Head-to-Head Model Comparison

Qwen3.5 27B vs DeepSeek Chat

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

İstediğiniz Modelleri Seçin ve Karşılaştırın

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Qwen3.5 27BProfile

DeepSeek ChatProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.5 27B

Açık Ağırlıklı Temel Model

Alibaba tarafından geliştirilen Qwen3.5 27B; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 262K bağlam penceresi, 90 token/sn hız, 75/100 zeka indeksi ve ücretsiz açık ağırlık

Intelligence Index75 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

DeepSeek

DeepSeek Chat

Açık Ağırlıklı Temel Model

DeepSeek tarafından geliştirilen DeepSeek Chat; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 75 token/sn hız, 50/100 zeka indeksi ve 1M token başına $0.28

Intelligence Index50 / 100

Input Token:

$0.28 / 1M

Output Token:

$0.42 / 1M

Context Window:

1M tokens

SWE-bench Verified:

54%

Bir Bakışta Temel Farklar

Qwen3.5 27B is 1.0x cheaper for API usage (input + output tokens combined).
DeepSeek Chat has a larger context window (1M vs 262K tokens).
Qwen3.5 27B is faster at 90 tok/s vs 75 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Qwen3.5 27B	DeepSeek Chat
Intelligence Index Composite reasoning & knowledge score (0-100)	75	50
Provider	Alibaba	DeepSeek
Input Price Cost per million input tokens	$0.00 / 1M	$0.28 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.42 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.12 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	90 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	N/A	54%
GPQA Diamond Graduate-level reasoning	56.5%	65%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.03 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Qwen3.5 27B

1.0x cheaper monthly runtime

Speed

Qwen3.5 27B

+20% faster generation

Context

DeepSeek Chat

1M tokens window

Doğrulanmış Benchmark Puanları

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

Qwen3.5 27BDeepSeek Chat

SWE-bench Verified (Autonomous Coding)

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

N/Avs54%

GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs65%

MMLU-Pro (Multi-task Language Understanding)

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

74%vs79.5%

LMSYS Chatbot Arena Elo

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

1285ptsvs1335pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs10.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs36%

SciCode (Scientific Computing)

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

N/Avs33.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs56.5%

AIME (Advanced Math Competition)

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

N/Avs74%

Long-Context Reasoning

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

N/Avs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs75 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

Qwen3.5 27B0.41s

DeepSeek Chat0.52s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen3.5 27B5.97s

DeepSeek Chat7.19s

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen3.5 27B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

DeepSeek Chat Monthly API Bill

$0.84 / month

$0.28/1M in · $0.42/1M out

Qwen3.5 27B saves approximately $0.84 / month (100%) under this workload.1.0x Savings

Qwen3.5 27B · Strengths & Key Advantages

262K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (GPQA Diamond 56.5% · MMLU-Pro 74%)
Sadece 410 ms ilk token gecikmesi (TTFT) ile saniyede 90 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (27B)

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 Chat · Strengths & Key Advantages

1M bağlam penceresi ve yanıt başına maksimum 16K çıktı tokenı kapasitesi (SWE-bench Verified 54% · GPQA Diamond 65%)
Sadece 520 ms ilk token gecikmesi (TTFT) ile saniyede 75 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (671B Total (37B ))

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.

Metric	Qwen3.5 27B	DeepSeek Chat
Cost per Task Estimated USD to finish one task	$0.00	$0.15
Output Tokens / Task Tokens generated per task	13K	35K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3.5 27B	DeepSeek Chat
Official API Model ID (SDK)	qwen3-5-27b-instruct	deepseek-chat
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2025-12
Knowledge Cutoff	December 2025	September 2025
Architecture	Dense Multimodal Autoregressive Transformer	Multi-Head Latent Attention (MLA) Sparse MoE
Parameters	27B	671B Total (37B 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 ($0.28/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $0.028/1M (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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 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
Output Modalities	text	text

Qwen3.5 27B vs DeepSeek Chat: Hızlı Yanıtlar (SSS)

Which is more intelligent, Qwen3.5 27B or DeepSeek Chat?

Qwen3.5 27B is more intelligent. It scores 75 compared with 50 on the composite Intelligence Index.

Which model is faster?

Qwen3.5 27B is faster, generating 90 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Qwen3.5 27B is cheaper at $0.00 per 1M tokens blended, compared with $0.12 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.5 27B has lower latency, with a time to first token of 0.41s compared with 0.52s.

Which model has a larger context window?

DeepSeek Chat has a larger context window, supporting 1M tokens compared with 262.1K tokens.

Sonuç

Hem Qwen3.5 27B (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 Chat (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: Qwen3.5 27B karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Qwen3.5 27B daha yüksek üretim hızı sağlar; Qwen3.5 27B ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Qwen3.5 27B mi yoksa DeepSeek Chat mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Qwen3.5 27B önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Qwen3.5 27B ve DeepSeek Chat API fiyatları nasıl karşılaştırılır?+

Qwen3.5 27B 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, DeepSeek Chat $0.28 / $0.42 seviyesindedir. Karma maliyette Qwen3.5 27B daha avantajlıdır.

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

Qwen3.5 27B 262.1K token bağlam penceresi sunarken DeepSeek Chat 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Qwen3.5 27B 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.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Qwen3.5 27B vs DeepSeek Chat

İstediğiniz Modelleri Seçin ve Karşılaştırın

Qwen3.5 27B

DeepSeek Chat

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Qwen3.5 27B · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek Chat · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Qwen3.5 27B vs DeepSeek Chat: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

Related reading