GPT OSS Safeguard 120B vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT OSS Safeguard 120B by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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GPT OSS Safeguard 120B
Modelo Base de Pesos AbiertosGPT OSS Safeguard 120B de OpenAI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 131K, velocidad de 90 tokens/s, índice de inteligencia de 52
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Kimi K3
Razonamiento de Pesos AbiertosKimi K3 de Moonshot AI es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 1.0M, velocidad de 58 tokens/s, índice de inteligencia de 93/100 y ta
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Diferencias Clave de un Vistazo
- 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 Safeguard 120B is faster at 90 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 41.0%).
| Característica | GPT OSS Safeguard 120B | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.15 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 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 | 90 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 41% | 74.5% |
GPQA Diamond Graduate-level reasoning | 65% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT OSS Safeguard 120B · Strengths & Key Advantages
- Ventana de contexto de 131K con hasta 33K tokens de salida máxima (SWE-bench Verified 41% · GPQA Diamond 65%)
- Rendimiento ágil de 90 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
- Pesos abiertos (117B (5.1B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Kimi K3 · Strengths & Key Advantages
- Ventana de contexto de 1.0M con hasta 131K tokens de salida máxima (SWE-bench Verified 74.5% · GPQA Diamond 79%)
- Rendimiento ágil de 58 tokens/segundo con latencia inicial (TTFT) de solo 720 ms
- Pesos abiertos (2.8T total (MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT OSS Safeguard 120B | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.13 | $2.00 |
Output Tokens / Task Tokens generated per task | 40K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 18K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GPT OSS Safeguard 120B | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-safeguard-120b | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-10 | 2026-07 |
| Knowledge Cutoff | June 2024 | March 2026 |
| Architecture | Sparse MoE safety-classifier model | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | 117B (5.1B 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 ($0.15/1M in) across full 131K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.015/1M (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) · 73 GB VRAM (4-bit Quantized) · 252 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 Safeguard 120B vs Kimi K3: Respuestas Rápidas (FAQ)
Which is more intelligent, GPT OSS Safeguard 120B or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 52 on the composite Intelligence Index.
Which model is faster?
GPT OSS Safeguard 120B is faster, generating 90 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 $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT OSS Safeguard 120B has lower latency, with a time to first token of 0.52s 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.
Veredicto
Choose GPT OSS Safeguard 120B for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api., or choose Kimi K3 for ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
Which is cheaper: GPT OSS Safeguard 120B or Kimi K3?+
GPT OSS Safeguard 120B costs $0.15/1M input and $0.6/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT OSS Safeguard 120B or Kimi K3?+
GPT OSS Safeguard 120B 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: OpenAI & Moonshot AI Official Documentation