changes before they are reported as finished. Prepare the expected result and the evidence that would demonstrate it: the selected passages for a report, the changed behaviour for a code proposal or the fields extracted from a document. The guide also distinguishes a user’s mid-task message from tool output: new user instructions belong in a user turn, while the returned tool evidence remains separate.
This edition has its own safeguards and can return a refusal for categories including cyber harm, biological harm, competing-model development or other prohibited uses. The producer does not document server-side fallback for Haiku 5.5. Its guide also notes that xhigh effort can occasionally end a multi-turn response without visible answer text, and that reasoning-like text can appear more often at low effort or with thinking disabled. These are specific behaviours to account for when a workflow requires a visible, structured result. For ordinary extraction or summary work, use a focused source, a defined output and the effort level appropriate to the task.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits anthropic/claude-haiku-5.5 | 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 $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including100,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than100,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.002 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "anthropic/claude-haiku-5.5", "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='anthropic/claude-haiku-5.5',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='anthropic/claude-haiku-5.5',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 anthropic/claude-haiku-5.5, ~anthropic/claude-haiku-latest
Use the method paths shown above.
anthropic/claude-haiku-5.5Claude Haiku 5.5 is Anthropic’s small model for classification, extraction, summaries, routing and focused agent tasks. It combines text and image understanding with adaptive reasoning and is shaped for repetitive work that needs a prompt response.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Haiku 5.5 is Anthropic’s small-model edition for high-volume, latency-sensitive tasks. Its main uses include classification, routing, information extraction, summarisation, conversation compaction and focused subagent work. It accepts text and images and generates text. A useful brief gives it a narrow question and an explicit result: a category, a defined set of fields, a short summary or the relevant evidence for another part of a larger task. Keep the source identifiers with the result so an application can connect the extracted information to its original material.
Anthropic positions this edition for live customer support, browser use and short coding subtasks as well as repetitive analysis. It can complement Claude Sonnet 5.5 or Claude Opus 5.5 in a workflow where a larger model handles an ambitious project and Haiku handles a bounded lookup or summary. For a support task, supply the question and the permitted reference material, then specify the response tone and what must be returned. For a coding subtask, identify the relevant files or excerpts and the exact question, rather than assigning an open-ended project without a completion condition.
The documented context window is one million tokens, with up to 128,000 output tokens in ordinary generation. Thinking tokens count towards the generation allowance, so the result needs room for both reasoning and visible text. Its reliable knowledge cutoff is June 2026; later information needs supplied evidence or a search tool. Anthropic separately documents a batch-only beta that can raise output to 300,000 tokens for this edition. That larger allowance requires the producer’s extended-output feature and is distinct from the ordinary limit. An asynchronous batch handles requests independently, making it useful for collections of documents or records whose results are needed after processing rather than immediately.
Adaptive thinking is enabled by default, and effort is the main control for reasoning depth. The levels are low, medium, high, xhigh and max, with medium as the default. Anthropic recommends medium for most work, high for more sustained tasks or strict instruction following, and low for simpler, short requests. Thinking can be disabled at low, medium or high; disabling it at xhigh or max is rejected. A prompt asking for a direct answer does not substitute for that control. Manual budget_tokens settings from Haiku 4.5 do not transfer to this edition, and temperature, top_p and top_k should be omitted in the documented interface.
Haiku 5.5 uses the newer tokenizer introduced in earlier Claude generations, and Anthropic estimates that the same text can use roughly 30% more tokens than on Haiku 4.5, depending on its content. It also retains thinking blocks from earlier assistant turns, so conversation size can grow beyond the tokenizer change alone. Its minimum cacheable prompt length is 512 tokens. Keep earlier turns unchanged when retaining reasoning, and put new instructions in the newest turn. Changing top-level effort invalidates the conversation’s message cache; the producer documents a separate beta for changing effort within adaptive conversations. None of these capabilities establishes a cache hit for a particular request.
For retrieval, the model’s prompting guide recommends supplying the current date with the search tool and keeping the original system and tool definitions stable. Give a fresh date in the newest turn if the conversation continues on a later day. State what must be found and preserve the returned sources before asking for a conclusion. For example, an illustrative task could ask for the documented behaviour of a specified software version, followed by a short explanation grounded in the retrieved passage. The application supplies the search tool and returns its actual results; the model’s proposed lookup is not evidence that the lookup happened.
When structured JSON output is combined with tools, the guide documents a specific interaction: with thinking disabled, Haiku can skip a necessary tool call. Anthropic recommends adaptive thinking for such requests, or separating the final JSON requirement from the tool step. Define the output fields and make clear that the final answer follows the tool results. A data-extraction brief can name the required fields and how to represent missing values, while a lookup brief can first request the evidence needed to fill them. Read response blocks by their type, since the answer need not be the first block and thinking text is omitted by default.
For longer agent prompts, Anthropic notes that low effort can stop early, skip a search or omit a check. Its guide recommends explicit completion instructions and real checks for code changes before they are reported as finished. Prepare the expected result and the evidence that would demonstrate it: the selected passages for a report, the changed behaviour for a code proposal or the fields extracted from a document. The guide also distinguishes a user’s mid-task message from tool output: new user instructions belong in a user turn, while the returned tool evidence remains separate.
This edition has its own safeguards and can return a refusal for categories including cyber harm, biological harm, competing-model development or other prohibited uses. The producer does not document server-side fallback for Haiku 5.5. Its guide also notes that xhigh effort can occasionally end a multi-turn response without visible answer text, and that reasoning-like text can appear more often at low effort or with thinking disabled. These are specific behaviours to account for when a workflow requires a visible, structured result. For ordinary extraction or summary work, use a focused source, a defined output and the effort level appropriate to the task.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits anthropic/claude-haiku-5.5 | 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 $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including100,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than100,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.002 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "anthropic/claude-haiku-5.5",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'anthropic/claude-haiku-5.5', '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': 'anthropic/claude-haiku-5.5', '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 anthropic/claude-haiku-5.5, ~anthropic/claude-haiku-latest
Use the method paths shown above.
~anthropic/claude-haiku-latestClaude Haiku 5.5 is Anthropic’s small model for classification, extraction, summaries, routing and focused agent tasks. It combines text and image understanding with adaptive reasoning and is shaped for repetitive work that needs a prompt response.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Haiku 5.5 is Anthropic’s small-model edition for high-volume, latency-sensitive tasks. Its main uses include classification, routing, information extraction, summarisation, conversation compaction and focused subagent work. It accepts text and images and generates text. A useful brief gives it a narrow question and an explicit result: a category, a defined set of fields, a short summary or the relevant evidence for another part of a larger task. Keep the source identifiers with the result so an application can connect the extracted information to its original material.
Anthropic positions this edition for live customer support, browser use and short coding subtasks as well as repetitive analysis. It can complement Claude Sonnet 5.5 or Claude Opus 5.5 in a workflow where a larger model handles an ambitious project and Haiku handles a bounded lookup or summary. For a support task, supply the question and the permitted reference material, then specify the response tone and what must be returned. For a coding subtask, identify the relevant files or excerpts and the exact question, rather than assigning an open-ended project without a completion condition.
The documented context window is one million tokens, with up to 128,000 output tokens in ordinary generation. Thinking tokens count towards the generation allowance, so the result needs room for both reasoning and visible text. Its reliable knowledge cutoff is June 2026; later information needs supplied evidence or a search tool. Anthropic separately documents a batch-only beta that can raise output to 300,000 tokens for this edition. That larger allowance requires the producer’s extended-output feature and is distinct from the ordinary limit. An asynchronous batch handles requests independently, making it useful for collections of documents or records whose results are needed after processing rather than immediately.
Adaptive thinking is enabled by default, and effort is the main control for reasoning depth. The levels are low, medium, high, xhigh and max, with medium as the default. Anthropic recommends medium for most work, high for more sustained tasks or strict instruction following, and low for simpler, short requests. Thinking can be disabled at low, medium or high; disabling it at xhigh or max is rejected. A prompt asking for a direct answer does not substitute for that control. Manual budget_tokens settings from Haiku 4.5 do not transfer to this edition, and temperature, top_p and top_k should be omitted in the documented interface.
Haiku 5.5 uses the newer tokenizer introduced in earlier Claude generations, and Anthropic estimates that the same text can use roughly 30% more tokens than on Haiku 4.5, depending on its content. It also retains thinking blocks from earlier assistant turns, so conversation size can grow beyond the tokenizer change alone. Its minimum cacheable prompt length is 512 tokens. Keep earlier turns unchanged when retaining reasoning, and put new instructions in the newest turn. Changing top-level effort invalidates the conversation’s message cache; the producer documents a separate beta for changing effort within adaptive conversations. None of these capabilities establishes a cache hit for a particular request.
For retrieval, the model’s prompting guide recommends supplying the current date with the search tool and keeping the original system and tool definitions stable. Give a fresh date in the newest turn if the conversation continues on a later day. State what must be found and preserve the returned sources before asking for a conclusion. For example, an illustrative task could ask for the documented behaviour of a specified software version, followed by a short explanation grounded in the retrieved passage. The application supplies the search tool and returns its actual results; the model’s proposed lookup is not evidence that the lookup happened.
When structured JSON output is combined with tools, the guide documents a specific interaction: with thinking disabled, Haiku can skip a necessary tool call. Anthropic recommends adaptive thinking for such requests, or separating the final JSON requirement from the tool step. Define the output fields and make clear that the final answer follows the tool results. A data-extraction brief can name the required fields and how to represent missing values, while a lookup brief can first request the evidence needed to fill them. Read response blocks by their type, since the answer need not be the first block and thinking text is omitted by default.
For longer agent prompts, Anthropic notes that low effort can stop early, skip a search or omit a check. Its guide recommends explicit completion instructions and real checks for code changes before they are reported as finished. Prepare the expected result and the evidence that would demonstrate it: the selected passages for a report, the changed behaviour for a code proposal or the fields extracted from a document. The guide also distinguishes a user’s mid-task message from tool output: new user instructions belong in a user turn, while the returned tool evidence remains separate.
This edition has its own safeguards and can return a refusal for categories including cyber harm, biological harm, competing-model development or other prohibited uses. The producer does not document server-side fallback for Haiku 5.5. Its guide also notes that xhigh effort can occasionally end a multi-turn response without visible answer text, and that reasoning-like text can appear more often at low effort or with thinking disabled. These are specific behaviours to account for when a workflow requires a visible, structured result. For ordinary extraction or summary work, use a focused source, a defined output and the effort level appropriate to the task.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits ~anthropic/claude-haiku-latest | 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 $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including100,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than100,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.002 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "~anthropic/claude-haiku-latest", "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='~anthropic/claude-haiku-latest',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='~anthropic/claude-haiku-latest',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 anthropic/claude-haiku-5.5, ~anthropic/claude-haiku-latest
Use the method paths shown above.
~anthropic/claude-haiku-latestClaude Haiku 5.5 is Anthropic’s small model for classification, extraction, summaries, routing and focused agent tasks. It combines text and image understanding with adaptive reasoning and is shaped for repetitive work that needs a prompt response.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Haiku 5.5 is Anthropic’s small-model edition for high-volume, latency-sensitive tasks. Its main uses include classification, routing, information extraction, summarisation, conversation compaction and focused subagent work. It accepts text and images and generates text. A useful brief gives it a narrow question and an explicit result: a category, a defined set of fields, a short summary or the relevant evidence for another part of a larger task. Keep the source identifiers with the result so an application can connect the extracted information to its original material.
Anthropic positions this edition for live customer support, browser use and short coding subtasks as well as repetitive analysis. It can complement Claude Sonnet 5.5 or Claude Opus 5.5 in a workflow where a larger model handles an ambitious project and Haiku handles a bounded lookup or summary. For a support task, supply the question and the permitted reference material, then specify the response tone and what must be returned. For a coding subtask, identify the relevant files or excerpts and the exact question, rather than assigning an open-ended project without a completion condition.
The documented context window is one million tokens, with up to 128,000 output tokens in ordinary generation. Thinking tokens count towards the generation allowance, so the result needs room for both reasoning and visible text. Its reliable knowledge cutoff is June 2026; later information needs supplied evidence or a search tool. Anthropic separately documents a batch-only beta that can raise output to 300,000 tokens for this edition. That larger allowance requires the producer’s extended-output feature and is distinct from the ordinary limit. An asynchronous batch handles requests independently, making it useful for collections of documents or records whose results are needed after processing rather than immediately.
Adaptive thinking is enabled by default, and effort is the main control for reasoning depth. The levels are low, medium, high, xhigh and max, with medium as the default. Anthropic recommends medium for most work, high for more sustained tasks or strict instruction following, and low for simpler, short requests. Thinking can be disabled at low, medium or high; disabling it at xhigh or max is rejected. A prompt asking for a direct answer does not substitute for that control. Manual budget_tokens settings from Haiku 4.5 do not transfer to this edition, and temperature, top_p and top_k should be omitted in the documented interface.
Haiku 5.5 uses the newer tokenizer introduced in earlier Claude generations, and Anthropic estimates that the same text can use roughly 30% more tokens than on Haiku 4.5, depending on its content. It also retains thinking blocks from earlier assistant turns, so conversation size can grow beyond the tokenizer change alone. Its minimum cacheable prompt length is 512 tokens. Keep earlier turns unchanged when retaining reasoning, and put new instructions in the newest turn. Changing top-level effort invalidates the conversation’s message cache; the producer documents a separate beta for changing effort within adaptive conversations. None of these capabilities establishes a cache hit for a particular request.
For retrieval, the model’s prompting guide recommends supplying the current date with the search tool and keeping the original system and tool definitions stable. Give a fresh date in the newest turn if the conversation continues on a later day. State what must be found and preserve the returned sources before asking for a conclusion. For example, an illustrative task could ask for the documented behaviour of a specified software version, followed by a short explanation grounded in the retrieved passage. The application supplies the search tool and returns its actual results; the model’s proposed lookup is not evidence that the lookup happened.
When structured JSON output is combined with tools, the guide documents a specific interaction: with thinking disabled, Haiku can skip a necessary tool call. Anthropic recommends adaptive thinking for such requests, or separating the final JSON requirement from the tool step. Define the output fields and make clear that the final answer follows the tool results. A data-extraction brief can name the required fields and how to represent missing values, while a lookup brief can first request the evidence needed to fill them. Read response blocks by their type, since the answer need not be the first block and thinking text is omitted by default.
For longer agent prompts, Anthropic notes that low effort can stop early, skip a search or omit a check. Its guide recommends explicit completion instructions and real checks for code changes before they are reported as finished. Prepare the expected result and the evidence that would demonstrate it: the selected passages for a report, the changed behaviour for a code proposal or the fields extracted from a document. The guide also distinguishes a user’s mid-task message from tool output: new user instructions belong in a user turn, while the returned tool evidence remains separate.
This edition has its own safeguards and can return a refusal for categories including cyber harm, biological harm, competing-model development or other prohibited uses. The producer does not document server-side fallback for Haiku 5.5. Its guide also notes that xhigh effort can occasionally end a multi-turn response without visible answer text, and that reasoning-like text can appear more often at low effort or with thinking disabled. These are specific behaviours to account for when a workflow requires a visible, structured result. For ordinary extraction or summary work, use a focused source, a defined output and the effort level appropriate to the task.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits ~anthropic/claude-haiku-latest | 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 $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including100,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.0004 / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than100,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour $0.002 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "~anthropic/claude-haiku-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': '~anthropic/claude-haiku-latest', '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': '~anthropic/claude-haiku-latest', '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 anthropic/claude-haiku-5.5, ~anthropic/claude-haiku-latest
Use the method paths shown above.
Technical example in English
Extract a support handoff\n\nExtract a handoff from this ticket, using only its text: T-184: 'I changed my billing email yesterday. Receipts still go to the old address. My login works. Please resend March and April receipts.' Return ticket ID, requested actions, working function and missing information. Use 'not stated' for absent details.Four labeled fields retaining T-184, the receipt request, the working login and explicitly missing details.Every extracted value must trace to the ticket; do not invent either email address.