Qwen3.8 Max Preview vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.8 Max Preview da Alibaba e GPT-6 Astra da OpenAI são dois modelos de IA de destaque. O Qwen3.8 Max Preview oferece uma janela de contexto de 1M tokens a $0.00/$0.00 por milhão de tokens, enquanto o GPT-6 Astra oferece 272K tokens de contexto a $10.00/$50.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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Qwen3.8 Max Preview
Modelo Base de Pesos AbertosO Qwen3.8 Max Preview da Alibaba é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 1M, velocidade de 60 tokens/s, índice de inteligência de 93/100
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
GPT-6 Astra
Modelo Nativo de Voz e ÁudioO GPT-6 Astra da OpenAI é um modelo de IA projetado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, oferecendo janela de contexto de 272K, velocidade de 100 tokens/s, índice de inteligência de 96/100 e ta
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
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 272K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 60 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 75.2%).
| Feature | Qwen3.8 Max Preview | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 96 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 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 | 32K | 128K |
Throughput Speed Tokens generated per second | 60 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 75.2% | 82.4% |
GPQA Diamond Graduate-level reasoning | 79.5% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 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.
Qwen3.8 Max Preview · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 32K tokens (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
- Taxa de transferência rápida de 60 tokens/segundo com latência inicial (TTFT) de 680 ms
- Pesos abertos (2.4T total (95B MoE)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
GPT-6 Astra · Strengths & Key Advantages
- Janela de contexto de 272K com limite máximo de saída de 128K tokens (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Taxa de transferência rápida de 100 tokens/segundo com latência inicial (TTFT) de 950 ms
- Economia de API competitiva ($10/$50 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.8 Max Preview | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 32K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.8 Max Preview | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-8-max-preview-instruct | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | Colossus-Scale Open Sparse Mixture-of-Experts | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 2.4T total (95B active MoE) | 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 | 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) · Vision (~258–765 tokens per image tile) | 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) · 1488 GB VRAM (4-bit Quantized) · 5160 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, vision | text, vision, audio |
| Output Modalities | text | text, audio |
Qwen3.8 Max Preview vs GPT-6 Astra: Respostas Rápidas (FAQ)
Which is more intelligent, Qwen3.8 Max Preview or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 93 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $7.70 (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 0.95s.
Which model has a larger context window?
Qwen3.8 Max Preview has a larger context window, supporting 1M tokens compared with 272K tokens.
Veredito
Tanto Qwen3.8 Max Preview (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto GPT-6 Astra (Indicado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: GPT-6 Astra lidera em inteligência geral e programação, GPT-6 Astra oferece geração mais rápida e Qwen3.8 Max Preview apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Qwen3.8 Max Preview ou GPT-6 Astra?+
Nas avaliações de inteligência e código, GPT-6 Astra leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Qwen3.8 Max Preview ou GPT-6 Astra?+
O Qwen3.8 Max Preview custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o GPT-6 Astra custa $10.00 / $50.00. No custo combinado, Qwen3.8 Max Preview é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Qwen3.8 Max Preview suporta 1M tokens de contexto contra 272K tokens do GPT-6 Astra, enquanto GPT-6 Astra entrega maior velocidade de geração.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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: Documentação oficial de API da Alibaba e OpenAI e benchmarks independentes (2026).