AI 模型情报

Kimi-K2.5-fast

nebius/kimi-k2-5-fast

出品方: nebius · 系列: kimi · 发布 2025-12-15 · 知识截止: 2025-06

⚠ 本模型为社区微调 / 衍生版本,非厂商官方发布。

$0.500
输入 / 1M token
$2.50
输出 / 1M token
256K
上下文长度
8K
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, image · 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.

Coding79
  • Tool calling40/40
  • Structured output20/20
  • Reasoning10/10
  • Context window (100K → 1M)8/20
  • Provider availability1/10
Agents81
  • Tool calling35/35
  • Structured output25/25
  • Reasoning15/15
  • Output token limit5/15
  • Provider availability1/10
JSON / structured output94
  • Structured output / JSON mode50/50
  • Tool calling20/20
  • Temperature control10/10
  • Price-friendly for high-volume14/20
Cost efficiency56
  • Headline price (log-scaled)51/95
  • Has prompt-cache pricing5/5
Long context60
  • Context window (100K → 2M)50/90
  • Has published price for full window10/10
Vision82
  • Accepts image input50/50
  • Context window (10K → 1M)21/30
  • Has published price10/10
  • Provider availability1/10
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
$3.75
< $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
$7.50
< $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
$2.25
< $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
$6.50
< $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
$7.50
< $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 nebius · moonshotai/Kimi-K2.5-fast

$0.500
输入
$2.50
输出
$0.050
缓存读
$0.625
缓存写

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
Nebius Token Factory
nebius
moonshotai/Kimi-K2.5-fast$0.500$2.50256K2025-12-15

Frequently asked questions

How much does Kimi-K2.5-fast cost?

Kimi-K2.5-fast costs $0.500 per 1M input tokens and $2.50 per 1M output tokens, sourced from nebius. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of Kimi-K2.5-fast?

Kimi-K2.5-fast has a context window of 256K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.

Does Kimi-K2.5-fast support tool calling?

Yes. Kimi-K2.5-fast 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 Kimi-K2.5-fast support structured output / JSON mode?

Yes. Kimi-K2.5-fast 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 Kimi-K2.5-fast accept image input?

Yes. Kimi-K2.5-fast accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check nebius's docs for the exact rule.

Is Kimi-K2.5-fast open-weight?

Yes. Kimi-K2.5-fast'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 Kimi-K2.5-fast?

If Kimi-K2.5-fast doesn't fit, consider Hermes-4-70B, Hermes-4-405B, INTELLECT-3. 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.

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.