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Qwen-Omni Turbo vs GPT-Realtime-2.1

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-Omni Turbo vs GPT-Realtime-2.1: Benchmarks, Pricing & Speed Comparison

Qwen-Omni Turbo von Alibaba und GPT-Realtime-2.1 von OpenAI gehören zu den führenden KI-Modellen. Qwen-Omni Turbo bietet ein Kontextfenster von 32.8K Tokens bei API-Preisen von $0.30/$1.20 pro 1M Tokens, während GPT-Realtime-2.1 128K Tokens Kontext zu $32.00/$64.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Alibaba

Qwen-Omni Turbo

Natives Sprach- & Audiomodell

Qwen-Omni Turbo von Alibaba ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 33K Kontextfenster, 90 Token/s Durchsatz, einem Intelligenz-Index von 74/100 und $0.3/$1.2 pro 1M Token.

Intelligence Index74 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

OpenAI

GPT-Realtime-2.1

Natives Sprach- & Audiomodell

GPT-Realtime-2.1 von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 128K Kontextfenster, 90 Token/s Durchsatz, einem Intelligenz-Index von 65/100 und $32/$64 pro 1M Token.

Intelligence Index65 / 100
Input Token:

$32.00 / 1M

Output Token:

$64.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

62%

Wichtigste Unterschiede auf einen Blick

  • Qwen-Omni Turbo is 70.1x cheaper for API usage (input + output tokens combined).
  • GPT-Realtime-2.1 has a larger context window (128K vs 33K tokens).
  • GPT-Realtime-2.1 is faster at 90 tok/s vs 90 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen-Omni TurboGPT-Realtime-2.1
Intelligence Index
Composite reasoning & knowledge score (0-100)
7465
Provider
AlibabaOpenAI
Input Price
Cost per million input tokens
$0.30 / 1M$32.00 / 1M
Output Price
Cost per million output tokens
$1.20 / 1M$64.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.20 / 1M$15.04 / 1M
Context Window
Maximum input + output tokens
32.8K tokens~49 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K33K
Throughput Speed
Tokens generated per second
90 tok/s90 tok/s
SWE-bench Verified
Real-world software engineering
N/A62%
GPQA Diamond
Graduate-level reasoning
N/A70%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$3.20 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Qwen-Omni Turbo
70.1x cheaper monthly runtime
Speed
GPT-Realtime-2.1
+0% faster generation
Context
GPT-Realtime-2.1
128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Qwen-Omni TurboGPT-Realtime-2.1
SWE-bench Verified (Autonomous Coding)

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

N/Avs62%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs70%
MMLU-Pro (Multi-task Language Understanding)

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

69%vs83%
LMSYS Chatbot Arena Elo

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

1235ptsvs1350pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs10.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs38%
SciCode (Scientific Computing)

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

N/Avs36.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs62%
AIME (Advanced Math Competition)

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

N/Avs86.5%
Long-Context Reasoning

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

N/Avs74%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs90 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-Omni Turbo0.38s
GPT-Realtime-2.10.32s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen-Omni Turbo5.94s
GPT-Realtime-2.15.88s

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-Omni Turbo Monthly API Bill
$1.57 / month
$0.3/1M in · $1.2/1M out
GPT-Realtime-2.1 Monthly API Bill
$110.40 / month
$32/1M in · $64/1M out
Qwen-Omni Turbo saves approximately $108.83 / month (99%) under this workload.70.1x Savings

Qwen-Omni Turbo · Strengths & Key Advantages

  • 33K Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 69% · Arena Elo 1235)
  • Hohe Generierungsgeschwindigkeit von 90 Token/Sekunde bei nur 380 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.3/$1.2 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

GPT-Realtime-2.1 · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 62% · GPQA Diamond 70%)
  • Hohe Generierungsgeschwindigkeit von 90 Token/Sekunde bei nur 320 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($32/$64 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricQwen-Omni TurboGPT-Realtime-2.1
Cost per Task
Estimated USD to finish one task
$0.06$6.66
Output Tokens / Task
Tokens generated per task
12K26K
Reasoning Tokens / Task
Thinking tokens burned per task
2K14K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureQwen-Omni TurboGPT-Realtime-2.1
Official API Model ID (SDK)qwen-omni-turbo-instructgpt-realtime-2-1
API ProtocolAlibaba REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2025-012026-07
Knowledge CutoffNovember 2024February 2026
ArchitectureEnd-to-End Omni-Modal Multimodal TransformerReal-time speech-to-speech multimodal transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.3/1M in) across full 33K windowFlat rate ($32/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.03/1M tokensAutomatic prefix caching at $3.2/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)Text BPE (~4 chars/token) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationAlibaba Production Safety & Alignment VerifiedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audioaudio, text
Output Modalitiestext, audioaudio, text

Qwen-Omni Turbo vs GPT-Realtime-2.1: Schnelle Antworten (FAQ)

Which is more intelligent, Qwen-Omni Turbo or GPT-Realtime-2.1?

Qwen-Omni Turbo is more intelligent. It scores 74 compared with 65 on the composite Intelligence Index.

Which model is cheaper to run?

Qwen-Omni Turbo is cheaper at $0.20 per 1M tokens blended, compared with $15.04 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-Realtime-2.1 has lower latency, with a time to first token of 0.32s compared with 0.38s.

Which model has a larger context window?

GPT-Realtime-2.1 has a larger context window, supporting 128K tokens compared with 32.8K tokens.

Fazit

Sowohl Qwen-Omni Turbo (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-Realtime-2.1 (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen-Omni Turbo führt bei komplexem Reasoning und Coding, Qwen-Omni Turbo liefert den höheren Token-Durchsatz und Qwen-Omni Turbo ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen-Omni Turbo oder GPT-Realtime-2.1?+

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

Wie unterscheiden sich die API-Preise von Qwen-Omni Turbo und GPT-Realtime-2.1?+

Qwen-Omni Turbo kostet $0.30 / $1.20 pro 1M Input-/Output-Tokens, GPT-Realtime-2.1 liegt bei $32.00 / $64.00. Beim gewichteten Gesamtpreis ist Qwen-Omni Turbo günstiger.

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

Qwen-Omni Turbo bietet 32.8K Tokens Kontext gegenüber 128K Tokens bei GPT-Realtime-2.1. Bei der Generierungsgeschwindigkeit liegt Qwen-Omni Turbo 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 OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).