o3-pro vs Qwen3.8 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3-pro by OpenAI against Qwen3.8 Max Preview by Alibaba across real-world coding, reasoning, token economics, and latency.
Troque e Compare Qualquer Modelo
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Live comparison: no in-depth editorial review has been published for this pair yet.
o3-pro
Modelo de Raciocínio ProfundoO o3-pro da OpenAI é um modelo de inteligência artificial com janela de contexto de 200K tokens e preço de API de $20/1M na entrada e $80/1M na saída, projetado para programação, raciocínio e automação empresarial.
$20.00 / 1M
$80.00 / 1M
200K tokens
71.5%
Qwen3.8 Max Preview
Modelo de Pesos AbertosO Qwen3.8 Max Preview da Alibaba é um modelo de inteligência artificial com janela de contexto de 1M tokens e preço de API de $2.0/1M na entrada e $6.0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
Principais Diferenças em Resumo
- 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%).
| Feature | o3-pro | Qwen3.8 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 93 |
Provider | OpenAI | Alibaba |
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 text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 100K | 32K |
Throughput Speed Tokens generated per second | 30 tok/s | 60 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 |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o3-pro · Strengths & Key Advantages
- Arquitetura High-effort reasoning transformer avançada da OpenAI com contexto de 200K tokens
- Excelente relação custo-benefício ($20/1M entrada, $80/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Qwen3.8 Max Preview · Strengths & Key Advantages
- Arquitetura Colossus-Scale Open Sparse Mixture-of-Experts avançada da Alibaba com contexto de 1M tokens
- Excelente relação custo-benefício ($2.0/1M entrada, $6.0/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3-pro | Qwen3.8 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $11.68 | $0.00 |
Output Tokens / Task Tokens generated per task | 58K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 38K | 15K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | o3-pro | Qwen3.8 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | o3-pro | qwen3-8-max-preview-instruct |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-07 |
| Knowledge Cutoff | June 2024 | April 2026 |
| Architecture | High-effort reasoning transformer | Colossus-Scale Open Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.4T total (95B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (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 window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $5/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text 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 VRAM | Cloud-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 Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-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 Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
o3-pro vs Qwen3.8 Max Preview: Respostas Rápidas (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.
Veredito
Choose o3-pro for automação corporativa, engenharia de software, agentes autônomos e pipelines de produção com openai, or choose Qwen3.8 Max Preview for automação corporativa, engenharia de software, agentes autônomos e pipelines de produção com alibaba.
Perguntas Frequentes (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.
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.
Fontes oficiais: OpenAI & Alibaba Official Documentation