Head-to-Head Model Comparison

QwQ 32B Preview vs Qwen2.5-VL 72B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

QwQ 32B PreviewProfile

Qwen2.5-VL 72BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

QwQ 32B Preview

Penalaran Bobot Terbuka

QwQ 32B Preview dari Alibaba adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 33K, kecepatan 52 token/detik, indeks kecerdasan 47/100, sert

Intelligence Index47 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

54.2%

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

Qwen2.5-VL 72B is 1.0x cheaper for API usage (input + output tokens combined).
Qwen2.5-VL 72B has a larger context window (131K vs 33K tokens).
Qwen2.5-VL 72B is faster at 55 tok/s vs 52 tok/s.
QwQ 32B Preview leads on SWE-bench coding (54.2% vs 38.0%).

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

Feature	QwQ 32B Preview	Qwen2.5-VL 72B
Intelligence Index Composite reasoning & knowledge score (0-100)	47	79
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.20 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	32.8K tokens~49 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	52 tok/s	55 tok/s
SWE-bench Verified Real-world software engineering	54.2%	38%
GPQA Diamond Graduate-level reasoning	65.2%	58%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

QwQ 32B Preview

+16.2% SWE-bench lead

Cost

Qwen2.5-VL 72B

1.0x cheaper monthly runtime

Speed

Qwen2.5-VL 72B

+6% faster generation

Context

Qwen2.5-VL 72B

131.1K tokens window

Skor Benchmark Terverifikasi

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

QwQ 32B PreviewQwen2.5-VL 72B

SWE-bench Verified (Autonomous Coding)

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

54.2%vs38%

GPQA Diamond (Doctorate-Level Science & Math)

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

65.2%vs58%

MMLU-Pro (Multi-task Language Understanding)

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

74.8%vs77.5%

LMSYS Chatbot Arena Elo

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

1312ptsvs1315pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

9.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

29.5%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

36.2%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.4%vsN/A

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

79.7%vsN/A

Long-Context Reasoning

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

60.1%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

52 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

QwQ 32B Preview0.82s

Qwen2.5-VL 72B0.72s

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

QwQ 32B Preview10.44s

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)

QwQ 32B Preview Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

Qwen2.5-VL 72B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen2.5-VL 72B saves approximately $0.92 / month (100%) under this workload.1.0x Savings

QwQ 32B Preview · Strengths & Key Advantages

Jendela konteks 33K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
Kecepatan generasi tinggi 52 token/detik dengan latensi token pertama (TTFT) hanya 820 ms
Bobot terbuka (32B) 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	QwQ 32B Preview	Qwen2.5-VL 72B
Cost per Task Estimated USD to finish one task	$0.45	$0.00
Output Tokens / Task Tokens generated per task	22K	20K
Reasoning Tokens / Task Thinking tokens burned per task	18K	6K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	QwQ 32B Preview	Qwen2.5-VL 72B
Official API Model ID (SDK)	qwq-32b-instruct	qwen2-5-vl-72b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-11	2024-09
Knowledge Cutoff	September 2024	June 2024
Architecture	Reinforcement Learning Reasoning Transformer	Open-Weights Multimodal Vision-Language Transformer
Parameters	32B	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)	Flat rate ($0.2/1M in) across full 33K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	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	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) · 20 GB VRAM (4-bit Quantized) · 69 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

QwQ 32B Preview vs Qwen2.5-VL 72B: Jawaban Cepat (FAQ)

Which is more intelligent, QwQ 32B Preview or Qwen2.5-VL 72B?

Qwen2.5-VL 72B is more intelligent. It scores 79 compared with 47 on the composite Intelligence Index.

Which model is faster?

Qwen2.5-VL 72B is faster, generating 55 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen2.5-VL 72B is cheaper at $0.00 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen2.5-VL 72B has lower latency, with a time to first token of 0.72s compared with 0.82s.

Which model has a larger context window?

Qwen2.5-VL 72B has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.

Kesimpulan

Baik QwQ 32B Preview (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, 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: Qwen2.5-VL 72B unggul dalam penalaran dan pemrograman, Qwen2.5-VL 72B memberikan kecepatan generasi token lebih tinggi, dan Qwen2.5-VL 72B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, QwQ 32B Preview atau Qwen2.5-VL 72B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen2.5-VL 72B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API QwQ 32B Preview vs Qwen2.5-VL 72B?+

QwQ 32B Preview mengenakan biaya $0.20 / $0.60 per 1M token input/output, sedangkan Qwen2.5-VL 72B sebesar $0.00 / $0.00. Secara keseluruhan, Qwen2.5-VL 72B lebih hemat biaya.

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

QwQ 32B Preview mendukung konteks 32.8K token dibandingkan 131.1K token pada Qwen2.5-VL 72B, sementara Qwen2.5-VL 72B 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)

QwQ 32B Preview vs Qwen2.5-VL 72B

Ganti & Bandingkan Model Apa Saja

QwQ 32B Preview

Qwen2.5-VL 72B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

QwQ 32B Preview · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

QwQ 32B Preview vs Qwen2.5-VL 72B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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