Kimi K2 Thinking Turbo vs Claude Opus 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Kimi K2 Thinking Turbo by Moonshot AI and Claude Opus 4.5 by Anthropic represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Kimi K2 Thinking Turbo holds a notable pricing advantage for token ingestion. On output generation, Kimi K2 Thinking Turbo is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Kimi K2 Thinking Turbo provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Kimi K2 Thinking Turbo leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Kimi K2 Thinking Turbo
深度推理模型Moonshot AI 推出的 Kimi K2 Thinking Turbo 人工智能模型，具备 262K 上下文窗口，输入价格为 $0.6/百万 Token，输出价格为 $2.4/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.60 / 1M
$2.40 / 1M
262.1K tokens
N/A
Claude Opus 4.5
深度推理模型Anthropic 推出的 Claude Opus 4.5 人工智能模型，具备 200K 上下文窗口，输入价格为 $5/百万 Token，输出价格为 $25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$5.00 / 1M
$25.00 / 1M
200K tokens
77.2%
核心差异一览
- Kimi K2 Thinking Turbo is 9.8x cheaper for API usage (input + output tokens combined).
- Kimi K2 Thinking Turbo has a larger context window (262K vs 200K tokens).
- Kimi K2 Thinking Turbo is faster at 90 tok/s vs 62 tok/s.
| Feature | Kimi K2 Thinking Turbo | Claude Opus 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 63 |
Provider | Moonshot AI | Anthropic |
Input Price Cost per million input tokens | $0.60 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $25.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.40 / 1M | $3.85 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 16K | 64K |
Throughput Speed Tokens generated per second | 90 tok/s | 62 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 77.2% |
GPQA Diamond Graduate-level reasoning | 68.5% | 74.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.50 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
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.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2 Thinking Turbo · Strengths & Key Advantages
- 由 Moonshot AI 研发的高精度 Hosted Accelerated Reasoning Engine 架构，支持 262K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.6/1M，输出 $2.4/1M），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
Claude Opus 4.5 · Strengths & Key Advantages
- 由 Anthropic 研发的高精度 Hybrid Reasoning 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $5/1M，输出 $25/1M），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.
| Metric | Kimi K2 Thinking Turbo | Claude Opus 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $5.86 |
Output Tokens / Task Tokens generated per task | 22K | 73K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 43K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2 Thinking Turbo | Claude Opus 4.5 |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-thinking-turbo | claude-opus-4-5-20251101 |
| API Protocol | Moonshot AI REST API (OpenAI-Compatible) | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-11 | 2025-11 |
| Knowledge Cutoff | September 2025 | March 2025 |
| Architecture | Hosted Accelerated Reasoning Engine | Hybrid Reasoning |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.6/1M in) across full 262K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | $0.5/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Moonshot AI Production Safety & Alignment Verified | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Kimi K2 Thinking Turbo vs Claude Opus 4.5: 常见问题解答 (FAQ)
Which is more intelligent, Kimi K2 Thinking Turbo or Claude Opus 4.5?
Kimi K2 Thinking Turbo is more intelligent. It scores 84 compared with 63 on the composite Intelligence Index.
Which model is faster?
Kimi K2 Thinking Turbo is faster, generating 90 tokens per second compared with 62 tokens per second.
Which model is cheaper to run?
Kimi K2 Thinking Turbo is cheaper at $0.40 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K2 Thinking Turbo has lower latency, with a time to first token of 0.52s compared with 2.10s.
Which model has a larger context window?
Kimi K2 Thinking Turbo has a larger context window, supporting 262.1K tokens compared with 200K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Kimi K2 Thinking Turbo if your priority is Production reasoning pipelines, automated code reviews, and low-latency research bots. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 4.5 if your workflow prioritizes Complex reasoning, creative work, and high-stakes decision making where quality outweighs cost. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Kimi K2 Thinking Turbo compare to Claude Opus 4.5 in API token pricing and cost per token?+
Kimi K2 Thinking Turbo costs $0.60 per 1M input tokens and $2.40 per 1M output tokens, whereas Claude Opus 4.5 costs $5.00 input and $25.00 output. Overall, Kimi K2 Thinking Turbo is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Kimi K2 Thinking Turbo or Claude Opus 4.5?+
Kimi K2 Thinking Turbo supports a context window of 262K tokens (~197k words), while Claude Opus 4.5 supports 200K tokens (~150k words). Therefore, Kimi K2 Thinking Turbo can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Kimi K2 Thinking Turbo or Claude Opus 4.5?+
Kimi K2 Thinking Turbo scores at the frontier of coding benchmarks, while Claude Opus 4.5 scores 77.2%. For complex autonomous debugging and agentic engineering, Claude Opus 4.5 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Kimi K2 Thinking Turbo and Claude Opus 4.5?+
Kimi K2 Thinking Turbo features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 4.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Kimi K2 Thinking Turbo and Claude Opus 4.5?+
Kimi K2 Thinking Turbo generates approximately 90 tokens per second, while Claude Opus 4.5 delivers 62 tokens per second. In latency-sensitive production environments, Kimi K2 Thinking Turbo provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Kimi K2 Thinking Turbo and Claude Opus 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Kimi K2 Thinking Turbo to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.5. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs