Qwen 2.5 Coder 32B vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen 2.5 Coder 32B 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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Live comparison: no in-depth editorial review has been published for this pair yet.
Qwen 2.5 Coder 32B
نموذج مفتوح الأوزاننموذج Qwen 2.5 Coder 32B المقدم من Alibaba يوفر نافذة سياق تصل إلى 128K رمز، بتكلفة $0.2 لكل مليون رمز إدخال و$0.6 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.20 / 1M
$0.60 / 1M
128K tokens
48.6%
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
- Qwen 2.5 Coder 32B is 68.3x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 128K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 78 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 48.6%).
| Feature | Qwen 2.5 Coder 32B | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.20 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.13 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 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 | 78 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 48.6% | 82.4% |
GPQA Diamond Graduate-level reasoning | 48.2% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $1.00 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج 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.
Qwen 2.5 Coder 32B · Strengths & Key Advantages
- بنية Dense Code-Specialized Transformer متطورة من Alibaba مع سعة سياق تبلغ 128K رمز
- كفاءة عالية في التكلفة ($0.2/1M إدخال و$0.6/1M 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 | Qwen 2.5 Coder 32B | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $2.15 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 Coder 32B | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-coder-32b-instruct | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-11 | 2026-09 |
| Knowledge Cutoff | September 2024 | June 2026 |
| Architecture | Dense Code-Specialized Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 32B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 128K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.04/1M 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 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 |
Qwen 2.5 Coder 32B vs GPT-6 Astra: إجابات سريعة (FAQ)
Which is more intelligent, Qwen 2.5 Coder 32B or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 43 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Qwen 2.5 Coder 32B is cheaper at $0.13 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 Coder 32B has lower latency, with a time to first token of 0.54s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 128K tokens.
الحكم النهائي
Choose Qwen 2.5 Coder 32B 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: Qwen 2.5 Coder 32B or GPT-6 Astra?+
Qwen 2.5 Coder 32B costs $0.2/1M input and $0.6/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Qwen 2.5 Coder 32B or GPT-6 Astra?+
Qwen 2.5 Coder 32B supports 128K 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