Head-to-Head Model Comparison

GLM-4.6 vs Qwen3-Next 80B-A3B (Thinking)

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.

GLM-4.6Profile

Qwen3-Next 80B-A3B (Thinking)Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.6

Model Dasar Bobot Terbuka

GLM-4.6 dari Zhipu AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 205K, kecepatan 70 token/detik, indeks kecerdasan 82/100, serta akses gratis ope

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204.8K tokens

SWE-bench Verified:

48%

Alibaba

Qwen3-Next 80B-A3B (Thinking)

Model Dasar Bobot Terbuka

Qwen3-Next 80B-A3B (Thinking) dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 95 token/detik, indeks kecerdasan 82/100, s

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

46%

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
GLM-4.6 has a larger context window (205K vs 131K tokens).
Qwen3-Next 80B-A3B (Thinking) is faster at 95 tok/s vs 70 tok/s.
GLM-4.6 leads on SWE-bench coding (48.0% vs 46.0%).

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

Feature	GLM-4.6	Qwen3-Next 80B-A3B (Thinking)
Intelligence Index Composite reasoning & knowledge score (0-100)	82	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	204.8K tokens~307 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	70 tok/s	95 tok/s
SWE-bench Verified Real-world software engineering	48%	46%
GPQA Diamond Graduate-level reasoning	63.5%	68.5%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

GLM-4.6

+2.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3-Next 80B-A3B (Thinking)

+36% faster generation

Context

GLM-4.6

204.8K tokens window

Skor Benchmark Terverifikasi

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

GLM-4.6Qwen3-Next 80B-A3B (Thinking)

SWE-bench Verified (Autonomous Coding)

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

48%vs46%

GPQA Diamond (Doctorate-Level Science & Math)

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

63.5%vs68.5%

MMLU-Pro (Multi-task Language Understanding)

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

78%vs80%

LMSYS Chatbot Arena Elo

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

1320ptsvs1345pts

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.

70 tok/s vs95 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

GLM-4.60.56s

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

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

GLM-4.67.70s

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

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)

GLM-4.6 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-4.6 · Strengths & Key Advantages

Jendela konteks 205K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 48% · GPQA Diamond 63.5%)
Kecepatan generasi tinggi 70 token/detik dengan latensi token pertama (TTFT) hanya 560 ms
Bobot terbuka (150B 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

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

Jendela konteks 131K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 46% · GPQA Diamond 68.5%)
Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 540 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	GLM-4.6	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	18K	20K
Reasoning Tokens / Task Thinking tokens burned per task	6K	12K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.6	Qwen3-Next 80B-A3B (Thinking)
Official API Model ID (SDK)	glm-4-6	qwen3-next-80b-a3b-thinking-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-09	2025-09
Knowledge Cutoff	July 2025	August 2025
Architecture	Advanced Mixture-of-Experts Enterprise Transformer	Deliberate Reasoning Sparse Mixture-of-Experts
Parameters	150B 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) · 93 GB VRAM (4-bit Quantized) · 323 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-4.6 vs Qwen3-Next 80B-A3B (Thinking): Jawaban Cepat (FAQ)

Which model is faster?

Qwen3-Next 80B-A3B (Thinking) is faster, generating 95 tokens per second compared with 70 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 0.56s.

Which model has a larger context window?

GLM-4.6 has a larger context window, supporting 204.8K tokens compared with 131.1K tokens.

Kesimpulan

Baik GLM-4.6 (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 (Thinking) (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GLM-4.6 unggul dalam penalaran dan pemrograman, Qwen3-Next 80B-A3B (Thinking) memberikan kecepatan generasi token lebih tinggi, dan GLM-4.6 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-4.6 atau Qwen3-Next 80B-A3B (Thinking)?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GLM-4.6 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GLM-4.6 vs Qwen3-Next 80B-A3B (Thinking)?+

GLM-4.6 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen3-Next 80B-A3B (Thinking) sebesar $0.00 / $0.00. Secara keseluruhan, GLM-4.6 lebih hemat biaya.

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

GLM-4.6 mendukung konteks 204.8K token dibandingkan 131.1K token pada Qwen3-Next 80B-A3B (Thinking), sementara Qwen3-Next 80B-A3B (Thinking) 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)

GLM-4.6 vs Qwen3-Next 80B-A3B (Thinking)

Ganti & Bandingkan Model Apa Saja

GLM-4.6

Qwen3-Next 80B-A3B (Thinking)

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-4.6 · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-4.6 vs Qwen3-Next 80B-A3B (Thinking): Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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