Head-to-Head Model Comparison

Phi-4 Multimodal vs Command R7B Arabic

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Phi-4 MultimodalProfile

Command R7B ArabicProfile

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Popular Matchups:

Microsoft

Phi-4 Multimodal

Nativ Multimodale KI

Phi-4 Multimodal von Microsoft ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 128K Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 75/100 und kostenlosem Open-W

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

Open-Weights-Basismodell

Command R7B Arabic von Cohere ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 135 Token/s Durchsatz, einem Intelligenz-Index von 39/100 und $0.07/$0.2 pro 1M Toke

Intelligence Index39 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

26%

Wichtigste Unterschiede auf einen Blick

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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

128K Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 71% · Arena Elo 1250)
Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 230 ms Erstlatenz (TTFT)
Offene Modellgewichte (5.6B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Command R7B Arabic · Strengths & Key Advantages

128K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 26% · GPQA Diamond 43%)
Hohe Generierungsgeschwindigkeit von 135 Token/Sekunde bei nur 310 ms Erstlatenz (TTFT)
Offene Modellgewichte (7B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl Phi-4 Multimodal (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Command R7B Arabic (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Phi-4 Multimodal führt bei komplexem Reasoning und Coding, Phi-4 Multimodal liefert den höheren Token-Durchsatz und Phi-4 Multimodal ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Phi-4 Multimodal oder Command R7B Arabic?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Phi-4 Multimodal vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Phi-4 Multimodal und Command R7B Arabic?+

Phi-4 Multimodal kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Command R7B Arabic liegt bei $0.07 / $0.20. Beim gewichteten Gesamtpreis ist Phi-4 Multimodal günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Phi-4 Multimodal bietet 128K Tokens Kontext gegenüber 128K Tokens bei Command R7B Arabic. Bei der Generierungsgeschwindigkeit liegt Phi-4 Multimodal vorn.

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

Beliebige Modelle wechseln & vergleichen

Phi-4 Multimodal

Command R7B Arabic

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Phi-4 Multimodal · Strengths & Key Advantages

Considerations & Trade-offs

Command R7B Arabic · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Phi-4 Multimodal vs Command R7B Arabic: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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