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Head-to-Head Model Comparison

Qwen 2.5 7B Instruct vs GPT-6 Astra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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AlibabaOpenAIlightweightflagship

Compare Qwen 2.5 7B Instruct by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

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:
Alibaba

Qwen 2.5 7B Instruct

오픈 웨이트 모델

Alibaba의 Qwen 2.5 7B Instruct 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0.06/1M 토큰, 출력 $0.12/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index35 / 100
Input Token:

$0.06 / 1M

Output Token:

$0.12 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

28.5%

OpenAI

GPT-6 Astra

심층 추론 모델

OpenAI의 GPT-6 Astra 모델은 1M 토큰 컨텍스트 윈도우와 입력 $10.0/1M 토큰, 출력 $50.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

한눈에 보는 핵심 차이점

  • Qwen 2.5 7B Instruct is 284.7x cheaper for API usage (input + output tokens combined).
  • GPT-6 Astra has a larger context window (272K vs 131K tokens).
  • Qwen 2.5 7B Instruct is faster at 130 tok/s vs 100 tok/s.
  • GPT-6 Astra leads on SWE-bench coding (82.4% vs 28.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen 2.5 7B InstructGPT-6 Astra
Intelligence Index
Composite reasoning & knowledge score (0-100)
3596
Provider
AlibabaOpenAI
Input Price
Cost per million input tokens
$0.06 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$0.12 / 1M$50.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$7.70 / 1M
Context Window
Maximum input + output tokens
131.1K tokens~197 A4 pages of text272K tokens~408 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
130 tok/s100 tok/s
SWE-bench Verified
Real-world software engineering
28.5%82.4%
GPQA Diamond
Graduate-level reasoning
36.8%80.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$1.00 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
GPT-6 Astra
+53.9% SWE-bench lead
Cost
Qwen 2.5 7B Instruct
284.7x cheaper monthly runtime
Speed
Qwen 2.5 7B Instruct
+30% faster generation
Context
GPT-6 Astra
272K tokens window

검증된 벤치마크 점수 비교

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

Qwen 2.5 7B InstructGPT-6 Astra
SWE-bench Verified (Autonomous Coding)

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

28.5%vs82.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

36.8%vs80.9%
MMLU-Pro (Multi-task Language Understanding)

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

56.4%vs89.9%
LMSYS Chatbot Arena Elo

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

1205ptsvs1520pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

3.2%vs18.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

18.4%vs63.8%
SciCode (Scientific Computing)

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

18.2%vs54.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

29.8%vs79.8%
AIME (Advanced Math Competition)

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

38.6%vs93.6%
Long-Context Reasoning

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

58.2%vs91.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs100 tok/s

지연 시간(Latency) 및 응답 속도

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
Qwen 2.5 7B Instruct0.29s
GPT-6 Astra0.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen 2.5 7B Instruct4.14s
GPT-6 Astra5.95s

실시간 API 토큰 비용 시뮬레이터

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)
Qwen 2.5 7B Instruct Monthly API Bill
$0.22 / month
$0.06/1M in · $0.12/1M out
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Qwen 2.5 7B Instruct saves approximately $61.28 / month (100%) under this workload.284.7x Savings

Qwen 2.5 7B Instruct · Strengths & Key Advantages

  • Alibaba의 첨단 Dense Autoregressive Transformer 아키텍처와 최대 131K 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $0.06/1M, 출력 $0.12/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

GPT-6 Astra · Strengths & Key Advantages

  • OpenAI의 첨단 Omni-Adaptive Autoregressive with Dynamic CoT 아키텍처와 최대 1M 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $10.0/1M, 출력 $50.0/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Token Use & Task Economics

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

MetricQwen 2.5 7B InstructGPT-6 Astra
Cost per Task
Estimated USD to finish one task
$0.05$2.15
Output Tokens / Task
Tokens generated per task
20K58K
Reasoning Tokens / Task
Thinking tokens burned per task
0K32K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureQwen 2.5 7B InstructGPT-6 Astra
Official API Model ID (SDK)qwen-2-5-7b-instructgpt-6-astra
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2024-092026-09
Knowledge CutoffJune 2024June 2026
ArchitectureDense Autoregressive TransformerOmni-Adaptive Autoregressive with Dynamic CoT
Parameters7BConfidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.06/1M in) across full 131K windowFlat rate ($10/1M in) across full 272K window
Prompt Cache Policy & TTLCached input at $0.012/1M tokensAutomatic prefix caching at $1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-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 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision, audio
Output Modalitiestexttext, audio

Qwen 2.5 7B Instruct vs GPT-6 Astra: 핵심 요약 문답 (FAQ)

Which is more intelligent, Qwen 2.5 7B Instruct or GPT-6 Astra?

GPT-6 Astra is more intelligent. It scores 96 compared with 35 on the composite Intelligence Index.

Which model is faster?

Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 100 tokens per second.

Which model is cheaper to run?

Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s 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.

종합 평가

Choose Qwen 2.5 7B Instruct for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 alibaba 기반 프로덕션 파이프라인, or choose GPT-6 Astra for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인.

자주 묻는 질문 (FAQ)

Which is cheaper: Qwen 2.5 7B Instruct or GPT-6 Astra?+

Qwen 2.5 7B Instruct costs $0.06/1M input and $0.12/1M 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 7B Instruct or GPT-6 Astra?+

Qwen 2.5 7B Instruct supports 131K tokens whereas GPT-6 Astra supports 272K 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.

공식 출처: Alibaba & OpenAI Official Documentation