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Head-to-Head Model Comparison

Qwen 2.5 72B vs Qwen 2.5 32B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Qwen 2.5 72B vs Qwen 2.5 32B Instruct: Benchmarks, Pricing & Speed Comparison

Alibaba tarafından geliştirilen Qwen 2.5 72B ve Alibaba tarafından sunulan Qwen 2.5 32B Instruct, kendi sınıflarının öne çıkan yapay zeka modelleridir. Qwen 2.5 72B, 128K token bağlam penceresi ve 1M token başına $0.35/$0.70 fiyat sunarken; Qwen 2.5 32B Instruct, 131.1K token bağlam penceresi ve $0.18/$0.36 fiyatla gelir. Aşağıda tüm benchmark, hız ve maliyet detaylarını inceleyebilirsiniz.

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Alibaba

Qwen 2.5 72B

Açık Ağırlıklı Temel Model

Alibaba tarafından geliştirilen Qwen 2.5 72B; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 128K bağlam penceresi, 64 token/sn hız, 44/100 zeka indeksi ve 1M token başına $0.35

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

Açık Ağırlıklı Temel Model

Alibaba tarafından geliştirilen Qwen 2.5 32B Instruct; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 131K bağlam penceresi, 85 token/sn hız, 41/100 zeka indeksi ve 1M token baş

Intelligence Index41 / 100
Input Token:

$0.18 / 1M

Output Token:

$0.36 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.8%

Bir Bakışta Temel Farklar

  • 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 →
FeatureQwen 2.5 72BQwen 2.5 32B Instruct
Intelligence Index
Composite reasoning & knowledge score (0-100)
4441
Provider
AlibabaAlibaba
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 text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
64 tok/s85 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

Hangi Alanda Kim Kazanıyor?

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

Doğrulanmış Benchmark Puanları

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

Gecikme Süresi ve Yanıt Hızı

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

Gerçek Zamanlı Token Maliyet Simülatörü

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)
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 bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 45.2% · GPQA Diamond 52.8%)
  • Sadece 610 ms ilk token gecikmesi (TTFT) ile saniyede 64 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (72B)
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Qwen 2.5 32B Instruct · Strengths & Key Advantages

  • 131K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
  • Sadece 480 ms ilk token gecikmesi (TTFT) ile saniyede 85 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (32B)
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

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

MetricQwen 2.5 72BQwen 2.5 32B Instruct
Cost per Task
Estimated USD to finish one task
$0.28$0.16
Output Tokens / Task
Tokens generated per task
32K28K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

FeatureQwen 2.5 72BQwen 2.5 32B Instruct
Official API Model ID (SDK)qwen-2-5-72b-instructqwen-2-5-32b-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-092024-09
Knowledge CutoffSeptember 2024June 2024
ArchitectureDense Autoregressive TransformerDense Autoregressive Transformer
Parameters72B32B
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 ($0.35/1M in) across full 128K windowFlat rate ($0.18/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.07/1M tokensCached input at $0.036/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative 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)
Quantization & Min GPU VRAMBF16 / 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 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
Output Modalitiestexttext

Qwen 2.5 72B vs Qwen 2.5 32B Instruct: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Qwen 2.5 72B (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Qwen 2.5 32B Instruct (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Qwen 2.5 72B karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Qwen 2.5 32B Instruct daha yüksek üretim hızı sağlar; Qwen 2.5 32B Instruct ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Qwen 2.5 72B mi yoksa Qwen 2.5 32B Instruct mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Qwen 2.5 72B önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Qwen 2.5 72B ve Qwen 2.5 32B Instruct API fiyatları nasıl karşılaştırılır?+

Qwen 2.5 72B 1M giriş/çıkış tokeni başına $0.35 / $0.70 ücretlendirilirken, Qwen 2.5 32B Instruct $0.18 / $0.36 seviyesindedir. Karma maliyette Qwen 2.5 32B Instruct daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Qwen 2.5 72B 128K token bağlam penceresi sunarken Qwen 2.5 32B Instruct 131.1K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Qwen 2.5 32B Instruct öndedir.

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.

Resmi kaynaklar: Alibaba ve Alibaba resmi API dokümantasyonu, sistem kartları ve bağımsız benchmark testleri (2026).