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Head-to-Head Model Comparison

MiniMax-M2.1 vs MiniMax-M2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
MiniMax-M2.1 vs MiniMax-M2: Benchmarks, Pricing & Speed Comparison

MiniMax-M2.1 di MiniMax e MiniMax-M2 di MiniMax sono due modelli di IA di riferimento. MiniMax-M2.1 offre una finestra di contesto di 204K token a $0.00/$0.00 per milione di token, mentre MiniMax-M2 mette a disposizione 196K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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MiniMax

MiniMax-M2.1

IA Agentica e Strumenti Autonomi

MiniMax-M2.1 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 204K, velocità di 88 token/s, indice di intelligenza di 80/100 e tariffa di acce

Intelligence Index80 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

N/A

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

Differenze Chiave a Colpo d’Occhio

  • Both models cost the same under this workload — choose on capability, not price.
  • MiniMax-M2.1 has a larger context window (204K vs 196K tokens).
  • MiniMax-M2.1 is faster at 88 tok/s vs 85 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMiniMax-M2.1MiniMax-M2
Intelligence Index
Composite reasoning & knowledge score (0-100)
8078
Provider
MiniMaxMiniMax
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
204K tokens~306 A4 pages of text196K tokens~294 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
88 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
GPQA Diamond
Graduate-level reasoning
61%58%

Chi vince in ogni categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
MiniMax-M2.1
+4% faster generation
Context
MiniMax-M2.1
204K tokens window

Punteggi Benchmark Verificati

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

MiniMax-M2.1MiniMax-M2
GPQA Diamond (Doctorate-Level Science & Math)

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

61%vs58%
MMLU-Pro (Multi-task Language Understanding)

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

76.5%vs74%
LMSYS Chatbot Arena Elo

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

1315ptsvs1290pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

88 tok/s vs85 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-M2.10.42s
MiniMax-M20.44s
Est. End-to-End Response (500 tokens)TTFT + generation time
MiniMax-M2.16.10s
MiniMax-M26.32s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
MiniMax-M2.1 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
MiniMax-M2 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-M2.1 · Strengths & Key Advantages

  • Finestra di contesto da 204K con capacità massima di output fino a 8K token (GPQA Diamond 61% · MMLU-Pro 76.5%)
  • Throughput elevato di 88 token/secondo con latenza al primo token (TTFT) di soli 420 ms
  • Pesi aperti (Confidential / 130B 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

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

Token Use & Task Economics

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

MetricMiniMax-M2.1MiniMax-M2
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
16K15K
Reasoning Tokens / Task
Thinking tokens burned per task
4K3K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureMiniMax-M2.1MiniMax-M2
Official API Model ID (SDK)minimax-m2-1minimax-m2
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-122025-10
Knowledge CutoffOctober 2025August 2025
ArchitectureRefined Agentic Mixture-of-Experts TransformerHigh-Efficiency Open Mixture-of-Experts Transformer
ParametersConfidential / 130B MoEConfidential / 120B MoE
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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) · 74 GB VRAM (4-bit Quantized) · 258 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

MiniMax-M2.1 vs MiniMax-M2: Risposte Rapide (FAQ)

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

MiniMax-M2.1 is more intelligent. It scores 80 compared with 78 on the composite Intelligence Index.

Which model is faster?

MiniMax-M2.1 is faster, generating 88 tokens per second compared with 85 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 0.44s.

Which model has a larger context window?

MiniMax-M2.1 has a larger context window, supporting 204K tokens compared with 196K tokens.

Verdetto

Sia MiniMax-M2.1 (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) sia MiniMax-M2 (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: MiniMax-M2.1 guida nei benchmark di ragionamento e programmazione, MiniMax-M2.1 garantisce una generazione più rapida e MiniMax-M2.1 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, MiniMax-M2.1 o MiniMax-M2?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale MiniMax-M2.1. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra MiniMax-M2.1 e MiniMax-M2?+

MiniMax-M2.1 costa $0.00 / $0.00 per milione di token di input/output, mentre MiniMax-M2 costa $0.00 / $0.00. Nel costo combinato, MiniMax-M2.1 risulta più economico.

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

MiniMax-M2.1 supporta 204K token di contesto contro 196K token di MiniMax-M2, mentre MiniMax-M2.1 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Fonti ufficiali: Documentazione API ufficiale di MiniMax e MiniMax, schede di sistema e benchmark indipendenti (2026).