Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 27B by Alibaba and Qwen3-Next 80B-A3B Instruct by Alibaba represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Qwen3.5 27B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Qwen3-Next 80B-A3B Instruct leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen3.5 27B
오픈 웨이트 모델Alibaba의 Qwen3.5 27B 모델은 262K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Qwen3-Next 80B-A3B Instruct
오픈 웨이트 모델Alibaba의 Qwen3-Next 80B-A3B Instruct 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
한눈에 보는 핵심 차이점
- 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.
| 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% |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
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.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3.5 27B · Strengths & Key Advantages
- Alibaba의 첨단 Dense Multimodal Autoregressive Transformer 아키텍처와 최대 262K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages
- Alibaba의 첨단 Standard Instruction-Tuned Sparse Mixture-of-Experts 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
Verdict & Model Selection Recommendation: Choose Qwen3.5 27B if your priority is Dedicated self-hosted API endpoints and vision classification pipelines. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3-Next 80B-A3B Instruct if your workflow prioritizes General conversational agents, customer care, and low-latency API wrappers. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Qwen3.5 27B compare to Qwen3-Next 80B-A3B Instruct in API token pricing and cost per token?+
Both Qwen3.5 27B and Qwen3-Next 80B-A3B Instruct are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Qwen3.5 27B or Qwen3-Next 80B-A3B Instruct?+
Qwen3.5 27B supports a context window of 262K tokens (~197k words), while Qwen3-Next 80B-A3B Instruct supports 131K tokens (~98k words). Therefore, Qwen3.5 27B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3.5 27B or Qwen3-Next 80B-A3B Instruct?+
Qwen3.5 27B scores at the frontier of coding benchmarks, while Qwen3-Next 80B-A3B Instruct scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen3.5 27B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3.5 27B and Qwen3-Next 80B-A3B Instruct?+
Qwen3.5 27B is optimized for low-latency direct generation. In contrast, Qwen3-Next 80B-A3B Instruct focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen3.5 27B and Qwen3-Next 80B-A3B Instruct?+
Qwen3.5 27B generates approximately 90 tokens per second, while Qwen3-Next 80B-A3B Instruct delivers 135 tokens per second. In latency-sensitive production environments, Qwen3-Next 80B-A3B Instruct provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.5 27B and Qwen3-Next 80B-A3B Instruct to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Qwen3-Next 80B-A3B Instruct, and route deep architectural or complex reasoning queries to Qwen3.5 27B. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
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