Head-to-Head Model Comparison

Command R7B Arabic vs Command R7B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Command R7B ArabicProfile

Command R7BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R7B Arabic

Modelo Base de Pesos Abertos

O Command R7B Arabic da Cohere é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 135 tokens/s, índice de inteligência de 39/100

Intelligence Index39 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

26%

Cohere

Command R7B

Modelo Base de Pesos Abertos

O Command R7B da Cohere é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 140 tokens/s, índice de inteligência de 38/100 e tari

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

27%

Principais Diferenças em Resumo

Both models cost the same under this workload — choose on capability, not price.
Command R7B is faster at 140 tok/s vs 135 tok/s.
Command R7B leads on SWE-bench coding (27.0% vs 26.0%).

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

Feature	Command R7B Arabic	Command R7B
Intelligence Index Composite reasoning & knowledge score (0-100)	39	38
Provider	Cohere	Cohere
Input Price Cost per million input tokens	$0.07 / 1M	$0.07 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.20 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.05 / 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	135 tok/s	140 tok/s
SWE-bench Verified Real-world software engineering	26%	27%
GPQA Diamond Graduate-level reasoning	43%	42.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.02 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Command R7B

+1.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Command R7B

+4% faster generation

Context

Tied

128K tokens window

Pontuações de Benchmarks Verificadas

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

Command R7B ArabicCommand R7B

SWE-bench Verified (Autonomous Coding)

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

26%vs27%

GPQA Diamond (Doctorate-Level Science & Math)

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

43%vs42.5%

MMLU-Pro (Multi-task Language Understanding)

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

67%vs66%

LMSYS Chatbot Arena Elo

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

1225ptsvs1215pts

Long-Context Reasoning

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

70%vs68%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 tok/s vs140 tok/s

Latência e Tempo de Resposta

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 R7B Arabic0.31s

Command R7B0.29s

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

Command R7B Arabic4.01s

Command R7B3.86s

Simulador Interativo de Custo 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 Arabic Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Command R7B Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

These models cost the same under this workload — no savings either way.Break-even

Command R7B Arabic · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 4K tokens (SWE-bench Verified 26% · GPQA Diamond 43%)
Taxa de transferência rápida de 135 tokens/segundo com latência inicial (TTFT) de 310 ms
Pesos abertos (7B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Command R7B · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 4K tokens (SWE-bench Verified 27% · GPQA Diamond 42.5%)
Taxa de transferência rápida de 140 tokens/segundo com latência inicial (TTFT) de 290 ms
Pesos abertos (7B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Command R7B Arabic	Command R7B
Cost per Task Estimated USD to finish one task	$0.05	$0.05
Output Tokens / Task Tokens generated per task	20K	20K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Command R7B Arabic	Command R7B
Official API Model ID (SDK)	command-r7b-arabic	command-r7b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-02	2024-12
Knowledge Cutoff	December 2024	October 2024
Architecture	Arabic-Specialized RAG Transformer	Efficient Compact RAG Transformer
Parameters	7B	7B
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	Flat rate ($0.07/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Cached input at $0.02/1M tokens
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) · 4 GB VRAM (4-bit Quantized) · 15 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 Arabic vs Command R7B: Respostas Rápidas (FAQ)

Which is more intelligent, Command R7B Arabic or Command R7B?

Command R7B Arabic is more intelligent. It scores 39 compared with 38 on the composite Intelligence Index.

Which model is faster?

Command R7B is faster, generating 140 tokens per second compared with 135 tokens per second.

Which model has lower latency?

Command R7B has lower latency, with a time to first token of 0.29s compared with 0.31s.

Veredito

Tanto Command R7B Arabic (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Command R7B (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Command R7B Arabic lidera em inteligência geral e programação, Command R7B oferece geração mais rápida e Command R7B Arabic apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Command R7B Arabic ou Command R7B?+

Nas avaliações de inteligência e código, Command R7B Arabic leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Command R7B Arabic ou Command R7B?+

O Command R7B Arabic custa $0.07 / $0.20 por 1M de tokens (entrada/saída) e o Command R7B custa $0.07 / $0.20. No custo combinado, Command R7B Arabic é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Command R7B Arabic suporta 128K tokens de contexto contra 128K tokens do Command R7B, enquanto Command R7B entrega maior velocidade de geração.

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 Arabic vs Command R7B

Troque e Compare Qualquer Modelo

Command R7B Arabic

Command R7B

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Command R7B Arabic · Strengths & Key Advantages

Considerations & Trade-offs

Command R7B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Command R7B Arabic vs Command R7B: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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