Cohere Rerank 4 Pro is the quality-focused model in Cohere’s Rerank 4 family. It evaluates candidate documents against a query and returns their relevance ordering and scores. The model is intended for complex retrieval tasks, including multilingual documents and semi-structured information. It fits a search or retrieval-augmented workflow after an existing system has collected candidates: the result helps choose which passages should be shown or supplied as evidence to the next stage.
The producer identifier is rerank-v4.0-pro, separate from rerank-v4.0-fast and rerank-v3.5. Pro is positioned for complex cases where retrieval quality is the priority, while Fast targets lower latency and higher throughput. Both Rerank 4 editions use a 32,768-token context. The query and one candidate document share that budget; it is not a separate full window for every input field or a limit on the entire stored search corpus.
Supply a clear query and the list of candidate text strings. Each returned result includes the original document index and a relevance score, and top_n can select how many ranked results are returned; without it, the producer returns all results. Preserve a source identifier beside each input so ranking can be connected back to the exact passage. For a support question, ask about the required conditions and supply the candidate policy sections rather than expecting the reranker to locate policies that were not in the list.
The model supports more than 100 languages and can rerank English and non-English material. For a query with several requirements, state the conditions that make a passage useful, such as the product, situation and needed procedure. For semi-structured records, Cohere recommends YAML strings rather than passing raw objects in the V2 document list. Keep meaningful fields such as title and content in a deliberate order, because truncation can remove later fields. This preparation is useful for product records, messages and document metadata as well as ordinary prose.
The model-specific preparation guide limits the Rerank 4 query to the first 16,384 tokens. It describes longer-document processing through chunks that share the 32,768-token context with the query and reserved tokens. The current V2 reference separately defaults max_tokens_per_doc to 4096 and says longer document strings are truncated to that setting. Treat the model window and the producer request’s document truncation as different controls. Prepare concise relevant passages or choose an appropriate document budget instead of assuming every long source is always read in full.
For recommended request performance, Cohere advises against sending more than 1,000 documents at once. Preserve candidate identifiers when splitting a larger list into manageable groups. Relevance scores are normalised between 0 and 1, but they depend on the query and candidates; they are not a linear measure of factual correctness. Use the ordering as the primary retrieval result. If filtering by a score threshold, establish it with representative queries and borderline relevant passages from the actual domain rather than assuming a universal cutoff.
A useful final artifact is a ranked candidate list carrying source identifiers and scores, ready for a search result or an evidence-selection step. A document with a high relevance score can still contain outdated or incorrect information, so retain its source and content for the stage that uses it. Pro’s purpose is relevance judgement, not writing a free-form answer or generating embeddings. Keep the exact Pro edition attached to the result when comparing or refining a retrieval workflow.
", "aboutText": "Cohere Rerank 4 Pro is the quality-focused model in Cohere’s Rerank 4 family. It evaluates candidate documents against a query and returns their relevance ordering and scores. The model is intended for complex retrieval tasks, including multilingual documents and semi-structured information. It fits a search or retrieval-augmented workflow after an existing system has collected candidates: the result helps choose which passages should be shown or supplied as evidence to the next stage.\n\nThe producer identifier is rerank-v4.0-pro, separate from rerank-v4.0-fast and rerank-v3.5. Pro is positioned for complex cases where retrieval quality is the priority, while Fast targets lower latency and higher throughput. Both Rerank 4 editions use a 32,768-token context. The query and one candidate document share that budget; it is not a separate full window for every input field or a limit on the entire stored search corpus.\n\nSupply a clear query and the list of candidate text strings. Each returned result includes the original document index and a relevance score, and top_n can select how many ranked results are returned; without it, the producer returns all results. Preserve a source identifier beside each input so ranking can be connected back to the exact passage. For a support question, ask about the required conditions and supply the candidate policy sections rather than expecting the reranker to locate policies that were not in the list.\n\nThe model supports more than 100 languages and can rerank English and non-English material. For a query with several requirements, state the conditions that make a passage useful, such as the product, situation and needed procedure. For semi-structured records, Cohere recommends YAML strings rather than passing raw objects in the V2 document list. Keep meaningful fields such as title and content in a deliberate order, because truncation can remove later fields. This preparation is useful for product records, messages and document metadata as well as ordinary prose.\n\nThe model-specific preparation guide limits the Rerank 4 query to the first 16,384 tokens. It describes longer-document processing through chunks that share the 32,768-token context with the query and reserved tokens. The current V2 reference separately defaults max_tokens_per_doc to 4096 and says longer document strings are truncated to that setting. Treat the model window and the producer request’s document truncation as different controls. Prepare concise relevant passages or choose an appropriate document budget instead of assuming every long source is always read in full.\n\nFor recommended request performance, Cohere advises against sending more than 1,000 documents at once. Preserve candidate identifiers when splitting a larger list into manageable groups. Relevance scores are normalised between 0 and 1, but they depend on the query and candidates; they are not a linear measure of factual correctness. Use the ordering as the primary retrieval result. If filtering by a score threshold, establish it with representative queries and borderline relevant passages from the actual domain rather than assuming a universal cutoff.\n\nA useful final artifact is a ranked candidate list carrying source identifiers and scores, ready for a search result or an evidence-selection step. A document with a high relevance score can still contain outdated or incorrect information, so retain its source and content for the stage that uses it. Pro’s purpose is relevance judgement, not writing a free-form answer or generating embeddings. Keep the exact Pro edition attached to the result when comparing or refining a retrieval workflow.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "b3999ec1bfbe6124eed9b394da97c867b386e016d1c78584868b0eb352dcce93", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "cohere/rerank-4-proCohere Rerank 4 Pro orders candidate documents by relevance to a query for search and retrieval-augmented workflows. It is the quality-focused Rerank 4 edition, with a 32K context, multilingual coverage and support for semi-structured text.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Cohere Rerank 4 Pro is the quality-focused model in Cohere’s Rerank 4 family. It evaluates candidate documents against a query and returns their relevance ordering and scores. The model is intended for complex retrieval tasks, including multilingual documents and semi-structured information. It fits a search or retrieval-augmented workflow after an existing system has collected candidates: the result helps choose which passages should be shown or supplied as evidence to the next stage.
The producer identifier is rerank-v4.0-pro, separate from rerank-v4.0-fast and rerank-v3.5. Pro is positioned for complex cases where retrieval quality is the priority, while Fast targets lower latency and higher throughput. Both Rerank 4 editions use a 32,768-token context. The query and one candidate document share that budget; it is not a separate full window for every input field or a limit on the entire stored search corpus.
Supply a clear query and the list of candidate text strings. Each returned result includes the original document index and a relevance score, and top_n can select how many ranked results are returned; without it, the producer returns all results. Preserve a source identifier beside each input so ranking can be connected back to the exact passage. For a support question, ask about the required conditions and supply the candidate policy sections rather than expecting the reranker to locate policies that were not in the list.
The model supports more than 100 languages and can rerank English and non-English material. For a query with several requirements, state the conditions that make a passage useful, such as the product, situation and needed procedure. For semi-structured records, Cohere recommends YAML strings rather than passing raw objects in the V2 document list. Keep meaningful fields such as title and content in a deliberate order, because truncation can remove later fields. This preparation is useful for product records, messages and document metadata as well as ordinary prose.
The model-specific preparation guide limits the Rerank 4 query to the first 16,384 tokens. It describes longer-document processing through chunks that share the 32,768-token context with the query and reserved tokens. The current V2 reference separately defaults max_tokens_per_doc to 4096 and says longer document strings are truncated to that setting. Treat the model window and the producer request’s document truncation as different controls. Prepare concise relevant passages or choose an appropriate document budget instead of assuming every long source is always read in full.
For recommended request performance, Cohere advises against sending more than 1,000 documents at once. Preserve candidate identifiers when splitting a larger list into manageable groups. Relevance scores are normalised between 0 and 1, but they depend on the query and candidates; they are not a linear measure of factual correctness. Use the ordering as the primary retrieval result. If filtering by a score threshold, establish it with representative queries and borderline relevant passages from the actual domain rather than assuming a universal cutoff.
A useful final artifact is a ranked candidate list carrying source identifiers and scores, ready for a search result or an evidence-selection step. A document with a high relevance score can still contain outdated or incorrect information, so retain its source and content for the stage that uses it. Pro’s purpose is relevance judgement, not writing a free-form answer or generating embeddings. Keep the exact Pro edition attached to the result when comparing or refining a retrieval workflow.
POST /v1/reranktextrerank| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fielddocuments | Typearray | RequirementRequired | Values & limits | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits cohere/rerank-4-pro | Default— |
Fieldprovider | Typeschema | RequirementOptional | Values & limits | Default— |
Fieldquery | Typestring | RequirementRequired | Values & limits | Default— |
Fieldsession_id | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtop_n | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default— |
Fieldtrace | Typeschema | RequirementOptional | Values & limits | Default— |
Fielduser | Typestring | RequirementOptional | Values & limits | Default— |
Fielddocuments[].text | Typestring | RequirementOptional | Values & limits | Default— |
Fielddocuments[].image | Typestring | RequirementOptional | Values & limits | Default— |
text_rerank · Required: query, documents array with minItems1
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, query, documents |
Parametermaterial_types | Contracttext |
Parameteroptional_controls | Contractprovider, session_id, top_n, trace, user |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous
Send the exact model ID and retain the complete native response.
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 | Billing unit | Charge |
|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Billing unit— | Charge Input · Input tokens — / 1K tokens Output · Output tokens — / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationUltra | Rate schedule— | Billing unitper search unit | Charge total · search unit $0.005 per search unit |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "documents": [\n "Paris is the capital of France.",\n "Berlin is the capital of Germany."\n ],\n "model": "cohere/rerank-4-pro",\n "query": "What is the capital of France?",\n "top_n": 3\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_REQUEST_FILE:?Set your actual JSON request file}"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-pro'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\njson
Keep every complete returned response, file and content part.
Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.
Preserve original operation and full/partial reply; do not replay POST.
| Field | Type |
|---|---|
Fieldmodel | Typestring |
Fieldid | Typestring |
Fieldprovider | Typestring |
Fieldresults[].index | Typeinteger |
Fieldresults[].relevance_score | Typenumber |
Fieldresults[].document.text | Typestring |
Fieldresults[].document.image | Typestring |
Fieldusage.search_units | Typeinteger |
Fieldusage.total_tokens | Typeinteger |
Fieldusage.cost | Typenumber |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required field, representation or value | Next stepCorrect the indicated request field. |
Ailoot Ultra cohere/rerank-4-pro
Use the method paths shown above.