Head-to-Head Model Comparison

Command R7B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Command R7BProfile

Llama 3.3 70BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R7B

Modelo Base de Pesos Abiertos

Command R7B de Cohere es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 140 tokens/s, índice de inteligencia de 38/100 y tar

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

27%

Llama 3.3 70B

Modelo Base de Pesos Abiertos

Llama 3.3 70B 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 128K, velocidad de 280 tokens/s, índice de inteligencia de 38/100 y tar

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Diferencias Clave de un Vistazo

Llama 3.3 70B is 2.1x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 140 tok/s.
Llama 3.3 70B leads on SWE-bench coding (39.5% vs 27.0%).

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

Característica	Command R7B	Llama 3.3 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	38	38
Provider	Cohere	Meta
Input Price Cost per million input tokens	$0.07 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	4K
Throughput Speed Tokens generated per second	140 tok/s	280 tok/s
SWE-bench Verified Real-world software engineering	27%	39.5%
GPQA Diamond Graduate-level reasoning	42.5%	48.9%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.10 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Llama 3.3 70B

+12.5% SWE-bench lead

Costo

Llama 3.3 70B

2.1x cheaper monthly runtime

Velocidad

Llama 3.3 70B

+100% faster generation

Contexto

Tied

128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Command R7BLlama 3.3 70B

SWE-bench Verified (Autonomous Coding)

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

27%vs39.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

42.5%vs48.9%

MMLU-Pro (Multi-task Language Understanding)

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

66%vs71%

LMSYS Chatbot Arena Elo

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

1215ptsvs1285pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs7.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.1%

SciCode (Scientific Computing)

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

N/Avs23.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.5%

AIME (Advanced Math Competition)

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

N/Avs56.2%

Long-Context Reasoning

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

68%vs63.9%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs280 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

Command R7B0.29s

Llama 3.3 70B0.25s

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

Command R7B3.86s

Llama 3.3 70B2.04s

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)

Command R7B Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Llama 3.3 70B saves approximately $0.17 / month (52%) under this workload.2.1x Savings

Command R7B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 4K tokens de salida máxima (SWE-bench Verified 27% · GPQA Diamond 42.5%)
Rendimiento ágil de 140 tokens/segundo con latencia inicial (TTFT) de solo 290 ms
Pesos abiertos (7B) 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 3.3 70B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 4K tokens de salida máxima (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Rendimiento ágil de 280 tokens/segundo con latencia inicial (TTFT) de solo 250 ms
Pesos abiertos (70B) 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	Command R7B	Llama 3.3 70B
Cost per Task Estimated USD to finish one task	$0.05	$0.21
Output Tokens / Task Tokens generated per task	20K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Command R7B	Llama 3.3 70B
Official API Model ID (SDK)	command-r7b	meta-llama/Llama-3.3-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-12
Knowledge Cutoff	October 2024	December 2023
Architecture	Efficient Compact RAG Transformer	Dense Autoregressive Transformer
Parameters	7B	70B
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 ($0.07/1M in) across full 128K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	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)	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)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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	text
Output Modalities	text	text

Command R7B vs Llama 3.3 70B: Respuestas Rápidas (FAQ)

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Command R7B is cheaper at $0.05 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.29s.

Veredicto

Tanto Command R7B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Llama 3.3 70B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Command R7B lidera en inteligencia general y programación, Llama 3.3 70B ofrece mayor velocidad de generación de tokens y Command R7B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Command R7B o Llama 3.3 70B?+

En las pruebas de inteligencia compuesta y resolución de código, Command R7B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Command R7B frente a Llama 3.3 70B?+

Command R7B cuesta $0.07 / $0.20 por millón de tokens de entrada/salida, mientras que Llama 3.3 70B cuesta $0.00 / $0.00. En costo combinado, Command R7B es la opción más económica.

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

Command R7B cuenta con 128K tokens de contexto frente a 128K tokens de Llama 3.3 70B. En velocidad de inferencia, Llama 3.3 70B 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)

Command R7B vs Llama 3.3 70B

Cambia y Compara Cualquier Modelo

Command R7B

Llama 3.3 70B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Command R7B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Command R7B vs Llama 3.3 70B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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