Head-to-Head Model Comparison

Command R7B Arabic vs Mistral Small 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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

Modello Base a Pesi Aperti

Command R7B Arabic di Cohere è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 135 token/s, indice di intelligenza di 39/

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

Modello Base a Pesi Aperti

Mistral Small 3 di Mistral AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 32K, velocità di 105 token/s, indice di intelligenza di 41/

Intelligence Index41 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

32K tokens

SWE-bench Verified:

31.5%

Differenze Chiave a Colpo d’Occhio

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 →

Feature	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

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Mistral Small 3

+5.5% SWE-bench lead

Cost

Command R7B Arabic

1.6x cheaper monthly runtime

Speed

Command R7B Arabic

+29% faster generation

Context

Command R7B Arabic

128K tokens window

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 128K con capacità massima di output fino a 4K token (SWE-bench Verified 26% · GPQA Diamond 43%)
Throughput elevato di 135 token/secondo con latenza al primo token (TTFT) di soli 310 ms
Pesi aperti (7B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Mistral Small 3 · Strengths & Key Advantages

Finestra di contesto da 32K con capacità massima di output fino a 8K token (SWE-bench Verified 31.5% · GPQA Diamond 46.5%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 380 ms
Pesi aperti (24B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	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: Risposte Rapide (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.

Verdetto

Sia Command R7B Arabic (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Mistral Small 3 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mistral Small 3 guida nei benchmark di ragionamento e programmazione, Command R7B Arabic garantisce una generazione più rapida e Command R7B Arabic offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Command R7B Arabic o Mistral Small 3?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mistral Small 3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Command R7B Arabic e Mistral Small 3?+

Command R7B Arabic costa $0.07 / $0.20 per milione di token di input/output, mentre Mistral Small 3 costa $0.10 / $0.30. Nel costo combinato, Command R7B Arabic risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Command R7B Arabic supporta 128K token di contesto contro 32K token di Mistral Small 3, mentre Command R7B Arabic offre una velocità di generazione superiore.

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 e Confronta Qualsiasi Modello

Command R7B Arabic

Mistral Small 3

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Command R7B Arabic · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Small 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Command R7B Arabic vs Mistral Small 3: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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