Head-to-Head Model Comparison

GLM-4.6V vs MiniMax-M2.1

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-4.6VProfile

MiniMax-M2.1Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.6V

AI Multimodal Asli

GLM-4.6V dari Zhipu AI adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 131K, kecepatan 80 token/detik, indeks kecerdasan 77/100, serta akses gratis

Intelligence Index77 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

MiniMax

MiniMax-M2.1

Model Agen Otonom & Alat

MiniMax-M2.1 dari MiniMax adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 204K, kecepatan 88 token/detik, indeks kecerdasan 80/100, se

Intelligence Index80 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
MiniMax-M2.1 has a larger context window (204K vs 131K tokens).
MiniMax-M2.1 is faster at 88 tok/s vs 80 tok/s.

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

Feature	GLM-4.6V	MiniMax-M2.1
Intelligence Index Composite reasoning & knowledge score (0-100)	77	80
Provider	Zhipu AI	MiniMax
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	204K tokens~306 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	80 tok/s	88 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
GPQA Diamond Graduate-level reasoning	N/A	61%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

MiniMax-M2.1

+10% faster generation

Context

MiniMax-M2.1

204K tokens window

Skor Benchmark Terverifikasi

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

GLM-4.6VMiniMax-M2.1

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs61%

MMLU-Pro (Multi-task Language Understanding)

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

74%vs76.5%

LMSYS Chatbot Arena Elo

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

1280ptsvs1315pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs88 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-4.6V0.51s

MiniMax-M2.10.42s

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

GLM-4.6V6.76s

MiniMax-M2.16.10s

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-4.6V Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

MiniMax-M2.1 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-4.6V · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 74% · Arena Elo 1280)
Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 510 ms
Bobot terbuka (24B VLM) 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

MiniMax-M2.1 · Strengths & Key Advantages

Jendela konteks 204K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 61% · MMLU-Pro 76.5%)
Kecepatan generasi tinggi 88 token/detik dengan latensi token pertama (TTFT) hanya 420 ms
Bobot terbuka (Confidential / 130B 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-4.6V	MiniMax-M2.1
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	14K	16K
Reasoning Tokens / Task Thinking tokens burned per task	3K	4K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.6V	MiniMax-M2.1
Official API Model ID (SDK)	glm-4-6v	minimax-m2-1
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-12
Knowledge Cutoff	October 2025	October 2025
Architecture	High-Resolution Open Vision-Language Transformer	Refined Agentic Mixture-of-Experts Transformer
Parameters	24B VLM	Confidential / 130B 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) · 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) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 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

GLM-4.6V vs MiniMax-M2.1: Jawaban Cepat (FAQ)

Which is more intelligent, GLM-4.6V or MiniMax-M2.1?

MiniMax-M2.1 is more intelligent. It scores 80 compared with 77 on the composite Intelligence Index.

Which model is faster?

MiniMax-M2.1 is faster, generating 88 tokens per second compared with 80 tokens per second.

Which model has lower latency?

MiniMax-M2.1 has lower latency, with a time to first token of 0.42s compared with 0.51s.

Which model has a larger context window?

MiniMax-M2.1 has a larger context window, supporting 204K tokens compared with 131.1K tokens.

Kesimpulan

Baik GLM-4.6V (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun MiniMax-M2.1 (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: MiniMax-M2.1 unggul dalam penalaran dan pemrograman, MiniMax-M2.1 memberikan kecepatan generasi token lebih tinggi, dan GLM-4.6V lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-4.6V atau MiniMax-M2.1?+

Dalam indeks kecerdasan gabungan dan benchmark coding, MiniMax-M2.1 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GLM-4.6V vs MiniMax-M2.1?+

GLM-4.6V mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan MiniMax-M2.1 sebesar $0.00 / $0.00. Secara keseluruhan, GLM-4.6V lebih hemat biaya.

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

GLM-4.6V mendukung konteks 131.1K token dibandingkan 204K token pada MiniMax-M2.1, sementara MiniMax-M2.1 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-4.6V vs MiniMax-M2.1

Ganti & Bandingkan Model Apa Saja

GLM-4.6V

MiniMax-M2.1

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-4.6V · Strengths & Key Advantages

Considerations & Trade-offs

MiniMax-M2.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-4.6V vs MiniMax-M2.1: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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