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Head-to-Head Model Comparison

Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct

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
Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed Comparison

Qwen 2.5 32B Instruct von Alibaba und Qwen 2.5 7B Instruct von Alibaba gehören zu den führenden KI-Modellen. Qwen 2.5 32B Instruct bietet ein Kontextfenster von 131.1K Tokens bei API-Preisen von $0.18/$0.36 pro 1M Tokens, während Qwen 2.5 7B Instruct 131.1K Tokens Kontext zu $0.06/$0.12 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Popular Matchups:
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%

Alibaba

Qwen 2.5 7B Instruct

Kompaktes Effizienz-Modell

Qwen 2.5 7B Instruct von Alibaba ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 131K Kontextfenster, 130 Token/s Durchsatz, einem Intelligenz-Index von 35/100 und $0.06/$0.12 pro 1

Intelligence Index35 / 100
Input Token:

$0.06 / 1M

Output Token:

$0.12 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

28.5%

Wichtigste Unterschiede auf einen Blick

  • Qwen 2.5 7B Instruct is 3.0x cheaper for API usage (input + output tokens combined).
  • Qwen 2.5 7B Instruct is faster at 130 tok/s vs 85 tok/s.
  • Qwen 2.5 32B Instruct leads on SWE-bench coding (40.8% vs 28.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen 2.5 32B InstructQwen 2.5 7B Instruct
Intelligence Index
Composite reasoning & knowledge score (0-100)
4135
Provider
AlibabaAlibaba
Input Price
Cost per million input tokens
$0.18 / 1M$0.06 / 1M
Output Price
Cost per million output tokens
$0.36 / 1M$0.12 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.10 / 1M$0.03 / 1M
Context Window
Maximum input + output tokens
131.1K tokens~197 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
85 tok/s130 tok/s
SWE-bench Verified
Real-world software engineering
40.8%28.5%
GPQA Diamond
Graduate-level reasoning
48.5%36.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.04 / 1M$0.01 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Qwen 2.5 32B Instruct
+12.3% SWE-bench lead
Cost
Qwen 2.5 7B Instruct
3.0x cheaper monthly runtime
Speed
Qwen 2.5 7B Instruct
+53% faster generation
Context
Tied
131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Qwen 2.5 32B InstructQwen 2.5 7B Instruct
SWE-bench Verified (Autonomous Coding)

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

40.8%vs28.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

48.5%vs36.8%
MMLU-Pro (Multi-task Language Understanding)

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

67.2%vs56.4%
LMSYS Chatbot Arena Elo

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

1255ptsvs1205pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

5.6%vs3.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

26.2%vs18.4%
SciCode (Scientific Computing)

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

26.5%vs18.2%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

42.1%vs29.8%
AIME (Advanced Math Competition)

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

52.3%vs38.6%
Long-Context Reasoning

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

64%vs58.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs130 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 32B Instruct0.48s
Qwen 2.5 7B Instruct0.29s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen 2.5 32B Instruct6.36s
Qwen 2.5 7B Instruct4.14s

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)
Qwen 2.5 32B Instruct Monthly API Bill
$0.65 / month
$0.18/1M in · $0.36/1M out
Qwen 2.5 7B Instruct Monthly API Bill
$0.22 / month
$0.06/1M in · $0.12/1M out
Qwen 2.5 7B Instruct saves approximately $0.43 / month (67%) under this workload.3.0x Savings

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

Qwen 2.5 7B Instruct · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 28.5% · GPQA Diamond 36.8%)
  • Hohe Generierungsgeschwindigkeit von 130 Token/Sekunde bei nur 290 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (7B) 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.

MetricQwen 2.5 32B InstructQwen 2.5 7B Instruct
Cost per Task
Estimated USD to finish one task
$0.16$0.05
Output Tokens / Task
Tokens generated per task
28K20K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureQwen 2.5 32B InstructQwen 2.5 7B Instruct
Official API Model ID (SDK)qwen-2-5-32b-instructqwen-2-5-7b-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-092024-09
Knowledge CutoffJune 2024June 2024
ArchitectureDense Autoregressive TransformerDense Autoregressive Transformer
Parameters32B7B
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 ($0.18/1M in) across full 131K windowFlat rate ($0.06/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.036/1M tokensCached input at $0.012/1M 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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 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 32B Instruct vs Qwen 2.5 7B Instruct: Schnelle Antworten (FAQ)

Which is more intelligent, Qwen 2.5 32B Instruct or Qwen 2.5 7B Instruct?

Qwen 2.5 32B Instruct is more intelligent. It scores 41 compared with 35 on the composite Intelligence Index.

Which model is faster?

Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s compared with 0.48s.

Fazit

Sowohl Qwen 2.5 32B Instruct (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen 2.5 7B Instruct (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen 2.5 32B Instruct führt bei komplexem Reasoning und Coding, Qwen 2.5 7B Instruct liefert den höheren Token-Durchsatz und Qwen 2.5 7B Instruct ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen 2.5 32B Instruct oder Qwen 2.5 7B Instruct?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen 2.5 32B Instruct vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Qwen 2.5 32B Instruct und Qwen 2.5 7B Instruct?+

Qwen 2.5 32B Instruct kostet $0.18 / $0.36 pro 1M Input-/Output-Tokens, Qwen 2.5 7B Instruct liegt bei $0.06 / $0.12. Beim gewichteten Gesamtpreis ist Qwen 2.5 7B Instruct günstiger.

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

Qwen 2.5 32B Instruct bietet 131.1K Tokens Kontext gegenüber 131.1K Tokens bei Qwen 2.5 7B Instruct. Bei der Generierungsgeschwindigkeit liegt Qwen 2.5 7B 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 & 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).