AI 模型情报

Kimi K2.6 Thinking

nano-gpt/kimi-k2-6-thinking

出品方: nano-gpt · 系列: kimi-thinking · 发布 2026-04-16

⚠ 本模型为社区微调 / 衍生版本,非厂商官方发布。

$0.530
输入 / 1M token
$2.73
输出 / 1M token
256K
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, 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.

Coding59
  • Tool calling40/40
  • Structured output0/20
  • Reasoning10/10
  • Context window (100K → 1M)8/20
  • Provider availability1/10
Agents66
  • Tool calling35/35
  • Structured output0/25
  • Reasoning15/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output33
  • Structured output / JSON mode0/50
  • Tool calling20/20
  • Temperature control0/10
  • Price-friendly for high-volume13/20
Cost efficiency50
  • Headline price (log-scaled)50/95
  • Has prompt-cache pricing0/5
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
$4.02
< $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
$8.03
< $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
$2.43
< $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
$6.97
< $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
$8.00
< $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 nano-gpt · moonshotai/kimi-k2.6:thinking

$0.530
输入
$2.73
输出

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
NanoGPT
nano-gpt
moonshotai/kimi-k2.6:thinking$0.530$2.73256K2026-04-16

Frequently asked questions

How much does Kimi K2.6 Thinking cost?

Kimi K2.6 Thinking costs $0.530 per 1M input tokens and $2.73 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 Kimi K2.6 Thinking?

Kimi K2.6 Thinking has a context window of 256K tokens, with a max output of 66K tokens per reply. This is the total combined size of prompt + completion.

Does Kimi K2.6 Thinking support tool calling?

Yes. Kimi K2.6 Thinking 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 K2.6 Thinking support structured output / JSON mode?

No. Kimi K2.6 Thinking does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.

Can Kimi K2.6 Thinking accept image input?

Yes. Kimi K2.6 Thinking accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check nano-gpt's docs for the exact rule.

Is Kimi K2.6 Thinking open-weight?

Yes. Kimi K2.6 Thinking'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 K2.6 Thinking?

If Kimi K2.6 Thinking 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.

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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.