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Head-to-Head Model Comparison

o3-pro vs Qwen3.8 Max Preview

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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OpenAIAlibabareasoningflagship

Compare o3-pro by OpenAI against Qwen3.8 Max Preview by Alibaba across real-world coding, reasoning, token economics, and latency.

1:1 모델 맞대결 비교 (VS)

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

Popular Matchups:
OpenAI

o3-pro

심층 추론 및 논리 엔진

OpenAI의 o3-pro 모델은 심층 논리 추론, 고급 수학 문제 해결 및 다단계 과학적 분석에 최적화된 AI 모델로, 200K 컨텍스트 윈도우, 초당 30 토큰의 생성 속도, 종합 지능 지수 58/100점(1M 토큰당 입력 $20 / 출력 $80)을 제공합니다.

Intelligence Index58 / 100
Input Token:

$20.00 / 1M

Output Token:

$80.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

71.5%

Alibaba

Qwen3.8 Max Preview

오픈 웨이트 파운데이션 모델

Alibaba의 Qwen3.8 Max Preview 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 60 토큰의 생성 속도, 종합 지능 지수 93/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

한눈에 보는 핵심 차이점

  • Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
  • Qwen3.8 Max Preview has a larger context window (1M vs 200K tokens).
  • Qwen3.8 Max Preview is faster at 60 tok/s vs 30 tok/s.
  • Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 71.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo3-proQwen3.8 Max Preview
Intelligence Index
Composite reasoning & knowledge score (0-100)
5893
Provider
OpenAIAlibaba
Input Price
Cost per million input tokens
$20.00 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$80.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$15.50 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
100K32K
Throughput Speed
Tokens generated per second
30 tok/s60 tok/s
SWE-bench Verified
Real-world software engineering
71.5%75.2%
GPQA Diamond
Graduate-level reasoning
79.5%79.5%
Cached Input Price
Prompt caching discount per 1M tokens
$5.00 / 1M$0.00 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Qwen3.8 Max Preview
+3.7% SWE-bench lead
Cost
Qwen3.8 Max Preview
1.0x cheaper monthly runtime
Speed
Qwen3.8 Max Preview
+100% faster generation
Context
Qwen3.8 Max Preview
1M tokens window

검증된 벤치마크 점수 비교

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

o3-proQwen3.8 Max Preview
SWE-bench Verified (Autonomous Coding)

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

71.5%vs75.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

79.5%vs79.5%
MMLU-Pro (Multi-task Language Understanding)

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

88.5%vs89%
LMSYS Chatbot Arena Elo

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

1352ptsvs1485pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.6%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

47.5%vsN/A
SciCode (Scientific Computing)

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

45.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73%vs77%
AIME (Advanced Math Competition)

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

93%vsN/A
Long-Context Reasoning

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

80.5%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

30 tok/s vs60 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
o3-pro5.20s
Qwen3.8 Max Preview0.68s
Est. End-to-End Response (500 tokens)TTFT + generation time
o3-pro21.87s
Qwen3.8 Max Preview9.01s

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)
o3-pro Monthly API Bill
$109.50 / month
$20/1M in · $80/1M out
Qwen3.8 Max Preview Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3.8 Max Preview saves approximately $109.50 / month (100%) under this workload.1.0x Savings

o3-pro · Strengths & Key Advantages

  • 200K 컨텍스트 윈도우 및 최대 100K 출력 토큰 생성 지원 (SWE-bench Verified 71.5% · GPQA Diamond 79.5%)
  • 초당 30 토큰의 빠른 처리 속도와 5200ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $20 / 출력 $80)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
  • 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Qwen3.8 Max Preview · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 32K 출력 토큰 생성 지원 (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
  • 초당 60 토큰의 빠른 처리 속도와 680ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (2.4T total (95B MoE))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Token Use & Task Economics

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

Metrico3-proQwen3.8 Max Preview
Cost per Task
Estimated USD to finish one task
$11.68$0.00
Output Tokens / Task
Tokens generated per task
58K35K
Reasoning Tokens / Task
Thinking tokens burned per task
38K15K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Featureo3-proQwen3.8 Max Preview
Official API Model ID (SDK)o3-proqwen3-8-max-preview-instruct
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-062026-07
Knowledge CutoffJune 2024April 2026
ArchitectureHigh-effort reasoning transformerColossus-Scale Open Sparse Mixture-of-Experts
ParametersConfidential2.4T total (95B active MoE)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($20/1M in) across full 200K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix caching at $5/1M (no extra cache-write fee)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & 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)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

o3-pro vs Qwen3.8 Max Preview: 핵심 요약 문답 (FAQ)

Which is more intelligent, o3-pro or Qwen3.8 Max Preview?

Qwen3.8 Max Preview is more intelligent. It scores 93 compared with 58 on the composite Intelligence Index.

Which model is faster?

Qwen3.8 Max Preview is faster, generating 60 tokens per second compared with 30 tokens per second.

Which model is cheaper to run?

Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $15.50 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.8 Max Preview has lower latency, with a time to first token of 0.68s compared with 5.20s.

Which model has a larger context window?

Qwen3.8 Max Preview has a larger context window, supporting 1M tokens compared with 200K tokens.

종합 평가

Choose o3-pro for 심층 논리 추론, 고급 수학 문제 해결 및 다단계 과학적 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Qwen3.8 Max Preview for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..

자주 묻는 질문 (FAQ)

Which is cheaper: o3-pro or Qwen3.8 Max Preview?+

o3-pro costs $20/1M input and $80/1M output tokens, while Qwen3.8 Max Preview costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: o3-pro or Qwen3.8 Max Preview?+

o3-pro supports 200K tokens whereas Qwen3.8 Max Preview 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.

공식 출처: OpenAI & Alibaba Official Documentation