AI 模型情报

DeepSeek V3.2 TEE

chutes/v3-2-tee

出品方： chutes · 系列: deepseek · 发布 2025-12-29

⚠ 本模型为社区微调 / 衍生版本，非厂商官方发布。

$0.280
输入 / 1M token
$0.420
输出 / 1M token
131K
上下文长度
66K
最大输出

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

能力清单

✓ 工具调用✓ 推理✓ 结构化输出✗ 附件✓ 开放权重✓ 温度可调
支持模态: 输入 text · 输出 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.

Coding73
  • Tool calling40/40
  • Structured output20/20
  • Reasoning10/10
  • Context window (100K → 1M)2/20
  • Provider availability1/10
Agents91
  • Tool calling35/35
  • Structured output25/25
  • Reasoning15/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output99
  • Structured output / JSON mode50/50
  • Tool calling20/20
  • Temperature control10/10
  • Price-friendly for high-volume19/20
Cost efficiency71
  • Headline price (log-scaled)66/95
  • Has prompt-cache pricing5/5
Long context46
  • Context window (100K → 2M)36/90
  • Has published price for full window10/10
Production-readiness50
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K15/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)0/15

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
$1.61
< $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
$3.22
< $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
$0.77
< $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
$2.66
< $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
$3.61
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

定价详情

推荐定价来自 chutes · deepseek-ai/DeepSeek-V3.2-TEE

$0.280
输入
$0.420
输出
$0.140
缓存读

在 1 家渠道可用

服务商服务商模型 ID输入 / 1M输出 / 1M上下文发布日期
Chutes
chutes
deepseek-ai/DeepSeek-V3.2-TEE$0.280$0.420131K2025-12-29

Frequently asked questions

How much does DeepSeek V3.2 TEE cost?

DeepSeek V3.2 TEE costs $0.280 per 1M input tokens and $0.420 per 1M output tokens, sourced from chutes. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of DeepSeek V3.2 TEE?

DeepSeek V3.2 TEE has a context window of 131K tokens, with a max output of 66K tokens per reply. This is the total combined size of prompt + completion.

Does DeepSeek V3.2 TEE support tool calling?

Yes. DeepSeek V3.2 TEE supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Does DeepSeek V3.2 TEE support structured output / JSON mode?

Yes. DeepSeek V3.2 TEE supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Can DeepSeek V3.2 TEE accept image input?

No. DeepSeek V3.2 TEE only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is DeepSeek V3.2 TEE open-weight?

Yes. DeepSeek V3.2 TEE's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.

What are the best alternatives to DeepSeek V3.2 TEE?

If DeepSeek V3.2 TEE doesn't fit, consider Hermes 4 14B, MiMo V2 Flash TEE, dots.ocr. 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.

Explore more

More chutes models

Capability lists this model is in

See also

最近更新：

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.