Head-to-Head Model Comparison

Phi-4 vs Qwen2.5-VL 72B

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-4Profile

Qwen2.5-VL 72BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Microsoft

Phi-4

Modello Base a Pesi Aperti

Phi-4 di Microsoft 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 16K, velocità di 135 token/s, indice di intelligenza di 80/100 e tarif

Intelligence Index80 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

16.4K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen2.5-VL 72B

IA Multimodale Nativa

Qwen2.5-VL 72B di Alibaba 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 131K, velocità di 55 token/s, indice di intelligenza di 79/100

Intelligence Index79 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

38%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Qwen2.5-VL 72B has a larger context window (131K vs 16K tokens).
Phi-4 is faster at 135 tok/s vs 55 tok/s.

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

Feature	Phi-4	Qwen2.5-VL 72B
Intelligence Index Composite reasoning & knowledge score (0-100)	80	79
Provider	Microsoft	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	16.4K tokens~25 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	135 tok/s	55 tok/s
SWE-bench Verified Real-world software engineering	N/A	38%
GPQA Diamond Graduate-level reasoning	55%	58%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Phi-4

+145% faster generation

Context

Qwen2.5-VL 72B

131.1K tokens window

Punteggi Benchmark Verificati

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

Phi-4Qwen2.5-VL 72B

SWE-bench Verified (Autonomous Coding)

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

N/Avs38%

GPQA Diamond (Doctorate-Level Science & Math)

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

55%vs58%

MMLU-Pro (Multi-task Language Understanding)

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

78.5%vs77.5%

LMSYS Chatbot Arena Elo

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

1305ptsvs1315pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 tok/s vs55 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-40.27s

Qwen2.5-VL 72B0.72s

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

Phi-43.97s

Qwen2.5-VL 72B9.81s

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 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen2.5-VL 72B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

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

Phi-4 · Strengths & Key Advantages

Finestra di contesto da 16K con capacità massima di output fino a 4K token (GPQA Diamond 55% · MMLU-Pro 78.5%)
Throughput elevato di 135 token/secondo con latenza al primo token (TTFT) di soli 270 ms
Pesi aperti (14B) 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

Qwen2.5-VL 72B · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 38% · GPQA Diamond 58%)
Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 720 ms
Pesi aperti (72B) 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	Qwen2.5-VL 72B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	12K	20K
Reasoning Tokens / Task Thinking tokens burned per task	3K	6K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Phi-4	Qwen2.5-VL 72B
Official API Model ID (SDK)	phi-4	qwen2-5-vl-72b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-09
Knowledge Cutoff	June 2024	June 2024
Architecture	Synthetic-Data Dense Reasoning Transformer	Open-Weights Multimodal Vision-Language Transformer
Parameters	14B	72B
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)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 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, vision
Output Modalities	text	text

Phi-4 vs Qwen2.5-VL 72B: Risposte Rapide (FAQ)

Which is more intelligent, Phi-4 or Qwen2.5-VL 72B?

Phi-4 is more intelligent. It scores 80 compared with 79 on the composite Intelligence Index.

Which model is faster?

Phi-4 is faster, generating 135 tokens per second compared with 55 tokens per second.

Which model has lower latency?

Phi-4 has lower latency, with a time to first token of 0.27s compared with 0.72s.

Which model has a larger context window?

Qwen2.5-VL 72B has a larger context window, supporting 131.1K tokens compared with 16.4K tokens.

Verdetto

Sia Phi-4 (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 Qwen2.5-VL 72B (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Phi-4 guida nei benchmark di ragionamento e programmazione, Phi-4 garantisce una generazione più rapida e Phi-4 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Phi-4 o Qwen2.5-VL 72B?+

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

Qual è la differenza di prezzo API tra Phi-4 e Qwen2.5-VL 72B?+

Phi-4 costa $0.00 / $0.00 per milione di token di input/output, mentre Qwen2.5-VL 72B costa $0.00 / $0.00. Nel costo combinato, Phi-4 risulta più economico.

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

Phi-4 supporta 16.4K token di contesto contro 131.1K token di Qwen2.5-VL 72B, mentre Phi-4 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 vs Qwen2.5-VL 72B

Cambia e Confronta Qualsiasi Modello

Phi-4

Qwen2.5-VL 72B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Phi-4 · Strengths & Key Advantages

Considerations & Trade-offs

Qwen2.5-VL 72B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Phi-4 vs Qwen2.5-VL 72B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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