Head-to-Head Model Comparison

Phi-4 Multimodal vs Command R7B Arabic

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.

Phi-4 MultimodalProfile

Command R7B ArabicProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Microsoft

Phi-4 Multimodal

IA Multimodale Nativa

Phi-4 Multimodal di Microsoft è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 128K, velocità di 160 token/s, indice di intelligenza di 75

Intelligence Index75 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

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%

Differenze Chiave a Colpo d’Occhio

Phi-4 Multimodal is 1.0x cheaper for API usage (input + output tokens combined).
Phi-4 Multimodal is faster at 160 tok/s vs 135 tok/s.

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

Feature	Phi-4 Multimodal	Command R7B Arabic
Intelligence Index Composite reasoning & knowledge score (0-100)	75	39
Provider	Microsoft	Cohere
Input Price Cost per million input tokens	$0.00 / 1M	$0.07 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.20 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 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	8K	4K
Throughput Speed Tokens generated per second	160 tok/s	135 tok/s
SWE-bench Verified Real-world software engineering	N/A	26%
GPQA Diamond Graduate-level reasoning	N/A	43%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.02 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Phi-4 Multimodal

1.0x cheaper monthly runtime

Speed

Phi-4 Multimodal

+19% faster generation

Context

Tied

128K tokens window

Punteggi Benchmark Verificati

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

Phi-4 MultimodalCommand R7B Arabic

SWE-bench Verified (Autonomous Coding)

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

N/Avs26%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs43%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs67%

LMSYS Chatbot Arena Elo

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

1250ptsvs1225pts

Long-Context Reasoning

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

N/Avs70%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs135 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

Phi-4 Multimodal0.23s

Command R7B Arabic0.31s

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

Phi-4 Multimodal3.35s

Command R7B Arabic4.01s

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)

Phi-4 Multimodal Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Command R7B Arabic Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Phi-4 Multimodal saves approximately $0.32 / month (100%) under this workload.1.0x Savings

Phi-4 Multimodal · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (MMLU-Pro 71% · Arena Elo 1250)
Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 230 ms
Pesi aperti (5.6B) 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

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

Token Use & Task Economics

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

Metric	Phi-4 Multimodal	Command R7B Arabic
Cost per Task Estimated USD to finish one task	$0.00	$0.05
Output Tokens / Task Tokens generated per task	10K	20K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Phi-4 Multimodal	Command R7B Arabic
Official API Model ID (SDK)	phi-4-multimodal	command-r7b-arabic
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-02	2025-02
Knowledge Cutoff	November 2024	December 2024
Architecture	Compact Tri-Modal Foundation Transformer	Arabic-Specialized RAG Transformer
Parameters	5.6B	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)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($0.07/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 3 GB VRAM (4-bit Quantized) · 12 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, vision, audio	text
Output Modalities	text	text

Phi-4 Multimodal vs Command R7B Arabic: Risposte Rapide (FAQ)

Which is more intelligent, Phi-4 Multimodal or Command R7B Arabic?

Phi-4 Multimodal is more intelligent. It scores 75 compared with 39 on the composite Intelligence Index.

Which model is faster?

Phi-4 Multimodal is faster, generating 160 tokens per second compared with 135 tokens per second.

Which model is cheaper to run?

Phi-4 Multimodal is cheaper at $0.00 per 1M tokens blended, compared with $0.05 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Phi-4 Multimodal has lower latency, with a time to first token of 0.23s compared with 0.31s.

Verdetto

Sia Phi-4 Multimodal (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) 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.) eccellono in produzione: Phi-4 Multimodal guida nei benchmark di ragionamento e programmazione, Phi-4 Multimodal garantisce una generazione più rapida e Phi-4 Multimodal offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Phi-4 Multimodal o Command R7B Arabic?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Phi-4 Multimodal. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Phi-4 Multimodal e Command R7B Arabic?+

Phi-4 Multimodal costa $0.00 / $0.00 per milione di token di input/output, mentre Command R7B Arabic costa $0.07 / $0.20. Nel costo combinato, Phi-4 Multimodal risulta più economico.

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

Phi-4 Multimodal supporta 128K token di contesto contro 128K token di Command R7B Arabic, mentre Phi-4 Multimodal 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)

Phi-4 Multimodal vs Command R7B Arabic

Cambia e Confronta Qualsiasi Modello

Phi-4 Multimodal

Command R7B Arabic

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Phi-4 Multimodal · Strengths & Key Advantages

Considerations & Trade-offs

Command R7B Arabic · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Phi-4 Multimodal vs Command R7B Arabic: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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