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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
QwQ 32B Preview vs Qwen2.5-VL 72B: Benchmarks, Pricing & Speed Comparison

QwQ 32B Preview von Alibaba und Qwen2.5-VL 72B von Alibaba gehören zu den führenden KI-Modellen. QwQ 32B Preview bietet ein Kontextfenster von 32.8K Tokens bei API-Preisen von $0.20/$0.60 pro 1M Tokens, während Qwen2.5-VL 72B 131.1K Tokens Kontext zu $0.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Alibaba

QwQ 32B Preview

Open-Weights-Reasoning-Modell

QwQ 32B Preview von Alibaba ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 33K Kontextfenster, 52 Token/s Durchsatz, einem Intelligenz-Index von 47/100 und $0.2/$0.6 pro 1M

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

Nativ Multimodale KI

Qwen2.5-VL 72B von Alibaba ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 131K Kontextfenster, 55 Token/s Durchsatz, einem Intelligenz-Index von 79/100 und kostenlosem Open-Weight

Intelligence Index79 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

38%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureQwQ 32B PreviewQwen2.5-VL 72B
Intelligence Index
Composite reasoning & knowledge score (0-100)
4779
Provider
AlibabaAlibaba
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 text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
52 tok/s55 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

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
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

Interaktiver Token-Kosten-Simulator

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • 33K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
  • Hohe Generierungsgeschwindigkeit von 52 Token/Sekunde bei nur 820 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (32B) 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

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

  • 131K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 38% · GPQA Diamond 58%)
  • Hohe Generierungsgeschwindigkeit von 55 Token/Sekunde bei nur 720 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (72B) 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.

MetricQwQ 32B PreviewQwen2.5-VL 72B
Cost per Task
Estimated USD to finish one task
$0.45$0.00
Output Tokens / Task
Tokens generated per task
22K20K
Reasoning Tokens / Task
Thinking tokens burned per task
18K6K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureQwQ 32B PreviewQwen2.5-VL 72B
Official API Model ID (SDK)qwq-32b-instructqwen2-5-vl-72b-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-112024-09
Knowledge CutoffSeptember 2024June 2024
ArchitectureReinforcement Learning Reasoning TransformerOpen-Weights Multimodal Vision-Language Transformer
Parameters32B72B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLCached input at $0.05/1M tokensAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function callingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext, vision
Output Modalitiestexttext

QwQ 32B Preview vs Qwen2.5-VL 72B: Schnelle Antworten (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.

Fazit

Sowohl QwQ 32B Preview (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen2.5-VL 72B (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen2.5-VL 72B führt bei komplexem Reasoning und Coding, Qwen2.5-VL 72B liefert den höheren Token-Durchsatz und Qwen2.5-VL 72B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – QwQ 32B Preview oder Qwen2.5-VL 72B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen2.5-VL 72B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von QwQ 32B Preview und Qwen2.5-VL 72B?+

QwQ 32B Preview kostet $0.20 / $0.60 pro 1M Input-/Output-Tokens, Qwen2.5-VL 72B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Qwen2.5-VL 72B günstiger.

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

QwQ 32B Preview bietet 32.8K Tokens Kontext gegenüber 131.1K Tokens bei Qwen2.5-VL 72B. Bei der Generierungsgeschwindigkeit liegt Qwen2.5-VL 72B 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Offizielle Quellen: Offizielle API-Dokumentation von Alibaba und Alibaba, Systemkarten und unabhängige Benchmark-Auswertungen (2026).