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Head-to-Head Model Comparison

Kimi K2.6 vs Kimi K2.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Kimi K2.6 vs Kimi K2.5: Benchmarks, Pricing & Speed Comparison

Moonshot AI 的 Kimi K2.6 与 Moonshot AI 的 Kimi K2.5 是当前备受关注的两款主力 AI 模型。Kimi K2.6 提供 262.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Kimi K2.5 提供 262.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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Moonshot AI

Kimi K2.6

自主智能体与工具专家

Moonshot AI 推出的 Kimi K2.6 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 262K 上下文窗口、68 Token/秒生成速度与 87/100 综合智能指数（免费开源权重）。

Intelligence Index87 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

Moonshot AI

Kimi K2.5

原生多模态视觉模型

Moonshot AI 推出的 Kimi K2.5 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 262K 上下文窗口、65 Token/秒生成速度与 86/100 综合智能指数（免费开源权重）。

Intelligence Index86 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

52%

核心差异一览

  • Both models cost the same under this workload — choose on capability, not price.
  • Kimi K2.6 is faster at 68 tok/s vs 65 tok/s.
  • Kimi K2.6 leads on SWE-bench coding (54.0% vs 52.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureKimi K2.6Kimi K2.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
8786
Provider
Moonshot AIMoonshot AI
Input Price
Cost per million input tokens
$0.00 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text262.1K tokens~393 A4 pages of text
Max Output Tokens
Per single request
16K16K
Throughput Speed
Tokens generated per second
68 tok/s65 tok/s
SWE-bench Verified
Real-world software engineering
54%52%
GPQA Diamond
Graduate-level reasoning
71%69.5%

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Kimi K2.6
+2.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Kimi K2.6
+5% faster generation
Context
Tied
262.1K tokens window

权威基准测试得分对比

Official evaluation metrics verified by independent third-party research labs.

Kimi K2.6Kimi K2.5
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

54%vs52%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

71%vs69.5%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

83.5%vs82%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1395ptsvs1380pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs65 tok/s

延迟与响应速度

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Kimi K2.60.59s
Kimi K2.50.62s
Est. End-to-End Response (500 tokens)TTFT + generation time
Kimi K2.67.94s
Kimi K2.58.31s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Kimi K2.6 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Kimi K2.5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
These models cost the same under this workload — no savings either way.Break-even

Kimi K2.6 · Strengths & Key Advantages

  • 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 54% · GPQA Diamond 71%）
  • 高达 68 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 590 毫秒
  • 开放模型权重 (1.5T total (40B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Kimi K2.5 · Strengths & Key Advantages

  • 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 52% · GPQA Diamond 69.5%）
  • 高达 65 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 620 毫秒
  • 开放模型权重 (1.2T total (36B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricKimi K2.6Kimi K2.5
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
25K24K
Reasoning Tokens / Task
Thinking tokens burned per task
9K8K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureKimi K2.6Kimi K2.5
Official API Model ID (SDK)kimi-k2-6kimi-k2-5
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042026-01
Knowledge CutoffFebruary 2026November 2025
ArchitectureRefined Multimodal Agentic MoE TransformerMultimodal Open Mixture-of-Experts Transformer
Parameters1.5T total (40B active MoE)1.2T total (36B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 930 GB VRAM (4-bit Quantized) · 3225 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 744 GB VRAM (4-bit Quantized) · 2580 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Kimi K2.6 vs Kimi K2.5: 常见问题解答 (FAQ)

Which is more intelligent, Kimi K2.6 or Kimi K2.5?

Kimi K2.6 is more intelligent. It scores 87 compared with 86 on the composite Intelligence Index.

Which model is faster?

Kimi K2.6 is faster, generating 68 tokens per second compared with 65 tokens per second.

Which model has lower latency?

Kimi K2.6 has lower latency, with a time to first token of 0.59s compared with 0.62s.

综合结论

综合来看，Kimi K2.6（适合：专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Kimi K2.5（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Kimi K2.6 在综合智能与编程基准测试中表现更强，Kimi K2.6 拥有更快的 Token 生成速度，而 Kimi K2.6 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Kimi K2.6 和 Kimi K2.5 哪个更强？+

在综合智能指数与编程基准评测中，Kimi K2.6 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Kimi K2.6 与 Kimi K2.5 的 API 价格哪个更便宜？+

Kimi K2.6 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Kimi K2.5 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Kimi K2.6 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Kimi K2.6 支持 262.1K Token 上下文窗口，而 Kimi K2.5 支持 262.1K Token。在流式生成速度方面，Kimi K2.6 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

数据来源: Moonshot AI 与 Moonshot AI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。