GPT-4.1 nano vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-4.1 nano by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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GPT-4.1 nano
軽量・高コスト効率モデルOpenAI の GPT-4.1 nano は、優れたトークン単価効率と高速スループットを両立する高コスパ運用向けに設計されたAIモデルです。1.0M のコンテキストウィンドウ、毎秒 220 トークンの生成速度、知能指数 26/100（1Mトークンあたり入力$0.1・出力$0.4）を実現しています。
$0.10 / 1M
$0.40 / 1M
1.05M tokens
24%
Kimi K3
オープンウェイト推論モデルMoonshot AI の Kimi K3 は、強化学習により鍛えられた高効率なオープンウェイト論理推論向けに設計されたAIモデルです。1.0M のコンテキストウィンドウ、毎秒 58 トークンの生成速度、知能指数 93/100（無料のオープンウェイト提供）を実現しています。
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
ひと目でわかる主な違い
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 1M tokens).
- GPT-4.1 nano is faster at 220 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 24.0%).
| Feature | GPT-4.1 nano | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 26 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,571 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 33K | 131K |
Throughput Speed Tokens generated per second | 220 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 24% | 74.5% |
GPQA Diamond Graduate-level reasoning | 38% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.00 / 1M |
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.
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.
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.
GPT-4.1 nano · Strengths & Key Advantages
- 1.0M のコンテキストウィンドウと最大 33K トークンの出力容量（SWE-bench Verified 24% · GPQA Diamond 38%）
- 毎秒 220 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 220ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.1・出力$0.4）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Kimi K3 · Strengths & Key Advantages
- 1.0M のコンテキストウィンドウと最大 131K トークンの出力容量（SWE-bench Verified 74.5% · GPQA Diamond 79%）
- 毎秒 58 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 720ms の低遅延
- オープンウェイトモデル (2.8T total (MoE))として vLLM・SGLang・Ollama を用いた自社GPUでのローカル運用が可能
Considerations & Trade-offs
- 非量子化のフル精度でローカル推論を行うには十分なGPU VRAM容量が必要です
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4.1 nano | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $2.00 |
Output Tokens / Task Tokens generated per task | 42K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-4.1 nano | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-4-1-nano | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2026-07 |
| Knowledge Cutoff | June 2024 | March 2026 |
| Architecture | Dense multimodal transformer (nano) | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 1048K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.025/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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) · 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) | 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 Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-4.1 nano vs Kimi K3: よくある質問 (FAQ)
Which is more intelligent, GPT-4.1 nano or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 26 on the composite Intelligence Index.
Which model is faster?
GPT-4.1 nano is faster, generating 220 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 $0.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 nano has lower latency, with a time to first token of 0.22s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1.05M tokens.
総合評価
Choose GPT-4.1 nano for 優れたトークン単価効率と高速スループットを両立する高コスパ運用、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose Kimi K3 for 強化学習により鍛えられた高効率なオープンウェイト論理推論、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (FAQ)
Which is cheaper: GPT-4.1 nano or Kimi K3?+
GPT-4.1 nano costs $0.1/1M input and $0.4/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-4.1 nano or Kimi K3?+
GPT-4.1 nano supports 1.0M tokens whereas Kimi K3 supports 1.0M tokens.
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: OpenAI & Moonshot AI Official Documentation