Qwen-Omni Turbo vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen-Omni Turbo by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
Qwen-Omni Turbo
Omnimodalen temps réel end-à-end voix et vision conversationnel AI supporting duplex audio diffusion continue et instant visuel ancrage.
$0.30 / 1M
$1.20 / 1M
32.8K tokens
N/A
GPT-6 Astra
Agent d'OS autonomed'OpenAI late-2026 phare modèle de fondation intégrant natif utilisation de l'ordinateur agents, dynamic UI orchestration, et profond STEM raisonnement.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Différences Clés en un Coup d’Œil
- Qwen-Omni Turbo is 39.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 33K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 90 tok/s.
| Caractéristique | Qwen-Omni Turbo | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.30 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.20 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 90 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $1.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen-Omni Turbo · Strengths & Key Advantages
- natif complet-duplex parole-à-parole interaction sans separate TTS pipelines
- sous-400ms voix response latence pour natural phone-like conversations
- Concurrent visuel scene perception et vocal dialogue
Considerations & Trade-offs
- fenêtre de contexte limité à 32K tokens à maintain faible-latence audio buffering
- complexe codage et technique STEM précision est inférieur que texte-seulement flagships
GPT-6 Astra · Strengths & Key Advantages
- Flawless OS-level navigation et utilisation de l'ordinateur automatisation
- Seamless en temps réel multimodal voix et vision interactivity
- Dynamic chaîne de pensée qui self-adjusts profondeur based sur query difficulty
- immense 1M fenêtre de contexte avec instant prefix caching
Considerations & Trade-offs
- Strict sécurité filters peut occasionally throttle complexe penetration testing prompts
- Slightly supérieur raisonnement latence sur élevé-effort mathématiques queries
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen-Omni Turbo | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $2.15 |
Output Tokens / Task Tokens generated per task | 12K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 32K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Qwen-Omni Turbo | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen-omni-turbo-instruct | gpt-6-astra |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-01 | 2026-09 |
| Knowledge Cutoff | November 2024 | June 2026 |
| Architecture | End-to-End Omni-Modal Multimodal Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 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 window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-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 Classification | Alibaba Production Safety & Alignment Verified | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio | text, vision, audio |
| Output Modalities | text, audio | text, audio |
Qwen-Omni Turbo vs GPT-6 Astra: Réponses Rapides (FAQ)
Which is more intelligent, Qwen-Omni Turbo or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 74 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Qwen-Omni Turbo is cheaper at $0.20 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen-Omni Turbo has lower latency, with a time to first token of 0.38s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 32.8K tokens.
Verdict
Choose Qwen-Omni Turbo for en temps réel voix client agents, interactif tutors, et hands-free smart devices, or choose GPT-6 Astra for autonome browser/computer flux de travail, en temps réel voix agents, et élevé-difficulty mathématique raisonnement.
Questions fréquentes
Which is cheaper: Qwen-Omni Turbo or GPT-6 Astra?+
Qwen-Omni Turbo costs $0.3/1M input and $1.2/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Qwen-Omni Turbo or GPT-6 Astra?+
Qwen-Omni Turbo supports 33K tokens whereas GPT-6 Astra supports 272K tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Alibaba & OpenAI Official Documentation