Head-to-Head Model Comparison

Qwen 2.5 72B vs Qwen 2.5 32B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Qwen 2.5 72BProfile

Qwen 2.5 32B InstructProfile

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5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen 2.5 72B

Open-Weights-Basismodell

Qwen 2.5 72B von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 64 Token/s Durchsatz, einem Intelligenz-Index von 44/100 und $0.35/$0.7 pro 1M Token.

Intelligence Index44 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.70 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45.2%

Alibaba

Qwen 2.5 32B Instruct

Open-Weights-Basismodell

Qwen 2.5 32B Instruct von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 41/100 und $0.18/$0.36 pro 1M

Intelligence Index41 / 100

Input Token:

$0.18 / 1M

Output Token:

$0.36 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.8%

Wichtigste Unterschiede auf einen Blick

Qwen 2.5 32B Instruct is 1.9x cheaper for API usage (input + output tokens combined).
Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
Qwen 2.5 32B Instruct is faster at 85 tok/s vs 64 tok/s.
Qwen 2.5 72B leads on SWE-bench coding (45.2% vs 40.8%).

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

Feature	Qwen 2.5 72B	Qwen 2.5 32B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	44	41
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.35 / 1M	$0.18 / 1M
Output Price Cost per million output tokens	$0.70 / 1M	$0.36 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.19 / 1M	$0.10 / 1M
Context Window Maximum input + output tokens	128K tokens~192 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	64 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	45.2%	40.8%
GPQA Diamond Graduate-level reasoning	52.8%	48.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.07 / 1M	$0.04 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Qwen 2.5 72B

+4.4% SWE-bench lead

Cost

Qwen 2.5 32B Instruct

1.9x cheaper monthly runtime

Speed

Qwen 2.5 32B Instruct

+33% faster generation

Context

Qwen 2.5 32B Instruct

131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Qwen 2.5 72BQwen 2.5 32B Instruct

SWE-bench Verified (Autonomous Coding)

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

45.2%vs40.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

52.8%vs48.5%

MMLU-Pro (Multi-task Language Understanding)

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

71.4%vs67.2%

LMSYS Chatbot Arena Elo

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

1282ptsvs1255pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

7.2%vs5.6%

Terminal-Bench (Agentic Coding & Terminal Use)

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

28.1%vs26.2%

SciCode (Scientific Computing)

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

28.9%vs26.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

46.5%vs42.1%

AIME (Advanced Math Competition)

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

59.2%vs52.3%

Long-Context Reasoning

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

66.8%vs64%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

64 tok/s vs85 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

Qwen 2.5 72B0.61s

Qwen 2.5 32B Instruct0.48s

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

Qwen 2.5 72B8.42s

Qwen 2.5 32B Instruct6.36s

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)

Qwen 2.5 72B Monthly API Bill

$1.26 / month

$0.35/1M in · $0.7/1M out

Qwen 2.5 32B Instruct Monthly API Bill

$0.65 / month

$0.18/1M in · $0.36/1M out

Qwen 2.5 32B Instruct saves approximately $0.61 / month (49%) under this workload.1.9x Savings

Qwen 2.5 72B · Strengths & Key Advantages

128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 45.2% · GPQA Diamond 52.8%)
Hohe Generierungsgeschwindigkeit von 64 Token/Sekunde bei nur 610 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

Qwen 2.5 32B Instruct · Strengths & Key Advantages

131K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 480 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Qwen 2.5 72B	Qwen 2.5 32B Instruct
Cost per Task Estimated USD to finish one task	$0.28	$0.16
Output Tokens / Task Tokens generated per task	32K	28K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen 2.5 72B	Qwen 2.5 32B Instruct
Official API Model ID (SDK)	qwen-2-5-72b-instruct	qwen-2-5-32b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-09	2024-09
Knowledge Cutoff	September 2024	June 2024
Architecture	Dense Autoregressive Transformer	Dense Autoregressive Transformer
Parameters	72B	32B
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 ($0.35/1M in) across full 128K window	Flat rate ($0.18/1M in) across full 131K window
Prompt Cache Policy & TTL	Cached input at $0.07/1M tokens	Cached input at $0.036/1M 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)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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
Output Modalities	text	text

Qwen 2.5 72B vs Qwen 2.5 32B Instruct: Schnelle Antworten (FAQ)

Which is more intelligent, Qwen 2.5 72B or Qwen 2.5 32B Instruct?

Qwen 2.5 72B is more intelligent. It scores 44 compared with 41 on the composite Intelligence Index.

Which model is faster?

Qwen 2.5 32B Instruct is faster, generating 85 tokens per second compared with 64 tokens per second.

Which model is cheaper to run?

Qwen 2.5 32B Instruct is cheaper at $0.10 per 1M tokens blended, compared with $0.19 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen 2.5 32B Instruct has lower latency, with a time to first token of 0.48s compared with 0.61s.

Which model has a larger context window?

Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Fazit

Sowohl Qwen 2.5 72B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen 2.5 32B Instruct (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen 2.5 72B führt bei komplexem Reasoning und Coding, Qwen 2.5 32B Instruct liefert den höheren Token-Durchsatz und Qwen 2.5 32B Instruct ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen 2.5 72B oder Qwen 2.5 32B Instruct?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen 2.5 72B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Qwen 2.5 72B und Qwen 2.5 32B Instruct?+

Qwen 2.5 72B kostet $0.35 / $0.70 pro 1M Input-/Output-Tokens, Qwen 2.5 32B Instruct liegt bei $0.18 / $0.36. Beim gewichteten Gesamtpreis ist Qwen 2.5 32B Instruct günstiger.

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

Qwen 2.5 72B bietet 128K Tokens Kontext gegenüber 131.1K Tokens bei Qwen 2.5 32B Instruct. Bei der Generierungsgeschwindigkeit liegt Qwen 2.5 32B Instruct 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)

Qwen 2.5 72B vs Qwen 2.5 32B Instruct

Beliebige Modelle wechseln & vergleichen

Qwen 2.5 72B

Qwen 2.5 32B Instruct

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Qwen 2.5 72B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen 2.5 32B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Qwen 2.5 72B vs Qwen 2.5 32B Instruct: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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