Cohere Rerank 3.5 is a multilingual model for relevance ranking of candidate documents and semi-structured text. It evaluates each candidate in relation to a query and returns a reordered result list with relevance scores. It can refine candidates obtained through keyword or semantic search and select passages for a retrieval-augmented answer. Its task is to improve the evidence ordering rather than generate a free-form answer or create a new corpus index.
The exact producer identifier is rerank-v3.5. It uses a 4096-token context, distinct from Rerank 4 Pro and Fast’s 32K context. Query and document tokens count together against that per-candidate window. The model-specific preparation guide truncates a query beyond its first 2048 tokens and describes document chunks of 4093 tokens before the query is taken into account. Keep the query focused and prepare candidate passages containing the information needed to judge the requested relationship.
Supply the query and a list of candidate text strings. Each result carries an index pointing to the original document position and a relevance score; top_n chooses how many ranked results to retain. Without it, the documented producer workflow returns all results. Preserve document identifiers independently of the score so the top passage can still be traced to the original policy, product record or message.
Rerank 3.5 supports more than 100 languages, including English and non-English material. A query can contain several aspects of the need, such as the product, user situation and desired procedure. For structured records, the V2 release removed raw document objects and recommends YAML strings. Keep useful fields in a deliberate order, with decisive content before less relevant metadata, because long strings can be truncated. This works for titles and content, product attributes or a message subject and body.
The V2 producer reference uses max_tokens_per_doc for document truncation, with a default of 4096. Cohere’s preparation guide also describes long-document chunking; the effective input depends on the selected workflow and its document settings. For greater control, prepare identifiable passages yourself rather than assuming every section of a long source remains visible. Preserve enough surrounding context to distinguish an answer-bearing passage from one that only shares its vocabulary.
For request performance, the current reference recommends not sending more than 1,000 documents at once. Relevance scores are normalised between 0 and 1, but their values depend on the query and candidates and are not a linear scale of correctness. Use the ranking to identify useful evidence. If a score threshold is needed, choose representative domain queries and borderline relevant passages so the threshold reflects the actual retrieval task.
A practical support workflow can rerank candidate return-policy sections for a question about a damaged item, then pass the selected source text to an answer-writing stage. An email search can rank messages by a specific issue and retain the original message identifier with each result. The useful artifact is the ordered evidence list. Keep Rerank 3.5’s shorter context and V2 document preparation attached to that result instead of substituting later-model limits.
", "aboutText": "Cohere Rerank 3.5 is a multilingual model for relevance ranking of candidate documents and semi-structured text. It evaluates each candidate in relation to a query and returns a reordered result list with relevance scores. It can refine candidates obtained through keyword or semantic search and select passages for a retrieval-augmented answer. Its task is to improve the evidence ordering rather than generate a free-form answer or create a new corpus index.\n\nThe exact producer identifier is rerank-v3.5. It uses a 4096-token context, distinct from Rerank 4 Pro and Fast’s 32K context. Query and document tokens count together against that per-candidate window. The model-specific preparation guide truncates a query beyond its first 2048 tokens and describes document chunks of 4093 tokens before the query is taken into account. Keep the query focused and prepare candidate passages containing the information needed to judge the requested relationship.\n\nSupply the query and a list of candidate text strings. Each result carries an index pointing to the original document position and a relevance score; top_n chooses how many ranked results to retain. Without it, the documented producer workflow returns all results. Preserve document identifiers independently of the score so the top passage can still be traced to the original policy, product record or message.\n\nRerank 3.5 supports more than 100 languages, including English and non-English material. A query can contain several aspects of the need, such as the product, user situation and desired procedure. For structured records, the V2 release removed raw document objects and recommends YAML strings. Keep useful fields in a deliberate order, with decisive content before less relevant metadata, because long strings can be truncated. This works for titles and content, product attributes or a message subject and body.\n\nThe V2 producer reference uses max_tokens_per_doc for document truncation, with a default of 4096. Cohere’s preparation guide also describes long-document chunking; the effective input depends on the selected workflow and its document settings. For greater control, prepare identifiable passages yourself rather than assuming every section of a long source remains visible. Preserve enough surrounding context to distinguish an answer-bearing passage from one that only shares its vocabulary.\n\nFor request performance, the current reference recommends not sending more than 1,000 documents at once. Relevance scores are normalised between 0 and 1, but their values depend on the query and candidates and are not a linear scale of correctness. Use the ranking to identify useful evidence. If a score threshold is needed, choose representative domain queries and borderline relevant passages so the threshold reflects the actual retrieval task.\n\nA practical support workflow can rerank candidate return-policy sections for a question about a damaged item, then pass the selected source text to an answer-writing stage. An email search can rank messages by a specific issue and retain the original message identifier with each result. The useful artifact is the ordered evidence list. Keep Rerank 3.5’s shorter context and V2 document preparation attached to that result instead of substituting later-model limits.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "e597527dd9440f5f2a437ddae2889e97898b2478a5161d0704d12605ebdf6b90", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "cohere/rerank-v3.5Cohere Rerank 3.5 reorders candidate text and semi-structured records by relevance to a query. It supports more than 100 languages and a 4096-token context for refining keyword or semantic search results before an answer or search display.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Cohere Rerank 3.5 is a multilingual model for relevance ranking of candidate documents and semi-structured text. It evaluates each candidate in relation to a query and returns a reordered result list with relevance scores. It can refine candidates obtained through keyword or semantic search and select passages for a retrieval-augmented answer. Its task is to improve the evidence ordering rather than generate a free-form answer or create a new corpus index.
The exact producer identifier is rerank-v3.5. It uses a 4096-token context, distinct from Rerank 4 Pro and Fast’s 32K context. Query and document tokens count together against that per-candidate window. The model-specific preparation guide truncates a query beyond its first 2048 tokens and describes document chunks of 4093 tokens before the query is taken into account. Keep the query focused and prepare candidate passages containing the information needed to judge the requested relationship.
Supply the query and a list of candidate text strings. Each result carries an index pointing to the original document position and a relevance score; top_n chooses how many ranked results to retain. Without it, the documented producer workflow returns all results. Preserve document identifiers independently of the score so the top passage can still be traced to the original policy, product record or message.
Rerank 3.5 supports more than 100 languages, including English and non-English material. A query can contain several aspects of the need, such as the product, user situation and desired procedure. For structured records, the V2 release removed raw document objects and recommends YAML strings. Keep useful fields in a deliberate order, with decisive content before less relevant metadata, because long strings can be truncated. This works for titles and content, product attributes or a message subject and body.
The V2 producer reference uses max_tokens_per_doc for document truncation, with a default of 4096. Cohere’s preparation guide also describes long-document chunking; the effective input depends on the selected workflow and its document settings. For greater control, prepare identifiable passages yourself rather than assuming every section of a long source remains visible. Preserve enough surrounding context to distinguish an answer-bearing passage from one that only shares its vocabulary.
For request performance, the current reference recommends not sending more than 1,000 documents at once. Relevance scores are normalised between 0 and 1, but their values depend on the query and candidates and are not a linear scale of correctness. Use the ranking to identify useful evidence. If a score threshold is needed, choose representative domain queries and borderline relevant passages so the threshold reflects the actual retrieval task.
A practical support workflow can rerank candidate return-policy sections for a question about a damaged item, then pass the selected source text to an answer-writing stage. An email search can rank messages by a specific issue and retain the original message identifier with each result. The useful artifact is the ordered evidence list. Keep Rerank 3.5’s shorter context and V2 document preparation attached to that result instead of substituting later-model limits.
POST /v1/reranktextrerank| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fielddocuments | Typearray | RequirementRequired | Values & limits | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits cohere/rerank-v3.5 | 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 $0.002 per search unit |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "documents": [\n "Paris is the capital of France.",\n "Berlin is the capital of Germany."\n ],\n "model": "cohere/rerank-v3.5",\n "query": "What is the capital of France?",\n "top_n": 3\n}set -eu\n: "${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-v3.5'\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-v3.5
Use the method paths shown above.