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Head-to-Head Model Comparison

GPT-6 Astra vs Qwen3.7 Max

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs अनुसंधान दल द्वारा समीक्षितअंतिम समीक्षा: October 2026संपादकीय नीति
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Compare GPT-6 Astra by OpenAI against Qwen3.7 Max by Alibaba across real-world coding, reasoning, token economics, and latency.

आमने-सामने मॉडल तुलना (VS)

किसी भी मॉडल को बदलें और तुलना करें

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

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Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:
OpenAI

GPT-6 Astra

डीप रीजनिंग मॉडल

OpenAI द्वारा विकसित GPT-6 Astra एक उन्नत AI मॉडल है, जिसमें 1M टोकन का कॉन्टेक्स्ट विंडो और $10.0/1M इनपुट तथा $50.0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

Alibaba

Qwen3.7 Max

डीप रीजनिंग मॉडल

Alibaba द्वारा विकसित Qwen3.7 Max एक उन्नत AI मॉडल है, जिसमें 1M टोकन का कॉन्टेक्स्ट विंडो और $1.8/1M इनपुट तथा $7.2/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index92 / 100
Input Token:

$1.80 / 1M

Output Token:

$7.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

73.5%

एक नज़र में मुख्य अंतर

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-6 AstraQwen3.7 Max
Intelligence Index
Composite reasoning & knowledge score (0-100)
9692
Provider
OpenAIAlibaba
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 text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K32K
Throughput Speed
Tokens generated per second
100 tok/s80 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

किस क्षेत्र में कौन आगे है?

At-a-glance decision framework
Coding
GPT-6 Astra
+8.9% SWE-bench lead
Cost
Qwen3.7 Max
6.5x cheaper monthly runtime
Speed
GPT-6 Astra
+25% faster generation
Context
Qwen3.7 Max
1M tokens window

सत्यापित बेंचमार्क स्कोर तुलना

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

GPT-6 AstraQwen3.7 Max
SWE-bench Verified (Autonomous Coding)

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

82.4%vs73.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

80.9%vs78.5%
MMLU-Pro (Multi-task Language Understanding)

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

89.9%vs88.5%
LMSYS Chatbot Arena Elo

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

1520ptsvs1470pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

18.2%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

63.8%vsN/A
SciCode (Scientific Computing)

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

54.9%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.8%vsN/A
AIME (Advanced Math Competition)

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

93.6%vs86%
Long-Context Reasoning

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

91.2%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

100 tok/s vs80 tok/s

लेटेंसी और रिस्पॉन्स समय

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
GPT-6 Astra0.95s
Qwen3.7 Max0.58s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-6 Astra5.95s
Qwen3.7 Max6.83s

रियल-टाइम टोकन लागत सिम्युलेटर

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)
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Qwen3.7 Max Monthly API Bill
$9.45 / month
$1.8/1M in · $7.2/1M out
Qwen3.7 Max saves approximately $52.05 / month (85%) under this workload.6.5x Savings

GPT-6 Astra · Strengths & Key Advantages

  • OpenAI की उन्नत Omni-Adaptive Autoregressive with Dynamic CoT आर्किटेक्चर और 1M कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($10.0/1M इनपुट, $50.0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Qwen3.7 Max · Strengths & Key Advantages

  • Alibaba की उन्नत Next-Generation Deep Frontier Reasoning Transformer आर्किटेक्चर और 1M कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($1.8/1M इनपुट, $7.2/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Token Use & Task Economics

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

MetricGPT-6 AstraQwen3.7 Max
Cost per Task
Estimated USD to finish one task
$2.15$0.38
Output Tokens / Task
Tokens generated per task
58K32K
Reasoning Tokens / Task
Thinking tokens burned per task
32K14K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6 AstraQwen3.7 Max
Official API Model ID (SDK)gpt-6-astraqwen3-7-max-instruct
API ProtocolOpenAI Responses API & Chat Completions v1Alibaba REST API (OpenAI-Compatible)
Release Date2026-092026-05
Knowledge CutoffJune 2026April 2026
ArchitectureOmni-Adaptive Autoregressive with Dynamic CoTNext-Generation Deep Frontier Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM5,000 – 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 windowFlat rate ($1.8/1M in) across full 1000K window
Prompt Cache Policy & TTLAutomatic prefix caching at $1/1M (no extra cache-write fee)Cached input at $0.18/1M tokens
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 · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)API Prompt Engineering & RAG Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)Text BPE (~4 chars/token)
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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedAlibaba Production Safety & Alignment Verified
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, audiotext
Output Modalitiestext, audiotext

GPT-6 Astra vs Qwen3.7 Max: त्वरित प्रश्नोत्तर (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.

निष्कर्ष

Choose GPT-6 Astra for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और openai आधारित प्रोडक्शन सिस्टम, or choose Qwen3.7 Max for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और alibaba आधारित प्रोडक्शन सिस्टम.

अक्सर पूछे जाने वाले प्रश्न (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.

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.

स्रोत: OpenAI & Alibaba Official Documentation