Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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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

Open-Weights-Reasoning-Modell

GLM-4.5 von Zhipu AI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 131K Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 80/100 und kostenlosem Open-Weight

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)

Open-Weights-Basismodell

Qwen3-Next 80B-A3B (Thinking) von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 82/100 und kostenlosem

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

46%

Wichtigste Unterschiede auf einen Blick

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%

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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 Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 42% · GPQA Diamond 61%)
Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 550 ms Erstlatenz (TTFT)
Offene Modellgewichte (130B MoE) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

131K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 46% · GPQA Diamond 68.5%)
Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 540 ms Erstlatenz (TTFT)
Offene Modellgewichte (80B total (3B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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): Schnelle Antworten (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.

Fazit

Sowohl GLM-4.5 (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen3-Next 80B-A3B (Thinking) (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3-Next 80B-A3B (Thinking) führt bei komplexem Reasoning und Coding, Qwen3-Next 80B-A3B (Thinking) liefert den höheren Token-Durchsatz und GLM-4.5 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GLM-4.5 oder Qwen3-Next 80B-A3B (Thinking)?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3-Next 80B-A3B (Thinking) vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GLM-4.5 und Qwen3-Next 80B-A3B (Thinking)?+

GLM-4.5 kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Qwen3-Next 80B-A3B (Thinking) liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist GLM-4.5 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

GLM-4.5 bietet 131.1K Tokens Kontext gegenüber 131.1K Tokens bei Qwen3-Next 80B-A3B (Thinking). Bei der Generierungsgeschwindigkeit liegt Qwen3-Next 80B-A3B (Thinking) vorn.

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)

Beliebige Modelle wechseln & vergleichen

GLM-4.5

Qwen3-Next 80B-A3B (Thinking)

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GLM-4.5 · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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