Head-to-Head Model Comparison

Phi-4 vs Qwen2.5-VL 72B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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Phi-4Profile

Qwen2.5-VL 72BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Microsoft

Phi-4

Model Dasar Bobot Terbuka

Phi-4 dari Microsoft adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 16K, kecepatan 135 token/detik, indeks kecerdasan 80/100, serta akses gratis open

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

AI Multimodal Asli

Qwen2.5-VL 72B dari Alibaba adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 131K, kecepatan 55 token/detik, indeks kecerdasan 79/100, serta akses g

Intelligence Index79 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

38%

Perbedaan Utama Sekilas

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%

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 16K dengan kapasitas output maksimum hingga 4K token (GPQA Diamond 55% · MMLU-Pro 78.5%)
Kecepatan generasi tinggi 135 token/detik dengan latensi token pertama (TTFT) hanya 270 ms
Bobot terbuka (14B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 38% · GPQA Diamond 58%)
Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 720 ms
Bobot terbuka (72B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Phi-4 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen2.5-VL 72B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Phi-4 unggul dalam penalaran dan pemrograman, Phi-4 memberikan kecepatan generasi token lebih tinggi, dan Phi-4 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Phi-4 atau Qwen2.5-VL 72B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Phi-4 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Phi-4 vs Qwen2.5-VL 72B?+

Phi-4 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen2.5-VL 72B sebesar $0.00 / $0.00. Secara keseluruhan, Phi-4 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Phi-4 mendukung konteks 16.4K token dibandingkan 131.1K token pada Qwen2.5-VL 72B, sementara Phi-4 unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Phi-4

Qwen2.5-VL 72B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Phi-4 · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Phi-4 vs Qwen2.5-VL 72B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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