Head-to-Head Model Comparison

GPT-OSS 120B vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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GPT-OSS 120BProfile

Llama 5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

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OpenAI

GPT-OSS 120B

Modelo Base de Pesos Abiertos

GPT-OSS 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 95 tokens/s, índice de inteligencia de 56/100 y tar

Intelligence Index56 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

45.5%

Llama 5

Modelo Base de Pesos Abiertos

Llama 5 de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 5M, velocidad de 65 tokens/s, índice de inteligencia de 94/100 y tarifa de ac

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Diferencias Clave de un Vistazo

Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
Llama 5 has a larger context window (5M vs 131K tokens).
GPT-OSS 120B is faster at 95 tok/s vs 65 tok/s.
Llama 5 leads on SWE-bench coding (74.0% vs 45.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	GPT-OSS 120B	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	56	94
Provider	OpenAI	Meta
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	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	33K	128K
Throughput Speed Tokens generated per second	95 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	45.5%	74%
GPQA Diamond Graduate-level reasoning	68.5%	84%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.00 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Llama 5

+28.5% SWE-bench lead

Costo

Llama 5

1.0x cheaper monthly runtime

Velocidad

GPT-OSS 120B

+46% faster generation

Contexto

Llama 5

5M tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

GPT-OSS 120BLlama 5

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

45.5%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

68.5%vs84%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

80.5%vs87%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1330ptsvs1485pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

9.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

30.5%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

33%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.5%vs68%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

84.5%vs88%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

71%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs65 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

GPT-OSS 120B0.48s

Llama 50.85s

Est. End-to-End Response (500 tokens)TTFT + generation time

GPT-OSS 120B5.74s

Llama 58.54s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

GPT-OSS 120B Monthly API Bill

$0.79 / month

$0.15/1M in · $0.6/1M out

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 saves approximately $0.79 / month (100%) under this workload.1.0x Savings

GPT-OSS 120B · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 33K tokens de salida máxima (SWE-bench Verified 45.5% · GPQA Diamond 68.5%)
Rendimiento ágil de 95 tokens/segundo con latencia inicial (TTFT) de solo 480 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

Llama 5 · Strengths & Key Advantages

Ventana de contexto de 5M con hasta 128K tokens de salida máxima (SWE-bench Verified 74% · GPQA Diamond 84%)
Rendimiento ágil de 65 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
Pesos abiertos (600B+ total (~40-80B )) 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 120B	Llama 5
Cost per Task Estimated USD to finish one task	$0.14	$0.00
Output Tokens / Task Tokens generated per task	45K	40K
Reasoning Tokens / Task Thinking tokens burned per task	22K	20K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	GPT-OSS 120B	Llama 5
Official API Model ID (SDK)	gpt-oss-120b	meta-llama/Llama-5
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2026-04
Knowledge Cutoff	June 2024	January 2026
Architecture	Sparse MoE transformer with reasoning	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	117B (5.1B active MoE)	600B+ total (~40-80B active)
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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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) · 372 GB VRAM (4-bit Quantized) · 1290 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, video, audio
Output Modalities	text	text

GPT-OSS 120B vs Llama 5: Respuestas Rápidas (FAQ)

Which is more intelligent, GPT-OSS 120B or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.

Which model is faster?

GPT-OSS 120B is faster, generating 95 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Llama 5 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 120B has lower latency, with a time to first token of 0.48s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 131.1K tokens.

Veredicto

Tanto GPT-OSS 120B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Llama 5 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Llama 5 lidera en inteligencia general y programación, GPT-OSS 120B ofrece mayor velocidad de generación de tokens y Llama 5 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, GPT-OSS 120B o Llama 5?+

En las pruebas de inteligencia compuesta y resolución de código, Llama 5 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de GPT-OSS 120B frente a Llama 5?+

GPT-OSS 120B cuesta $0.15 / $0.60 por millón de tokens de entrada/salida, mientras que Llama 5 cuesta $0.00 / $0.00. En costo combinado, Llama 5 es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

GPT-OSS 120B cuenta con 131.1K tokens de contexto frente a 5M tokens de Llama 5. En velocidad de inferencia, GPT-OSS 120B ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

GPT-OSS 120B vs Llama 5

Cambia y Compara Cualquier Modelo

GPT-OSS 120B

Llama 5

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

GPT-OSS 120B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

GPT-OSS 120B vs Llama 5: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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