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Qwen3 235B-A22B vs QwQ 32B Preview

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
Qwen3 235B-A22B vs QwQ 32B Preview: Benchmarks, Pricing & Speed Comparison

Qwen3 235B-A22B von Alibaba und QwQ 32B Preview von Alibaba gehören zu den führenden KI-Modellen. Qwen3 235B-A22B bietet ein Kontextfenster von 131.1K Tokens bei API-Preisen von $0.00/$0.00 pro 1M Tokens, während QwQ 32B Preview 32.8K Tokens Kontext zu $0.20/$0.60 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Alibaba

Qwen3 235B-A22B

Open-Weights-Basismodell

Qwen3 235B-A22B von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 68 Token/s Durchsatz, einem Intelligenz-Index von 83/100 und kostenlosem Open-Weights-

Intelligence Index83 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

48.5%

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%

Wichtigste Unterschiede auf einen Blick

  • Qwen3 235B-A22B is 1.0x cheaper for API usage (input + output tokens combined).
  • Qwen3 235B-A22B has a larger context window (131K vs 33K tokens).
  • Qwen3 235B-A22B is faster at 68 tok/s vs 52 tok/s.
  • QwQ 32B Preview leads on SWE-bench coding (54.2% vs 48.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen3 235B-A22BQwQ 32B Preview
Intelligence Index
Composite reasoning & knowledge score (0-100)
8347
Provider
AlibabaAlibaba
Input Price
Cost per million input tokens
$0.00 / 1M$0.20 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.60 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.14 / 1M
Context Window
Maximum input + output tokens
131.1K tokens~197 A4 pages of text32.8K tokens~49 A4 pages of text
Max Output Tokens
Per single request
16K8K
Throughput Speed
Tokens generated per second
68 tok/s52 tok/s
SWE-bench Verified
Real-world software engineering
48.5%54.2%
GPQA Diamond
Graduate-level reasoning
67%65.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.05 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
QwQ 32B Preview
+5.7% SWE-bench lead
Cost
Qwen3 235B-A22B
1.0x cheaper monthly runtime
Speed
Qwen3 235B-A22B
+31% faster generation
Context
Qwen3 235B-A22B
131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Qwen3 235B-A22BQwQ 32B Preview
SWE-bench Verified (Autonomous Coding)

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

48.5%vs54.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

67%vs65.2%
MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs74.8%
LMSYS Chatbot Arena Elo

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

1365ptsvs1312pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs9.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs29.5%
SciCode (Scientific Computing)

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

N/Avs36.2%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs58.4%
AIME (Advanced Math Competition)

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

N/Avs79.7%
Long-Context Reasoning

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

N/Avs60.1%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs52 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
Qwen3 235B-A22B0.62s
QwQ 32B Preview0.82s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen3 235B-A22B7.97s
QwQ 32B Preview10.44s

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)
Qwen3 235B-A22B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
QwQ 32B Preview Monthly API Bill
$0.92 / month
$0.2/1M in · $0.6/1M out
Qwen3 235B-A22B saves approximately $0.92 / month (100%) under this workload.1.0x Savings

Qwen3 235B-A22B · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 48.5% · GPQA Diamond 67%)
  • Hohe Generierungsgeschwindigkeit von 68 Token/Sekunde bei nur 620 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (235B total (22B MoE)) 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

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

Token Use & Task Economics

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

MetricQwen3 235B-A22BQwQ 32B Preview
Cost per Task
Estimated USD to finish one task
$0.00$0.45
Output Tokens / Task
Tokens generated per task
22K22K
Reasoning Tokens / Task
Thinking tokens burned per task
8K18K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureQwen3 235B-A22BQwQ 32B Preview
Official API Model ID (SDK)qwen3-235b-a22b-instructqwq-32b-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-042024-11
Knowledge CutoffFebruary 2025September 2024
ArchitectureSparse Mixture-of-Experts Autoregressive TransformerReinforcement Learning Reasoning Transformer
Parameters235B total (22B active MoE)32B
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)Free across full context window (only KV-cache VRAM scales)Flat rate ($0.2/1M in) across full 33K window
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Cached input at $0.05/1M 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
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) · 146 GB VRAM (4-bit Quantized) · 505 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

Qwen3 235B-A22B vs QwQ 32B Preview: Schnelle Antworten (FAQ)

Which is more intelligent, Qwen3 235B-A22B or QwQ 32B Preview?

Qwen3 235B-A22B is more intelligent. It scores 83 compared with 47 on the composite Intelligence Index.

Which model is faster?

Qwen3 235B-A22B is faster, generating 68 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen3 235B-A22B 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?

Qwen3 235B-A22B has lower latency, with a time to first token of 0.62s compared with 0.82s.

Which model has a larger context window?

Qwen3 235B-A22B has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.

Fazit

Sowohl Qwen3 235B-A22B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch QwQ 32B Preview (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3 235B-A22B führt bei komplexem Reasoning und Coding, Qwen3 235B-A22B liefert den höheren Token-Durchsatz und Qwen3 235B-A22B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen3 235B-A22B oder QwQ 32B Preview?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3 235B-A22B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Qwen3 235B-A22B und QwQ 32B Preview?+

Qwen3 235B-A22B kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, QwQ 32B Preview liegt bei $0.20 / $0.60. Beim gewichteten Gesamtpreis ist Qwen3 235B-A22B günstiger.

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

Qwen3 235B-A22B bietet 131.1K Tokens Kontext gegenüber 32.8K Tokens bei QwQ 32B Preview. Bei der Generierungsgeschwindigkeit liegt Qwen3 235B-A22B 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).