Head-to-Head Model Comparison

Qwen3.8 Max Preview vs DeepSeek V4.1-Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Qwen3.8 Max Preview vs DeepSeek V4.1-Flash: Benchmarks, Pricing & Speed Comparison

Alibaba tarafından geliştirilen Qwen3.8 Max Preview ve DeepSeek tarafından sunulan DeepSeek V4.1-Flash, kendi sınıflarının öne çıkan yapay zeka modelleridir. Qwen3.8 Max Preview, 1M token bağlam penceresi ve 1M token başına $0.00/$0.00 fiyat sunarken; DeepSeek V4.1-Flash, 1M token bağlam penceresi ve $0.30/$1.20 fiyatla gelir. Aşağıda tüm benchmark, hız ve maliyet detaylarını inceleyebilirsiniz.

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Alibaba

Qwen3.8 Max Preview

Açık Ağırlıklı Temel Model

Alibaba tarafından geliştirilen Qwen3.8 Max Preview; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 60 token/sn hız, 93/100 zeka indeksi ve ücretsiz açık ağ

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

DeepSeek

DeepSeek V4.1-Flash

Açık Ağırlıklı Temel Model

DeepSeek tarafından geliştirilen DeepSeek V4.1-Flash; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 110 token/sn hız, 58/100 zeka indeksi ve 1M token başın

Intelligence Index58 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

74.2%

Bir Bakışta Temel Farklar

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen3.8 Max PreviewDeepSeek V4.1-Flash
Intelligence Index
Composite reasoning & knowledge score (0-100)
9358
Provider
AlibabaDeepSeek
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 text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
32K384K
Throughput Speed
Tokens generated per second
60 tok/s110 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

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework
Coding
Qwen3.8 Max Preview
+1.0% SWE-bench lead
Cost
Qwen3.8 Max Preview
1.0x cheaper monthly runtime
Speed
DeepSeek V4.1-Flash
+83% faster generation
Context
Tied
1M tokens window

Doğrulanmış Benchmark Puanları

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

Qwen3.8 Max PreviewDeepSeek V4.1-Flash
SWE-bench Verified (Autonomous Coding)

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

75.2%vs74.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

79.5%vs90.9%
MMLU-Pro (Multi-task Language Understanding)

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

89%vs89.5%
LMSYS Chatbot Arena Elo

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

1485ptsvs1455pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs16.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs31.2%
SciCode (Scientific Computing)

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

N/Avs48%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

77%vs73%
AIME (Advanced Math Competition)

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

N/Avs92.4%
Long-Context Reasoning

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

N/Avs93%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs110 tok/s

Gecikme Süresi ve Yanıt Hızı

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
Qwen3.8 Max Preview0.68s
DeepSeek V4.1-Flash0.46s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen3.8 Max Preview9.01s
DeepSeek V4.1-Flash5.01s

Gerçek Zamanlı Token Maliyet Simülatörü

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)
Qwen3.8 Max Preview Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
DeepSeek V4.1-Flash Monthly API Bill
$1.54 / month
$0.3/1M in · $1.2/1M out
Qwen3.8 Max Preview saves approximately $1.54 / month (100%) under this workload.1.0x Savings

Qwen3.8 Max Preview · Strengths & Key Advantages

  • 1M bağlam penceresi ve yanıt başına maksimum 32K çıktı tokenı kapasitesi (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
  • Sadece 680 ms ilk token gecikmesi (TTFT) ile saniyede 60 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (2.4T total (95B MoE))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

DeepSeek V4.1-Flash · Strengths & Key Advantages

  • 1M bağlam penceresi ve yanıt başına maksimum 384K çıktı tokenı kapasitesi (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
  • Sadece 460 ms ilk token gecikmesi (TTFT) ile saniyede 110 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (763B total (8B prefill / 16B decode ))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

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

MetricQwen3.8 Max PreviewDeepSeek V4.1-Flash
Cost per Task
Estimated USD to finish one task
$0.00$0.27
Output Tokens / Task
Tokens generated per task
35K58K
Reasoning Tokens / Task
Thinking tokens burned per task
15K28K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

FeatureQwen3.8 Max PreviewDeepSeek V4.1-Flash
Official API Model ID (SDK)qwen3-8-max-preview-instructdeepseek-reasoner
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-072026-09
Knowledge CutoffApril 2026June 2026
ArchitectureColossus-Scale Open Sparse Mixture-of-ExpertsCausal Encoder-Decoder (20 enc + 20 dec layers)
Parameters2.4T 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 CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix caching at $0.006/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 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 VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Qwen3.8 Max Preview vs DeepSeek V4.1-Flash: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Qwen3.8 Max Preview (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de DeepSeek V4.1-Flash (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Qwen3.8 Max Preview karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, DeepSeek V4.1-Flash daha yüksek üretim hızı sağlar; Qwen3.8 Max Preview ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Qwen3.8 Max Preview mi yoksa DeepSeek V4.1-Flash mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Qwen3.8 Max Preview önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Qwen3.8 Max Preview ve DeepSeek V4.1-Flash API fiyatları nasıl karşılaştırılır?+

Qwen3.8 Max Preview 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, DeepSeek V4.1-Flash $0.30 / $1.20 seviyesindedir. Karma maliyette Qwen3.8 Max Preview daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Qwen3.8 Max Preview 1M token bağlam penceresi sunarken DeepSeek V4.1-Flash 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise DeepSeek V4.1-Flash öndedir.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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.

Resmi kaynaklar: Alibaba ve DeepSeek resmi API dokümantasyonu, sistem kartları ve bağımsız benchmark testleri (2026).