Kimi K2.5 vs Kimi K2 Thinking
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Kimi K2.5 by Moonshot AI and Kimi K2 Thinking by Moonshot AI 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 262K tokens. In latency-sensitive production environments, Kimi K2.5 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 K2.5
オープンウェイトモデルMoonshot AI が提供する Kimi K2.5 は、最大 262K トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
52%
Kimi K2 Thinking
オープンウェイトモデルMoonshot AI が提供する Kimi K2 Thinking は、最大 262K トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
50%
ひと目でわかる主な違い
- Both models cost the same under this workload — choose on capability, not price.
- Kimi K2.5 is faster at 65 tok/s vs 55 tok/s.
- Kimi K2.5 leads on SWE-bench coding (52.0% vs 50.0%).
| Feature | Kimi K2.5 | Kimi K2 Thinking |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 85 |
Provider | Moonshot AI | Moonshot 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 text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 32K |
Throughput Speed Tokens generated per second | 65 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | 52% | 50% |
GPQA Diamond Graduate-level reasoning | 69.5% | 70% |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2.5 · Strengths & Key Advantages
- Moonshot AI の高度な Multimodal Open Mixture-of-Experts Transformer アーキテクチャと 262K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Kimi K2 Thinking · Strengths & Key Advantages
- Moonshot AI の高度な Deliberate Reasoning Open Sparse Mixture-of-Experts アーキテクチャと 262K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K2.5 | Kimi K2 Thinking |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 24K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 16K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2.5 | Kimi K2 Thinking |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-5 | kimi-k2-thinking |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-01 | 2025-11 |
| Knowledge Cutoff | November 2025 | September 2025 |
| Architecture | Multimodal Open Mixture-of-Experts Transformer | Deliberate Reasoning Open Sparse Mixture-of-Experts |
| Parameters | 1.2T total (36B active MoE) | 1T total (32B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (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 & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full 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 Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 744 GB VRAM (4-bit Quantized) · 2580 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-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 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Kimi K2.5 vs Kimi K2 Thinking: よくある質問 (FAQ)
Which is more intelligent, Kimi K2.5 or Kimi K2 Thinking?
Kimi K2.5 is more intelligent. It scores 86 compared with 85 on the composite Intelligence Index.
Which model is faster?
Kimi K2.5 is faster, generating 65 tokens per second compared with 55 tokens per second.
Which model has lower latency?
Kimi K2.5 has lower latency, with a time to first token of 0.62s compared with 0.95s.
総合評価
Verdict & Model Selection Recommendation: Choose Kimi K2.5 if your priority is Multimodal enterprise document intelligence, visual bug fixing, and self-hosted agents. Its performance in demanding tasks justifies its operational token cost. Choose Kimi K2 Thinking if your workflow prioritizes Formal mathematical proofs, algorithmic architecture, and comprehensive scientific research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Kimi K2.5 compare to Kimi K2 Thinking in API token pricing and cost per token?+
Both Kimi K2.5 and Kimi K2 Thinking are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Kimi K2.5 or Kimi K2 Thinking?+
Both Kimi K2.5 and Kimi K2 Thinking offer an identical context window of 262K tokens (~197k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Kimi K2.5 or Kimi K2 Thinking?+
Kimi K2.5 scores 52% on SWE-bench Verified while Kimi K2 Thinking scores 50%. For autonomous software engineering and multi-file debugging, Kimi K2.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Kimi K2.5 and Kimi K2 Thinking?+
Kimi K2.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Kimi K2 Thinking provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Kimi K2.5 and Kimi K2 Thinking?+
Kimi K2.5 generates approximately 65 tokens per second, while Kimi K2 Thinking delivers 55 tokens per second. In latency-sensitive production environments, Kimi K2.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Kimi K2.5 and Kimi K2 Thinking to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Kimi K2.5, and route deep architectural or complex reasoning queries to Kimi K2.5. This balanced routing strategy maximizes system throughput and answer quality.
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs