Source: https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/ Canonical: https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/ Language: en # tencent/Hunyuan-MT-7B [Markdown](https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/index.md) · [JSON](https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/facts.json) ## [tencent/Hunyuan-MT-7B](https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/) Tencent’s Hunyuan-MT-7B translates supplied text into a named target language, with multilingual coverage and particular attention to Chinese, minority languages and dialects. It is the translation model in the family; the separately named Chimera model combines multiple translation candidates. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/tencent-hunyuan-mt-7b/#method-1) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. Hunyuan-MT-7B is Tencent’s multilingual translation model. Its basic job is to take a source passage and produce that passage in the requested target language. Tencent distinguishes this translator from Hunyuan-MT-Chimera, the family’s ensemble model that refines several candidate translations into one result. #### Choose the language pair and the intended translation Tencent describes mutual translation across 33 languages and highlights five Chinese ethnic-minority languages or dialects. The published language table includes Chinese, Traditional Chinese, English, French, Portuguese, Spanish, Japanese, Turkish, Russian, Arabic, Korean, Thai, Italian, German, Vietnamese, Malay, Indonesian, Filipino, Hindi, Polish, Czech, Dutch, Khmer, Burmese, Persian, Gujarati, Urdu, Telugu, Marathi, Hebrew, Bengali, Tamil and Ukrainian. It also explicitly lists Tibetan, Kazakh, Mongolian, Uyghur and Cantonese. State the target language precisely, including the required writing system where relevant. A useful brief can identify whether a document needs Simplified or Traditional Chinese, and name the audience and terminology that should remain consistent. Keep the source passage intact so that the translation can be checked against the exact material supplied. #### Follow the producer’s translation prompt The manufacturer provides one prompt template for translation involving Chinese and another for language pairs that do not involve Chinese. Both name the target language, place the source text beneath the instruction and ask for the translation without an added explanation. The model has no default system prompt, so put the task instruction in the submitted message rather than relying on an unspecified default role. For a non-Chinese pair, an original brief can begin: “Translate the following segment into French, without additional explanation.” Follow it with the source passage. When the task involves Chinese, use the corresponding Chinese template and replace its target-language placeholder. The expected artifact is the translated passage, ready for a separate review or an application’s next processing step. #### Prepare segments that preserve meaning Use coherent sentences or paragraphs instead of breaking text at arbitrary character positions. Keep the surrounding material needed to interpret pronouns, technical terms and idioms. For a product manual, include the product name, defined terms and the actual instruction together; for a support exchange, preserve the question and the answer it refers to. If a larger document is processed in segments, keep a stable segment ID outside the text being translated and maintain the agreed terminology across the resulting passages. Ask explicitly how to handle names, measurements and placeholders, then inspect that treatment in the output. These preparation steps make the result easier to assemble and review; they do not replace checking the translated meaning. #### Keep the translator and the ensemble separate Hunyuan-MT-7B produces a translation from the source. Chimera’s documented workflow instead takes the original source plus several target-language candidates and produces a single refined translation. A workflow that uses both stages must supply those candidates to the separate ensemble model; selecting MT-7B alone does not perform that additional merge. The producer also lists FP8 checkpoints separately. Retain the ordinary Hunyuan-MT-7B edition name when saving the request and translation, and distinguish it from an FP8 or Chimera artifact when comparing results. The later HY-MT1.5 models have their own identities and specifications. #### Native inference guidance and result review Tencent recommends temperature 0.7, top\_p 0.6, top\_k 20 and repetition\_penalty 1.05 in its native inference example. Treat the translated content as the result to review. Check names, numbers, units, negation, dates and the relationship between sentences against the source, especially where a literal translation could lose the intended meaning. Retain the source, target language, selected model identity and final reviewed translation together. For application text, check that placeholders and formatting still connect to the intended fields; for a document, read the joined paragraphs for consistent terminology and tone. A fluent sentence can still require correction if it changes a factual detail or an instruction. Through Ailoot, select this exact model ID in /v1/chat/completions and supply the task in messages. Each message carries a role and its content; keep the relevant instruction and source material together. Collect the complete JSON response or all SSE segments when stream is enabled, and retain usage with the written result. **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `tencent/Hunyuan-MT-7B` [`tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/#m=tencent%2FHunyuan-MT-7B&route=Ultra&caller=tencent%2FHunyuan-MT-7B&fmt=chat) Other /`tencent/Hunyuan-MT-7B` ### Overview Tencent’s Hunyuan-MT-7B translates supplied text into a named target language, with multilingual coverage and particular attention to Chinese, minority languages and dialects. It is the translation model in the family; the separately named Chimera model combines multiple translation candidates. Accepted inputs Text · Images Results Text ### Available in Ailoot · inputs & methods **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` Input types `text`, `image` Output types `text` Output format JSON or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits tencent/Hunyuan-MT-7B Conditions: Use this exact Ailoot routing selector. | Default— | | Field`messages` | Type`array` | RequirementRequired | Values & limits Conditions: | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits Conditions: | Defaultfalse | Input material combinations **text\_input** · Required: messages **image\_with\_text** · Required: messages with actual image content blocks Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages | | Parameter`optional` | Contractstream | | Parameter`material_types` | Contracttext, image | | Parameter`conditions` | ContractUse this exact Ailoot routing selector., Image content is carried by this protocol where the exact selected model supports vision. Protocol admission does not establish image capability for every model. | Method aliases: `/chat/completions` Lifecycle: `synchronous_or_streamed` ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Standard rate When fast mode is selected Flex mode Standard rate When flex mode is selected Maximum reasoning effort Standard rate When maximum reasoning effort uses the default adjustment Explicit cache pricing Not enabled When explicit cache is requested Text-only zero rates Cache, tools and media follow their separately listed pricing conditions. For text-only input and output #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · OpenAI Chat curl · create once and retain the complete result ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" export AILOOT_API_KEY python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "tencent/Hunyuan-MT-7B", "messages": [{"role": "user", "content": "Explain how a rainbow forms."}]}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json # Submit once. Retain the complete result, original identifiers and every response header. ``` ### Receive and save results #### Ailoot Ultra · OpenAI Chat JSON or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Retain every content block, tool-call ID, thinking signature when returned, all SSE deltas and complete usage. A first text fragment is not the whole result. ##### If the original response needs checking Keep the original X-Client-Request-ID, response and headers. An unknown or result\_pending\_check outcome does not authorize a second creation. Response fields | Field | Type | | --- | --- | | Field`choices[]` | Typeobject | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionA required field, model selector, type or input representation is invalid. | Next stepInspect the returned validation detail before preparing a separate request. | | Response`` | ConditionThe original request or result is unknown or requires a check. | Next stepKeep original identifiers and headers; read only supported status/library records. Do not submit creation again. | ### Aliases #### Model aliases **Ailoot Ultra** [`tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/#m=tencent%2FHunyuan-MT-7B&route=Ultra&caller=tencent%2FHunyuan-MT-7B&fmt=chat) #### Method path aliases `/chat/completions` #### Related caller IDs [`tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/#m=tencent%2FHunyuan-MT-7B&route=Ultra&caller=tencent%2FHunyuan-MT-7B&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `tencent/Hunyuan-MT-7B` [`tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/#m=tencent%2FHunyuan-MT-7B&route=Ultra&caller=tencent%2FHunyuan-MT-7B&fmt=messages) Other /`tencent/Hunyuan-MT-7B` ### Overview Tencent’s Hunyuan-MT-7B translates supplied text into a named target language, with multilingual coverage and particular attention to Chinese, minority languages and dialects. It is the translation model in the family; the separately named Chimera model combines multiple translation candidates. Accepted inputs Text · Images Results json · sse ### Available in Ailoot · inputs & methods **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` Input types `text`, `image` Output types `json`, `sse` Output format JSON or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits tencent/Hunyuan-MT-7B Conditions: Use this exact Ailoot routing selector. | Default— | | Field`messages` | Type`array` | RequirementRequired | Values & limits Conditions: | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits Conditions: | Defaultfalse | | Field`max_tokens` | Type`integer` | RequirementRequired | Values & limits Minimum: 1 Conditions: | Default— | Input material combinations **text\_input** · Required: messages **image\_with\_text** · Required: messages with actual image content blocks Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages, max\_tokens | | Parameter`optional` | Contractstream | | Parameter`material_types` | Contracttext, image | | Parameter`conditions` | ContractUse this exact Ailoot routing selector., Image content is carried by this protocol where the exact selected model supports vision. Protocol admission does not establish image capability for every model. | Lifecycle: `synchronous_or_streamed` ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Standard rate When fast mode is selected Flex mode Standard rate When flex mode is selected Maximum reasoning effort Standard rate When maximum reasoning effort uses the default adjustment Explicit cache pricing Not enabled When explicit cache is requested Text-only zero rates Cache, tools and media follow their separately listed pricing conditions. For text-only input and output #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages curl · create once and retain the complete result ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" export AILOOT_API_KEY python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "tencent/Hunyuan-MT-7B", "messages": [{"role": "user", "content": "Explain how a rainbow forms."}], "max_tokens": 256}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json # Submit once. Retain the complete result, original identifiers and every response header. ``` ### Receive and save results #### Ailoot Ultra · Anthropic Messages JSON or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Retain every content block, tool-call ID, thinking signature when returned, all SSE deltas and complete usage. A first text fragment is not the whole result. ##### If the original response needs checking Keep the original X-Client-Request-ID, response and headers. An unknown or result\_pending\_check outcome does not authorize a second creation. Response fields | Field | Type | | --- | --- | | Field`content[]` | Typeobject | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionA required field, model selector, type or input representation is invalid. | Next stepInspect the returned validation detail before preparing a separate request. | | Response`` | ConditionThe original request or result is unknown or requires a check. | Next stepKeep original identifiers and headers; read only supported status/library records. Do not submit creation again. | ### Aliases #### Model aliases **Ailoot Ultra** [`tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/#m=tencent%2FHunyuan-MT-7B&route=Ultra&caller=tencent%2FHunyuan-MT-7B&fmt=messages) #### Method path aliases `/chat/completions` #### Related caller IDs [`tencent/Hunyuan-MT-7B`](https://ailoot.ai/models/en/#m=tencent%2FHunyuan-MT-7B&route=Ultra&caller=tencent%2FHunyuan-MT-7B&fmt=messages) FAQ · tencent/Hunyuan-MT-7B What inputs and results are available? `POST /v1/chat/completions` · `tencent/Hunyuan-MT-7B`: `text`, `image` → `text` `POST /v1/messages` · `tencent/Hunyuan-MT-7B`: `text`, `image` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `tencent/Hunyuan-MT-7B`: `model`, `messages`, `stream` `POST /v1/messages` · `tencent/Hunyuan-MT-7B`: `model`, `messages`, `stream`, `max_tokens` What limits apply? `POST /v1/messages` · `tencent/Hunyuan-MT-7B`: `max_tokens.minimum`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. 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