Head-to-Head Model Comparison

MiniMax-M2 vs GLM-4.6

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

MiniMax-M2Profile

GLM-4.6Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

MiniMax

MiniMax-M2

IA Agentica e Strumenti Autonomi

MiniMax-M2 di MiniMax è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 196K, velocità di 85 token/s, indice di intelligenza di 78/100 e tariffa di access

Intelligence Index78 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

196K tokens

SWE-bench Verified:

N/A

Zhipu AI

GLM-4.6

Modello Base a Pesi Aperti

GLM-4.6 di Zhipu AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 205K, velocità di 70 token/s, indice di intelligenza di 82/100 e tari

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204.8K tokens

SWE-bench Verified:

48%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
GLM-4.6 has a larger context window (205K vs 196K tokens).
MiniMax-M2 is faster at 85 tok/s vs 70 tok/s.

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

Feature	MiniMax-M2	GLM-4.6
Intelligence Index Composite reasoning & knowledge score (0-100)	78	82
Provider	MiniMax	Zhipu AI
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	196K tokens~294 A4 pages of text	204.8K tokens~307 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	85 tok/s	70 tok/s
SWE-bench Verified Real-world software engineering	N/A	48%
GPQA Diamond Graduate-level reasoning	58%	63.5%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

MiniMax-M2

+21% faster generation

Context

GLM-4.6

204.8K tokens window

Punteggi Benchmark Verificati

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

MiniMax-M2GLM-4.6

SWE-bench Verified (Autonomous Coding)

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

N/Avs48%

GPQA Diamond (Doctorate-Level Science & Math)

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

58%vs63.5%

MMLU-Pro (Multi-task Language Understanding)

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

74%vs78%

LMSYS Chatbot Arena Elo

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

1290ptsvs1320pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs70 tok/s

Latenza e Tempo di Risposta

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

MiniMax-M20.44s

GLM-4.60.56s

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

MiniMax-M26.32s

GLM-4.67.70s

Simulatore Interattivo dei Costi Token

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)

MiniMax-M2 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

GLM-4.6 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

MiniMax-M2 · Strengths & Key Advantages

Finestra di contesto da 196K con capacità massima di output fino a 8K token (GPQA Diamond 58% · MMLU-Pro 74%)
Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 440 ms
Pesi aperti (Confidential / 120B MoE) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GLM-4.6 · Strengths & Key Advantages

Finestra di contesto da 205K con capacità massima di output fino a 8K token (SWE-bench Verified 48% · GPQA Diamond 63.5%)
Throughput elevato di 70 token/secondo con latenza al primo token (TTFT) di soli 560 ms
Pesi aperti (150B MoE) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

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

Metric	MiniMax-M2	GLM-4.6
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	15K	18K
Reasoning Tokens / Task Thinking tokens burned per task	3K	6K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	MiniMax-M2	GLM-4.6
Official API Model ID (SDK)	minimax-m2	glm-4-6
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-10	2025-09
Knowledge Cutoff	August 2025	July 2025
Architecture	High-Efficiency Open Mixture-of-Experts Transformer	Advanced Mixture-of-Experts Enterprise Transformer
Parameters	Confidential / 120B MoE	150B 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) · 74 GB VRAM (4-bit Quantized) · 258 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 93 GB VRAM (4-bit Quantized) · 323 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

MiniMax-M2 vs GLM-4.6: Risposte Rapide (FAQ)

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

GLM-4.6 is more intelligent. It scores 82 compared with 78 on the composite Intelligence Index.

Which model is faster?

MiniMax-M2 is faster, generating 85 tokens per second compared with 70 tokens per second.

Which model has lower latency?

MiniMax-M2 has lower latency, with a time to first token of 0.44s 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 196K tokens.

Verdetto

Sia MiniMax-M2 (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) sia GLM-4.6 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GLM-4.6 guida nei benchmark di ragionamento e programmazione, MiniMax-M2 garantisce una generazione più rapida e MiniMax-M2 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, MiniMax-M2 o GLM-4.6?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GLM-4.6. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra MiniMax-M2 e GLM-4.6?+

MiniMax-M2 costa $0.00 / $0.00 per milione di token di input/output, mentre GLM-4.6 costa $0.00 / $0.00. Nel costo combinato, MiniMax-M2 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

MiniMax-M2 supporta 196K token di contesto contro 204.8K token di GLM-4.6, mentre MiniMax-M2 offre una velocità di generazione superiore.

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)

MiniMax-M2 vs GLM-4.6

Cambia e Confronta Qualsiasi Modello

MiniMax-M2

GLM-4.6

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

MiniMax-M2 · Strengths & Key Advantages

Considerations & Trade-offs

GLM-4.6 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

MiniMax-M2 vs GLM-4.6: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

Related reading