Head-to-Head Model Comparison

Command R7B Arabic vs Mistral Small 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Command R7B ArabicProfile

Mistral Small 3Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R7B Arabic

Modelo Base de Pesos Abiertos

Command R7B Arabic 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 135 tokens/s, índice de inteligencia de 39/10

Intelligence Index39 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

26%

Mistral AI

Mistral Small 3

Modelo Base de Pesos Abiertos

Mistral Small 3 de Mistral AI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 32K, velocidad de 105 tokens/s, índice de inteligencia de 41/10

Intelligence Index41 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

32K tokens

SWE-bench Verified:

31.5%

Diferencias Clave de un Vistazo

Command R7B Arabic is 1.6x cheaper for API usage (input + output tokens combined).
Command R7B Arabic has a larger context window (128K vs 32K tokens).
Command R7B Arabic is faster at 135 tok/s vs 105 tok/s.
Mistral Small 3 leads on SWE-bench coding (31.5% vs 26.0%).

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

Característica	Command R7B Arabic	Mistral Small 3
Intelligence Index Composite reasoning & knowledge score (0-100)	39	41
Provider	Cohere	Mistral AI
Input Price Cost per million input tokens	$0.07 / 1M	$0.10 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.09 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	32K tokens~48 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	135 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	26%	31.5%
GPQA Diamond Graduate-level reasoning	43%	46.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.05 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Mistral Small 3

+5.5% SWE-bench lead

Costo

Command R7B Arabic

1.6x cheaper monthly runtime

Velocidad

Command R7B Arabic

+29% faster generation

Contexto

Command R7B Arabic

128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Command R7B ArabicMistral Small 3

SWE-bench Verified (Autonomous Coding)

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

26%vs31.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

43%vs46.5%

MMLU-Pro (Multi-task Language Understanding)

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

67%vs70.2%

LMSYS Chatbot Arena Elo

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

1225ptsvs1238pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs27%

AIME (Advanced Math Competition)

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

N/Avs52%

Long-Context Reasoning

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

70%vs64%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 tok/s vs105 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 R7B Arabic0.31s

Mistral Small 30.38s

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

Command R7B Arabic4.01s

Mistral Small 35.14s

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 Arabic Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Mistral Small 3 Monthly API Bill

$0.50 / month

$0.1/1M in · $0.3/1M out

Command R7B Arabic saves approximately $0.18 / month (36%) under this workload.1.6x Savings

Command R7B Arabic · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 4K tokens de salida máxima (SWE-bench Verified 26% · GPQA Diamond 43%)
Rendimiento ágil de 135 tokens/segundo con latencia inicial (TTFT) de solo 310 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

Mistral Small 3 · Strengths & Key Advantages

Ventana de contexto de 32K con hasta 8K tokens de salida máxima (SWE-bench Verified 31.5% · GPQA Diamond 46.5%)
Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 380 ms
Pesos abiertos (24B) 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 Arabic	Mistral Small 3
Cost per Task Estimated USD to finish one task	$0.05	$0.08
Output Tokens / Task Tokens generated per task	20K	25K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Command R7B Arabic	Mistral Small 3
Official API Model ID (SDK)	command-r7b-arabic	mistral-small-3-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-02	2025-01
Knowledge Cutoff	December 2024	December 2024
Architecture	Arabic-Specialized RAG Transformer	Compact Dense Transformer
Parameters	7B	24B
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 ($0.1/1M in) across full 32K window
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Cached input at $0.05/1M 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) · 15 GB VRAM (4-bit Quantized) · 52 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 Mistral Small 3: Respuestas Rápidas (FAQ)

Which is more intelligent, Command R7B Arabic or Mistral Small 3?

Mistral Small 3 is more intelligent. It scores 41 compared with 39 on the composite Intelligence Index.

Which model is faster?

Command R7B Arabic is faster, generating 135 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

Command R7B Arabic is cheaper at $0.05 per 1M tokens blended, compared with $0.09 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command R7B Arabic has lower latency, with a time to first token of 0.31s compared with 0.38s.

Which model has a larger context window?

Command R7B Arabic has a larger context window, supporting 128K tokens compared with 32K tokens.

Veredicto

Tanto Command R7B Arabic (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Mistral Small 3 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Mistral Small 3 lidera en inteligencia general y programación, Command R7B Arabic ofrece mayor velocidad de generación de tokens y Command R7B Arabic resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Command R7B Arabic o Mistral Small 3?+

En las pruebas de inteligencia compuesta y resolución de código, Mistral Small 3 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Command R7B Arabic frente a Mistral Small 3?+

Command R7B Arabic cuesta $0.07 / $0.20 por millón de tokens de entrada/salida, mientras que Mistral Small 3 cuesta $0.10 / $0.30. En costo combinado, Command R7B Arabic es la opción más económica.

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

Command R7B Arabic cuenta con 128K tokens de contexto frente a 32K tokens de Mistral Small 3. En velocidad de inferencia, Command R7B Arabic 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 Arabic vs Mistral Small 3

Cambia y Compara Cualquier Modelo

Command R7B Arabic

Mistral Small 3

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Command R7B Arabic · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Small 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Command R7B Arabic vs Mistral Small 3: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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