AI 模型情報

Hunyuan Turbo S

nano-gpt/hunyuan-turbos-20250226

出品方: nano-gpt · 發布 2025-02-27

$0.187
輸入 / 1M token
$0.374
輸出 / 1M token
24K
上下文長度
8K
最大輸出

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

能力清單

工具呼叫推理結構化輸出附件開放權重? 溫度可調
支援模態: 輸入 text · 輸出 text

Model fit scores

0–100 · higher is better

These scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.

Coding1
  • Tool calling0/40
  • Structured output0/20
  • Reasoning0/10
  • Context window (100K → 1M)0/20
  • Provider availability1/10
Agents6
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit5/15
  • Provider availability1/10
JSON / structured output19
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume19/20
Cost efficiency69
  • Headline price (log-scaled)69/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Production-readiness58
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K8/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)15/15

Cost Efficiency Index

Open full calculator →

Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.

ScenarioCostAssumption
RAG answer
per 1,000 RAG answers
$1.12
< $0.01 per request
5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot.
Support ticket triage
per 10,000 tickets
$2.24
< $0.01 per request
1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support.
Data extraction
per 1,000 documents
$0.56
< $0.01 per request
2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines.
Code review
per 1,000 PRs
$1.87
< $0.01 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$2.47
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

定價詳情

推薦定價來自 nano-gpt · hunyuan-turbos-20250226

$0.187
輸入
$0.374
輸出

於 1 家供應商可用

服務商服務商模型 ID輸入 / 1M輸出 / 1M上下文發布日期
NanoGPT
nano-gpt
hunyuan-turbos-20250226$0.187$0.37424K2025-02-27

Frequently asked questions

How much does Hunyuan Turbo S cost?

Hunyuan Turbo S costs $0.187 per 1M input tokens and $0.374 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of Hunyuan Turbo S?

Hunyuan Turbo S has a context window of 24K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.

Does Hunyuan Turbo S support tool calling?

No. Hunyuan Turbo S does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does Hunyuan Turbo S support structured output / JSON mode?

No. Hunyuan Turbo S does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.

Can Hunyuan Turbo S accept image input?

No. Hunyuan Turbo S only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is Hunyuan Turbo S open-weight?

No. Hunyuan Turbo S is a proprietary model — only nano-gpt (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.

What are the best alternatives to Hunyuan Turbo S?

If Hunyuan Turbo S doesn't fit, consider Brave (Answers), Exa (Research), Auto model (Basic). Each one targets the same use case — see the Related links below for direct head-to-head pages.

Where does this data come from?

All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.

More nano-gpt models

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Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.