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Head-to-Head Model Comparison

Command R+ vs Command R

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial
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Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command R+ vs Command R: Benchmarks, Pricing & Speed Comparison

Command R+ de Cohere y Command R de Cohere compiten directamente en el ecosistema actual de IA. Command R+ ofrece una ventana de contexto de 128K tokens a $2.50/$10.00 por millón de tokens (entrada/salida), mientras que Command R ofrece 128K tokens de contexto a $0.15/$0.60 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.

Comparación Directa (VS)

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Cohere

Command R+

Modelo Base de Pesos Abiertos

Command R+ 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 65 tokens/s, índice de inteligencia de 48/100 y tarif

Intelligence Index48 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.5%

Cohere

Command R

Modelo Base de Pesos Abiertos

Command R 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 85 tokens/s, índice de inteligencia de 43/100 y tarifa

Intelligence Index43 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32%

Diferencias Clave de un Vistazo

  • Command R is 16.5x cheaper for API usage (input + output tokens combined).
  • Command R is faster at 85 tok/s vs 65 tok/s.
  • Command R+ leads on SWE-bench coding (41.5% vs 32.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaracterísticaCommand R+Command R
Intelligence Index
Composite reasoning & knowledge score (0-100)
4843
Provider
CohereCohere
Input Price
Cost per million input tokens
$2.50 / 1M$0.15 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$0.60 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.02 / 1M$0.13 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K4K
Throughput Speed
Tokens generated per second
65 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
41.5%32%
GPQA Diamond
Graduate-level reasoning
56.5%46%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.05 / 1M

¿Quién gana en cada área?

At-a-glance decision framework
Código
Command R+
+9.5% SWE-bench lead
Costo
Command R
16.5x cheaper monthly runtime
Velocidad
Command R
+31% faster generation
Contexto
Tied
128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Command R+Command R
SWE-bench Verified (Autonomous Coding)

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

41.5%vs32%
GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs46%
MMLU-Pro (Multi-task Language Understanding)

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

78.5%vs71%
LMSYS Chatbot Arena Elo

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

1300ptsvs1240pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vs28%
AIME (Advanced Math Competition)

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

66%vs52%
Long-Context Reasoning

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

80%vs75%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs85 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 R+0.58s
Command R0.42s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command R+8.27s
Command R6.30s

Simulador Interactivo de Costos de Tokens

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Command R+ Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
Command R Monthly API Bill
$0.84 / month
$0.15/1M in · $0.6/1M out
Command R saves approximately $13.04 / month (94%) under this workload.16.5x Savings

Command R+ · Strengths & Key Advantages

  • Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 41.5% · GPQA Diamond 56.5%)
  • Rendimiento ágil de 65 tokens/segundo con latencia inicial (TTFT) de solo 580 ms
  • Pesos abiertos (104B) 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 volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Command R · Strengths & Key Advantages

  • Ventana de contexto de 128K con hasta 4K tokens de salida máxima (SWE-bench Verified 32% · GPQA Diamond 46%)
  • Rendimiento ágil de 85 tokens/segundo con latencia inicial (TTFT) de solo 420 ms
  • Pesos abiertos (35B) 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.

MetricCommand R+Command R
Cost per Task
Estimated USD to finish one task
$0.65$0.12
Output Tokens / Task
Tokens generated per task
35K25K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

CaracterísticaCommand R+Command R
Official API Model ID (SDK)command-r-pluscommand-r
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-082024-08
Knowledge CutoffJune 2024June 2024
ArchitectureEnterprise-Grade Dense TransformerRetrieval-Augmented Dense Transformer
Parameters104B35B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($2.5/1M in) across full 128K windowFlat rate ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensCached input at $0.05/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 64 GB VRAM (4-bit Quantized) · 224 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 22 GB VRAM (4-bit Quantized) · 75 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Command R+ vs Command R: Respuestas Rápidas (FAQ)

Which is more intelligent, Command R+ or Command R?

Command R+ is more intelligent. It scores 48 compared with 43 on the composite Intelligence Index.

Which model is faster?

Command R is faster, generating 85 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Command R is cheaper at $0.13 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command R has lower latency, with a time to first token of 0.42s compared with 0.58s.

Veredicto

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

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Command R+ o Command R?+

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

¿Cuánto cuesta la API de Command R+ frente a Command R?+

Command R+ cuesta $2.50 / $10.00 por millón de tokens de entrada/salida, mientras que Command R cuesta $0.15 / $0.60. En costo combinado, Command R es la opción más económica.

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

Command R+ cuenta con 128K tokens de contexto frente a 128K tokens de Command R. En velocidad de inferencia, Command R 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: Documentación oficial de API de Cohere y Cohere, tarjetas de sistema y benchmarks independientes (2026).