Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

Model Dasar Bobot Terbuka

Qwen3.5 27B dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 90 token/detik, indeks kecerdasan 75/100, serta akses gratis

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

Model Dasar Bobot Terbuka

Qwen3-Next 80B-A3B Instruct dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 135 token/detik, indeks kecerdasan 78/100, se

Intelligence Index78 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

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%

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 56.5% · MMLU-Pro 74%)
Kecepatan generasi tinggi 90 token/detik dengan latensi token pertama (TTFT) hanya 410 ms
Bobot terbuka (27B) 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

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

Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 60% · MMLU-Pro 78%)
Kecepatan generasi tinggi 135 token/detik dengan latensi token pertama (TTFT) hanya 310 ms
Bobot terbuka (80B total (3B 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

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

Spesifikasi Teknis

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: Jawaban Cepat (FAQ)

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.

Kesimpulan

Baik Qwen3.5 27B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3-Next 80B-A3B Instruct (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-Next 80B-A3B Instruct unggul dalam penalaran dan pemrograman, Qwen3-Next 80B-A3B Instruct memberikan kecepatan generasi token lebih tinggi, dan Qwen3.5 27B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen3.5 27B atau Qwen3-Next 80B-A3B Instruct?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3-Next 80B-A3B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct?+

Qwen3.5 27B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen3-Next 80B-A3B Instruct sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3.5 27B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Qwen3.5 27B mendukung konteks 262.1K token dibandingkan 131.1K token pada Qwen3-Next 80B-A3B Instruct, sementara Qwen3-Next 80B-A3B Instruct unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Qwen3.5 27B

Qwen3-Next 80B-A3B Instruct

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Qwen3.5 27B · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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