Head-to-Head Model Comparison

Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct

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

Qwen3-Next 80B-A3B InstructProfile

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

Alibaba

Qwen3-Next 80B-A3B Instruct

Açık Ağırlıklı Temel Model

Alibaba tarafından geliştirilen Qwen3-Next 80B-A3B Instruct; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 131K bağlam penceresi, 135 token/sn hız, 78/100 zeka indeksi ve ücret

Intelligence Index78 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Bir Bakışta Temel Farklar

Both models cost the same under this workload — choose on capability, not price.
Qwen3.5 27B has a larger context window (262K vs 131K tokens).
Qwen3-Next 80B-A3B Instruct is faster at 135 tok/s vs 90 tok/s.

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

Feature	Qwen3.5 27B	Qwen3-Next 80B-A3B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	75	78
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	90 tok/s	135 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
GPQA Diamond Graduate-level reasoning	56.5%	60%

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3-Next 80B-A3B Instruct

+50% faster generation

Context

Qwen3.5 27B

262.1K tokens window

Doğrulanmış Benchmark Puanları

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

Qwen3.5 27BQwen3-Next 80B-A3B Instruct

GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs60%

MMLU-Pro (Multi-task Language Understanding)

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

74%vs78%

LMSYS Chatbot Arena Elo

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

1285ptsvs1315pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs135 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

Qwen3-Next 80B-A3B Instruct0.31s

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

Qwen3.5 27B5.97s

Qwen3-Next 80B-A3B Instruct4.01s

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

Qwen3-Next 80B-A3B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

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

Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages

131K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (GPQA Diamond 60% · MMLU-Pro 78%)
Sadece 310 ms ilk token gecikmesi (TTFT) ile saniyede 135 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (80B total (3B 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

Token Use & Task Economics

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

Metric	Qwen3.5 27B	Qwen3-Next 80B-A3B Instruct
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	13K	15K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3.5 27B	Qwen3-Next 80B-A3B Instruct
Official API Model ID (SDK)	qwen3-5-27b-instruct	qwen3-next-80b-a3b-instruct-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2025-09
Knowledge Cutoff	December 2025	August 2025
Architecture	Dense Multimodal Autoregressive Transformer	Standard Instruction-Tuned Sparse Mixture-of-Experts
Parameters	27B	80B total (3B active MoE)
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)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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) · 50 GB VRAM (4-bit Quantized) · 172 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 Qwen3-Next 80B-A3B Instruct: Hızlı Yanıtlar (SSS)

Which is more intelligent, Qwen3.5 27B or Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is more intelligent. It scores 78 compared with 75 on the composite Intelligence Index.

Which model is faster?

Qwen3-Next 80B-A3B Instruct is faster, generating 135 tokens per second compared with 90 tokens per second.

Which model has lower latency?

Qwen3-Next 80B-A3B Instruct has lower latency, with a time to first token of 0.31s compared with 0.41s.

Which model has a larger context window?

Qwen3.5 27B has a larger context window, supporting 262.1K tokens compared with 131.1K 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 Qwen3-Next 80B-A3B Instruct (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-Next 80B-A3B Instruct karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Qwen3-Next 80B-A3B Instruct 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 Qwen3-Next 80B-A3B Instruct mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Qwen3-Next 80B-A3B Instruct önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Qwen3.5 27B ve Qwen3-Next 80B-A3B Instruct API fiyatları nasıl karşılaştırılır?+

Qwen3.5 27B 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, Qwen3-Next 80B-A3B Instruct $0.00 / $0.00 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 Qwen3-Next 80B-A3B Instruct 131.1K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Qwen3-Next 80B-A3B Instruct 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 Qwen3-Next 80B-A3B Instruct

İstediğiniz Modelleri Seçin ve Karşılaştırın

Qwen3.5 27B

Qwen3-Next 80B-A3B Instruct

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

Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

Related reading