GPT-6 Astra vs Qwen3.7 Max
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-6 Astra by OpenAI against Qwen3.7 Max by Alibaba across real-world coding, reasoning, token economics, and latency.
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GPT-6 Astra
Natives Sprach- & AudiomodellGPT-6 Astra von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 272K Kontextfenster, 100 Token/s Durchsatz, einem Intelligenz-Index von 96/100 und $10/$50 pro 1M Token.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Qwen3.7 Max
Autonomes Agenten-ModellQwen3.7 Max von Alibaba ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 1M Kontextfenster, 80 Token/s Durchsatz, einem Intelligenz-Index von 92/100 und $1.8/$7.2 pro 1M Token.
$1.80 / 1M
$7.20 / 1M
1M tokens
73.5%
Wichtigste Unterschiede auf einen Blick
- Qwen3.7 Max is 6.5x cheaper for API usage (input + output tokens combined).
- Qwen3.7 Max has a larger context window (1M vs 272K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 80 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 73.5%).
| Feature | GPT-6 Astra | Qwen3.7 Max |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 96 | 92 |
Provider | OpenAI | Alibaba |
Input Price Cost per million input tokens | $10.00 / 1M | $1.80 / 1M |
Output Price Cost per million output tokens | $50.00 / 1M | $7.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $7.70 / 1M | $1.21 / 1M |
Context Window Maximum input + output tokens | 272K tokens~408 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 32K |
Throughput Speed Tokens generated per second | 100 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | 82.4% | 73.5% |
GPQA Diamond Graduate-level reasoning | 80.9% | 78.5% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.18 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6 Astra · Strengths & Key Advantages
- 272K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Hohe Generierungsgeschwindigkeit von 100 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($10/$50 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
Qwen3.7 Max · Strengths & Key Advantages
- 1M Kontextfenster und bis zu 32K maximale Output-Token (SWE-bench Verified 73.5% · GPQA Diamond 78.5%)
- Hohe Generierungsgeschwindigkeit von 80 Token/Sekunde bei nur 580 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($1.8/$7.2 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6 Astra | Qwen3.7 Max |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.15 | $0.38 |
Output Tokens / Task Tokens generated per task | 58K | 32K |
Reasoning Tokens / Task Thinking tokens burned per task | 32K | 14K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Astra | Qwen3.7 Max |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-astra | qwen3-7-max-instruct |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-09 | 2026-05 |
| Knowledge Cutoff | June 2026 | April 2026 |
| Architecture | Omni-Adaptive Autoregressive with Dynamic CoT | Next-Generation Deep Frontier Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($10/1M in) across full 272K window | Flat rate ($1.8/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $1/1M (no extra cache-write fee) | Cached input at $0.18/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | API Prompt Engineering & RAG Distillation |
| 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) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published | Alibaba Production Safety & Alignment Verified |
| 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 |
| Output Modalities | text, audio | text |
GPT-6 Astra vs Qwen3.7 Max: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-6 Astra or Qwen3.7 Max?
GPT-6 Astra is more intelligent. It scores 96 compared with 92 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Qwen3.7 Max is cheaper at $1.21 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.7 Max has lower latency, with a time to first token of 0.58s compared with 0.95s.
Which model has a larger context window?
Qwen3.7 Max has a larger context window, supporting 1M tokens compared with 272K tokens.
Fazit
Choose GPT-6 Astra for optimal für natürliche sprachsynthese, echtzeit-audioverarbeitung und präzise transkription, enterprise-rag-systeme und skalierbare api-pipelines., or choose Qwen3.7 Max for optimal für autonome agenten-workflows, zuverlässiges tool-calling und mehrstufige automatisierung, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: GPT-6 Astra or Qwen3.7 Max?+
GPT-6 Astra costs $10/1M input and $50/1M output tokens, while Qwen3.7 Max costs $1.8/1M input and $7.2/1M output tokens.
Which model has a larger context window: GPT-6 Astra or Qwen3.7 Max?+
GPT-6 Astra supports 272K tokens whereas Qwen3.7 Max supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: OpenAI & Alibaba Official Documentation