Qwen3 Coder Flash vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3 Coder Flash by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
بدّل وقارن بين أي نموذجين
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Qwen3 Coder Flash
نموذج ذكاء اصطناعي عالي الأداءنموذج Qwen3 Coder Flash المقدم من Alibaba يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $0.1 لكل مليون رمز إدخال و$0.4 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.10 / 1M
$0.40 / 1M
1M tokens
N/A
GPT-6 Astra
نموذج استدلال عميقنموذج GPT-6 Astra المقدم من OpenAI يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $10.0 لكل مليون رمز إدخال و$50.0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
أهم الفروقات في لمحة
- Qwen3 Coder Flash is 117.1x cheaper for API usage (input + output tokens combined).
- Qwen3 Coder Flash has a larger context window (1M vs 272K tokens).
- Qwen3 Coder Flash is faster at 130 tok/s vs 100 tok/s.
| Feature | Qwen3 Coder Flash | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 77 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 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 | 130 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 | $0.01 / 1M | $1.00 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3 Coder Flash · Strengths & Key Advantages
- بنية Optimized Lightweight Hosted Code Transformer متطورة من Alibaba مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($0.1/1M إدخال و$0.4/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
GPT-6 Astra · Strengths & Key Advantages
- بنية Omni-Adaptive Autoregressive with Dynamic CoT متطورة من OpenAI مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($10.0/1M إدخال و$50.0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3 Coder Flash | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.15 |
Output Tokens / Task Tokens generated per task | 15K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3 Coder Flash | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-flash-instruct | gpt-6-astra |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-07 | 2026-09 |
| Knowledge Cutoff | May 2025 | June 2026 |
| Architecture | Optimized Lightweight Hosted Code Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 1000K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.01/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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) | 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 | text, vision, audio |
| Output Modalities | text | text, audio |
Qwen3 Coder Flash vs GPT-6 Astra: إجابات سريعة (FAQ)
Which is more intelligent, Qwen3 Coder Flash or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 77 on the composite Intelligence Index.
Which model is faster?
Qwen3 Coder Flash is faster, generating 130 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Qwen3 Coder Flash is cheaper at $0.07 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3 Coder Flash has lower latency, with a time to first token of 0.33s compared with 0.95s.
Which model has a larger context window?
Qwen3 Coder Flash has a larger context window, supporting 1M tokens compared with 272K tokens.
الحكم النهائي
Choose Qwen3 Coder Flash for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات alibaba الإنتاجية, or choose GPT-6 Astra for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات openai الإنتاجية.
الأسئلة الشائعة (FAQ)
Which is cheaper: Qwen3 Coder Flash or GPT-6 Astra?+
Qwen3 Coder Flash costs $0.1/1M input and $0.4/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Qwen3 Coder Flash or GPT-6 Astra?+
Qwen3 Coder Flash supports 1M 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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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.
المصادر الرسمية: Alibaba & OpenAI Official Documentation