Qwen-VL Max vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen-VL Max by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
Cambia e Confronta Qualsiasi Modello
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Qwen-VL Max
IA Multimodale NativaQwen-VL Max di Alibaba è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 131K, velocità di 75 token/s, indice di intelligenza di 76/100 e t
$0.70 / 1M
$2.80 / 1M
131.1K tokens
N/A
GPT-6 Astra
Modello Vocale e Audio NativoGPT-6 Astra di OpenAI è un modello di IA progettato per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, con finestra di contesto da 272K, velocità di 100 token/s, indice di intelligenza di 96/100 e tariffa
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Differenze Chiave a Colpo d’Occhio
- Qwen-VL Max is 16.7x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 131K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 75 tok/s.
| Feature | Qwen-VL Max | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.70 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $2.80 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.47 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 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 | 75 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | 46.5% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $1.00 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen-VL Max · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (GPQA Diamond 46.5% · MMLU-Pro 76%)
- Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 650 ms
- Economia API competitiva ($0.7/$2.8 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
GPT-6 Astra · Strengths & Key Advantages
- Finestra di contesto da 272K con capacità massima di output fino a 128K token (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Throughput elevato di 100 token/secondo con latenza al primo token (TTFT) di soli 950 ms
- Economia API competitiva ($10/$50 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen-VL Max | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $2.15 |
Output Tokens / Task Tokens generated per task | 16K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 5K | 32K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen-VL Max | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen-vl-max-instruct | gpt-6-astra |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-04 | 2026-09 |
| Knowledge Cutoff | December 2023 | June 2026 |
| Architecture | Multimodal Vision-Language 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 ($0.7/1M in) across full 131K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.07/1M 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) | 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 | text, vision, audio |
| Output Modalities | text | text, audio |
Qwen-VL Max vs GPT-6 Astra: Risposte Rapide (FAQ)
Which is more intelligent, Qwen-VL Max or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 76 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen-VL Max is cheaper at $0.47 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen-VL Max has lower latency, with a time to first token of 0.65s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 131.1K tokens.
Verdetto
Choose Qwen-VL Max for ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi rag aziendali e integrazioni api scalabili., or choose GPT-6 Astra for ideale per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
Which is cheaper: Qwen-VL Max or GPT-6 Astra?+
Qwen-VL Max costs $0.7/1M input and $2.8/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Qwen-VL Max or GPT-6 Astra?+
Qwen-VL Max supports 131K 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.
Related reading
- Aion-3.0 vs Aion-Mini: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aion-3.0 vs Gemini 3.6 Flash: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aion-Mini vs Laguna XS 2.1: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aya Expanse 32B vs Llama 3.2 90B Vision: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aya Expanse 8B vs Command R+: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
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.
Fonti ufficiali: Alibaba & OpenAI Official Documentation