Head-to-Head Model Comparison

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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GLM-4.5Profile

Qwen3-Next 80B-A3B (Thinking)Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.5

오픈 웨이트 추론 모델

Zhipu AI의 GLM-4.5 모델은 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론에 최적화된 AI 모델로, 131K 컨텍스트 윈도우, 초당 75 토큰의 생성 속도, 종합 지능 지수 80/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index80 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

42%

Alibaba

Qwen3-Next 80B-A3B (Thinking)

오픈 웨이트 파운데이션 모델

Alibaba의 Qwen3-Next 80B-A3B (Thinking) 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 131K 컨텍스트 윈도우, 초당 95 토큰의 생성 속도, 종합 지능 지수 82/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

46%

한눈에 보는 핵심 차이점

Both models cost the same under this workload — choose on capability, not price.
Qwen3-Next 80B-A3B (Thinking) is faster at 95 tok/s vs 75 tok/s.
Qwen3-Next 80B-A3B (Thinking) leads on SWE-bench coding (46.0% vs 42.0%).

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

Feature	GLM-4.5	Qwen3-Next 80B-A3B (Thinking)
Intelligence Index Composite reasoning & knowledge score (0-100)	80	82
Provider	Zhipu AI	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	131.1K tokens~197 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	75 tok/s	95 tok/s
SWE-bench Verified Real-world software engineering	42%	46%
GPQA Diamond Graduate-level reasoning	61%	68.5%

부문별 우위 모델

At-a-glance decision framework

Coding

Qwen3-Next 80B-A3B (Thinking)

+4.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3-Next 80B-A3B (Thinking)

+27% faster generation

Context

Tied

131.1K tokens window

검증된 벤치마크 점수 비교

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

GLM-4.5Qwen3-Next 80B-A3B (Thinking)

SWE-bench Verified (Autonomous Coding)

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

42%vs46%

GPQA Diamond (Doctorate-Level Science & Math)

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

61%vs68.5%

MMLU-Pro (Multi-task Language Understanding)

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

75%vs80%

LMSYS Chatbot Arena Elo

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

1290ptsvs1345pts

AIME (Advanced Math Competition)

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

N/Avs82%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs95 tok/s

지연 시간(Latency) 및 응답 속도

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

GLM-4.50.55s

Qwen3-Next 80B-A3B (Thinking)0.54s

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

GLM-4.57.22s

Qwen3-Next 80B-A3B (Thinking)5.80s

실시간 API 토큰 비용 시뮬레이터

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)

GLM-4.5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3-Next 80B-A3B (Thinking) 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

GLM-4.5 · Strengths & Key Advantages

131K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 42% · GPQA Diamond 61%)
초당 75 토큰의 빠른 처리 속도와 550ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (130B MoE)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages

131K 컨텍스트 윈도우 및 최대 16K 출력 토큰 생성 지원 (SWE-bench Verified 46% · GPQA Diamond 68.5%)
초당 95 토큰의 빠른 처리 속도와 540ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (80B total (3B MoE))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Token Use & Task Economics

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

Metric	GLM-4.5	Qwen3-Next 80B-A3B (Thinking)
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	16K	20K
Reasoning Tokens / Task Thinking tokens burned per task	6K	12K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.5	Qwen3-Next 80B-A3B (Thinking)
Official API Model ID (SDK)	glm-4-5	qwen3-next-80b-a3b-thinking-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-07	2025-09
Knowledge Cutoff	May 2025	August 2025
Architecture	Hybrid Reasoning Mixture-of-Experts Transformer	Deliberate Reasoning Sparse Mixture-of-Experts
Parameters	130B MoE	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	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 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	text
Output Modalities	text	text

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): 핵심 요약 문답 (FAQ)

Which is more intelligent, GLM-4.5 or Qwen3-Next 80B-A3B (Thinking)?

Qwen3-Next 80B-A3B (Thinking) is more intelligent. It scores 82 compared with 80 on the composite Intelligence Index.

Which model is faster?

Qwen3-Next 80B-A3B (Thinking) is faster, generating 95 tokens per second compared with 75 tokens per second.

Which model has lower latency?

Qwen3-Next 80B-A3B (Thinking) has lower latency, with a time to first token of 0.54s compared with 0.55s.

종합 평가

GLM-4.5(강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Qwen3-Next 80B-A3B (Thinking)(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Qwen3-Next 80B-A3B (Thinking)가 앞서며, 토큰 생성 속도에서는 Qwen3-Next 80B-A3B (Thinking)가 빠르고, 대규모 API 운영 비용 면에서는 GLM-4.5가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 GLM-4.5와 Qwen3-Next 80B-A3B (Thinking) 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Qwen3-Next 80B-A3B (Thinking)가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

GLM-4.5와 Qwen3-Next 80B-A3B (Thinking)의 API 요금 차이는 얼마인가요?+

GLM-4.5는 100만 토큰당 입력 $0.00 / 출력 $0.00이며, Qwen3-Next 80B-A3B (Thinking)는 입력 $0.00 / 출력 $0.00입니다. 혼합 비용 기준으로는 GLM-4.5가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 GLM-4.5가 131.1K 토큰, Qwen3-Next 80B-A3B (Thinking)가 131.1K 토큰를 지원하며, 출력 속도 면에서는 Qwen3-Next 80B-A3B (Thinking)가 더 빠릅니다.

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)

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking)

원하는 AI 모델 선택 및 실시간 비교

GLM-4.5

Qwen3-Next 80B-A3B (Thinking)

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

GLM-4.5 · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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