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Head-to-Head Model Comparison

DeepSeek V4 Pro vs Kimi K3

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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DeepSeekMoonshot AIopen-sourceflagship

Compare DeepSeek V4 Pro by DeepSeek against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.

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DeepSeek

DeepSeek V4 Pro

开源权重模型

DeepSeek 推出的 DeepSeek V4 Pro 人工智能模型，具备 1M 上下文窗口，输入价格为 $1.32/百万 Token，输出价格为 $3.96/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index53 / 100
Input Token:

$1.32 / 1M

Output Token:

$3.96 / 1M

Context Window:

1M tokens

SWE-bench Verified:

68.5%

Moonshot AI

Kimi K3

开源权重模型

Moonshot AI 推出的 Kimi K3 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.5%

核心差异一览

  • Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
  • Kimi K3 has a larger context window (1M vs 1M tokens).
  • DeepSeek V4 Pro is faster at 80 tok/s vs 58 tok/s.
  • Kimi K3 leads on SWE-bench coding (74.5% vs 68.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureDeepSeek V4 ProKimi K3
Intelligence Index
Composite reasoning & knowledge score (0-100)
5393
Provider
DeepSeekMoonshot AI
Input Price
Cost per million input tokens
$1.32 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$3.96 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.76 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1.05M tokens~1,573 A4 pages of text
Max Output Tokens
Per single request
128K131K
Throughput Speed
Tokens generated per second
80 tok/s58 tok/s
SWE-bench Verified
Real-world software engineering
68.5%74.5%
GPQA Diamond
Graduate-level reasoning
81.2%79%
Cached Input Price
Prompt caching discount per 1M tokens
$0.14 / 1M$0.00 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Kimi K3
+6.0% SWE-bench lead
Cost
Kimi K3
1.0x cheaper monthly runtime
Speed
DeepSeek V4 Pro
+38% faster generation
Context
Kimi K3
1.05M tokens window

权威基准测试得分对比

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

DeepSeek V4 ProKimi K3
SWE-bench Verified (Autonomous Coding)

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

68.5%vs74.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

81.2%vs79%
MMLU-Pro (Multi-task Language Understanding)

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

88.9%vs88.8%
LMSYS Chatbot Arena Elo

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

1438ptsvs1480pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

15.6%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

46.5%vsN/A
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

43%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

70.2%vsN/A
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

91%vsN/A
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

90%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs58 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
DeepSeek V4 Pro0.58s
Kimi K30.72s
Est. End-to-End Response (500 tokens)TTFT + generation time
DeepSeek V4 Pro6.83s
Kimi K39.34s

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)
DeepSeek V4 Pro Monthly API Bill
$5.75 / month
$1.32/1M in · $3.96/1M out
Kimi K3 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Kimi K3 saves approximately $5.75 / month (100%) under this workload.1.0x Savings

DeepSeek V4 Pro · Strengths & Key Advantages

  • 由 DeepSeek 研发的高精度 Next-Gen Sparse MoE with Agent RL Tuning 架构，支持 1M 上下文窗口
  • 极具竞争力的 API 定价（输入 $1.32/1M，输出 $3.96/1M），支持高速推理与工具调用
  • 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
  • 超长上下文请求需要规划 Token 成本与并发配额
  • 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果

Kimi K3 · Strengths & Key Advantages

  • 由 Moonshot AI 研发的高精度 Colossus-Scale Multimodal Sparse Mixture-of-Experts 架构，支持 1.0M 上下文窗口
  • 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
  • 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
  • 超长上下文请求需要规划 Token 成本与并发配额
  • 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果

Token Use & Task Economics

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

MetricDeepSeek V4 ProKimi K3
Cost per Task
Estimated USD to finish one task
$0.67$2.00
Output Tokens / Task
Tokens generated per task
52K35K
Reasoning Tokens / Task
Thinking tokens burned per task
25K18K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureDeepSeek V4 ProKimi K3
Official API Model ID (SDK)deepseek-reasonerkimi-k3
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-082026-07
Knowledge CutoffMay 2026March 2026
ArchitectureNext-Gen Sparse MoE with Agent RL TuningColossus-Scale Multimodal Sparse Mixture-of-Experts
ParametersMoE (Colossal)2.8T total (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)Flat rate ($1.32/1M in) across full 1000K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix caching at $0.14/1M (no extra cache-write fee)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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)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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 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 Modalitiestexttext, vision
Output Modalitiestexttext

DeepSeek V4 Pro vs Kimi K3: 常见问题解答 (FAQ)

Which is more intelligent, DeepSeek V4 Pro or Kimi K3?

Kimi K3 is more intelligent. It scores 93 compared with 53 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4 Pro is faster, generating 80 tokens per second compared with 58 tokens per second.

Which model is cheaper to run?

Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $0.76 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek V4 Pro has lower latency, with a time to first token of 0.58s compared with 0.72s.

Which model has a larger context window?

Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1M tokens.

综合结论

Choose DeepSeek V4 Pro for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 deepseek 生态深度集成, or choose Kimi K3 for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 moonshot ai 生态深度集成.

常见问题 (FAQ)

Which is cheaper: DeepSeek V4 Pro or Kimi K3?+

DeepSeek V4 Pro costs $1.32/1M input and $3.96/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: DeepSeek V4 Pro or Kimi K3?+

DeepSeek V4 Pro supports 1M tokens whereas Kimi K3 supports 1.0M tokens.

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: DeepSeek & Moonshot AI Official Documentation