Qwen3.8 Max Preview vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.8 Max Preview da Alibaba e DeepSeek V4.1-Flash da DeepSeek 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 DeepSeek V4.1-Flash oferece 1M tokens de contexto a $0.30/$1.20 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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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%
DeepSeek V4.1-Flash
Modelo Base de Pesos AbertosO DeepSeek V4.1-Flash da DeepSeek 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 110 tokens/s, índice de inteligência de 58/10
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Principais Diferenças em Resumo
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 60 tok/s.
- Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 74.2%).
| Feature | Qwen3.8 Max Preview | DeepSeek V4.1-Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 58 |
Provider | Alibaba | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $0.30 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $1.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.18 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 32K | 384K |
Throughput Speed Tokens generated per second | 60 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 75.2% | 74.2% |
GPQA Diamond Graduate-level reasoning | 79.5% | 90.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.01 / 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
DeepSeek V4.1-Flash · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 384K tokens (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
- Taxa de transferência rápida de 110 tokens/segundo com latência inicial (TTFT) de 460 ms
- Pesos abertos (763B total (8B prefill / 16B decode )) 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.8 Max Preview | DeepSeek V4.1-Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.27 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 28K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.8 Max Preview | DeepSeek V4.1-Flash |
|---|---|---|
| Official API Model ID (SDK) | qwen3-8-max-preview-instruct | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | Colossus-Scale Open Sparse Mixture-of-Experts | Causal Encoder-Decoder (20 enc + 20 dec layers) |
| Parameters | 2.4T total (95B active MoE) | 763B total (8B prefill / 16B decode active) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.3/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.006/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Qwen3.8 Max Preview vs DeepSeek V4.1-Flash: Respostas Rápidas (FAQ)
Which is more intelligent, Qwen3.8 Max Preview or DeepSeek V4.1-Flash?
Qwen3.8 Max Preview is more intelligent. It scores 93 compared with 58 on the composite Intelligence Index.
Which model is faster?
DeepSeek V4.1-Flash is faster, generating 110 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 $0.18 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 0.68s.
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 DeepSeek V4.1-Flash (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen3.8 Max Preview lidera em inteligência geral e programação, DeepSeek V4.1-Flash 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 DeepSeek V4.1-Flash?+
Nas avaliações de inteligência e código, Qwen3.8 Max Preview leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Qwen3.8 Max Preview ou DeepSeek V4.1-Flash?+
O Qwen3.8 Max Preview custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o DeepSeek V4.1-Flash custa $0.30 / $1.20. 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 1M tokens do DeepSeek V4.1-Flash, enquanto DeepSeek V4.1-Flash 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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- Grok 4.7 vs. DeepSeek V4.1-Flash: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Llama 5 vs. Qwen3.8 Max Preview: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Mistral Medium 3.5 vs. DeepSeek V4.1-Flash: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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 DeepSeek e benchmarks independentes (2026).