GPT-OSS 20B vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-OSS 20B by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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GPT-OSS 20B
オープンウェイトモデルOpenAI が提供する GPT-OSS 20B は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0.1/100万トークン・出力 $0.5/100万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。
$0.10 / 1M
$0.50 / 1M
131.1K tokens
32.5%
Kimi K3
オープンウェイトモデルMoonshot AI が提供する Kimi K3 は、最大 1.0M 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
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 131K tokens).
- GPT-OSS 20B is faster at 160 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 32.5%).
| Feature | GPT-OSS 20B | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 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 | $0.50 / 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 | 131.1K tokens~197 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 | 160 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 32.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 55% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 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-OSS 20B · Strengths & Key Advantages
- OpenAI の高度な Sparse MoE transformer with reasoning アーキテクチャと 131K 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 $0.1/1M、出力 $0.5/1M）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 K3 · Strengths & Key Advantages
- Moonshot AI の高度な Colossus-Scale Multimodal Sparse Mixture-of-Experts アーキテクチャと 1.0M 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 | GPT-OSS 20B | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $2.00 |
Output Tokens / Task Tokens generated per task | 40K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 18K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-OSS 20B | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-20b | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-08 | 2026-07 |
| Knowledge Cutoff | June 2024 | March 2026 |
| Architecture | Sparse MoE transformer with reasoning | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | 21B (3.6B active MoE) | 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 Cloud | 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 131K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.01/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 13 GB VRAM (4-bit Quantized) · 45 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 Classification | OpenAI Preparedness Framework Audited & System Card Published | 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 | text, vision |
| Output Modalities | text | text |
GPT-OSS 20B vs Kimi K3: よくある質問 (FAQ)
Which is more intelligent, GPT-OSS 20B or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 46 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 20B is faster, generating 160 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-OSS 20B has lower latency, with a time to first token of 0.26s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 131.1K tokens.
総合評価
Choose GPT-OSS 20B for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、openai エコシステムの統合, or choose Kimi K3 for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、moonshot ai エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: GPT-OSS 20B or Kimi K3?+
GPT-OSS 20B costs $0.1/1M input and $0.5/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-OSS 20B or Kimi K3?+
GPT-OSS 20B supports 131K 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