Qwen3.8 Max Preview vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.8 Max Preview dari Alibaba dan DeepSeek V4.1-Flash dari DeepSeek adalah dua model AI unggulan. Qwen3.8 Max Preview menawarkan jendela konteks 1M token dengan harga $0.00/$0.00 per 1 juta token, sedangkan DeepSeek V4.1-Flash menyediakan konteks 1M token seharga $0.30/$1.20 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Qwen3.8 Max Preview
Model Dasar Bobot TerbukaQwen3.8 Max Preview dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 60 token/detik, indeks kecerdasan 93/100, serta akses g
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
DeepSeek V4.1-Flash
Model Dasar Bobot TerbukaDeepSeek V4.1-Flash dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 110 token/detik, indeks kecerdasan 58/100, serta tarif
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.8 Max Preview · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
- Kecepatan generasi tinggi 60 token/detik dengan latensi token pertama (TTFT) hanya 680 ms
- Bobot terbuka (2.4T total (95B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
DeepSeek V4.1-Flash · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 384K token (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
- Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 460 ms
- Bobot terbuka (763B total (8B prefill / 16B decode )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Qwen3.8 Max Preview (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek V4.1-Flash (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.8 Max Preview unggul dalam penalaran dan pemrograman, DeepSeek V4.1-Flash memberikan kecepatan generasi token lebih tinggi, dan Qwen3.8 Max Preview lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen3.8 Max Preview atau DeepSeek V4.1-Flash?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.8 Max Preview memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen3.8 Max Preview vs DeepSeek V4.1-Flash?+
Qwen3.8 Max Preview mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan DeepSeek V4.1-Flash sebesar $0.30 / $1.20. Secara keseluruhan, Qwen3.8 Max Preview lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen3.8 Max Preview mendukung konteks 1M token dibandingkan 1M token pada DeepSeek V4.1-Flash, sementara DeepSeek V4.1-Flash unggul dalam kecepatan output token per detik.
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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- Llama 5 vs Qwen3.8 Max Preview: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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.
Sumber resmi: Dokumentasi API resmi Alibaba & DeepSeek serta evaluasi benchmark independen (2026).