GetInfoUs
Head-to-Head Model Comparison

MiniMax-M3 vs MiniMax-M2.7

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
Condividi
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
MiniMax-M3 vs MiniMax-M2.7: Benchmarks, Pricing & Speed Comparison

MiniMax-M3 di MiniMax e MiniMax-M2.7 di MiniMax sono due modelli di IA di riferimento. MiniMax-M3 offre una finestra di contesto di 524.3K token a $0.00/$0.00 per milione di token, mentre MiniMax-M2.7 mette a disposizione 204K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

Cambia e Confronta Qualsiasi Modello

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
MiniMax

MiniMax-M3

IA Multimodale Nativa

MiniMax-M3 di MiniMax è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 524K, velocità di 70 token/s, indice di intelligenza di 89/100 e ta

Intelligence Index89 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

524.3K tokens

SWE-bench Verified:

65%

MiniMax

MiniMax-M2.7

IA Agentica e Strumenti Autonomi

MiniMax-M2.7 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 75 token/s, indice di intelligenza di 85/100 e tariffa di acce

Intelligence Index85 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

56%

Differenze Chiave a Colpo d’Occhio

  • Both models cost the same under this workload — choose on capability, not price.
  • MiniMax-M3 has a larger context window (524K vs 204K tokens).
  • MiniMax-M2.7 is faster at 75 tok/s vs 70 tok/s.
  • MiniMax-M3 leads on SWE-bench coding (65.0% vs 56.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMiniMax-M3MiniMax-M2.7
Intelligence Index
Composite reasoning & knowledge score (0-100)
8985
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
524.3K tokens~786 A4 pages of text204K tokens~306 A4 pages of text
Max Output Tokens
Per single request
32K16K
Throughput Speed
Tokens generated per second
70 tok/s75 tok/s
SWE-bench Verified
Real-world software engineering
65%56%
GPQA Diamond
Graduate-level reasoning
72%67.5%

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
MiniMax-M3
+9.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
MiniMax-M2.7
+7% faster generation
Context
MiniMax-M3
524.3K tokens window

Punteggi Benchmark Verificati

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

MiniMax-M3MiniMax-M2.7
SWE-bench Verified (Autonomous Coding)

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

65%vs56%
GPQA Diamond (Doctorate-Level Science & Math)

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

72%vs67.5%
MMLU-Pro (Multi-task Language Understanding)

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

85%vs81.5%
LMSYS Chatbot Arena Elo

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

1420ptsvs1375pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 tok/s vs75 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-M30.56s
MiniMax-M2.70.51s
Est. End-to-End Response (500 tokens)TTFT + generation time
MiniMax-M37.70s
MiniMax-M2.77.18s

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-M3 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
MiniMax-M2.7 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-M3 · Strengths & Key Advantages

  • Finestra di contesto da 524K con capacità massima di output fino a 32K token (SWE-bench Verified 65% · GPQA Diamond 72%)
  • Throughput elevato di 70 token/secondo con latenza al primo token (TTFT) di soli 560 ms
  • Pesi aperti (Confidential / 300B 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-M2.7 · Strengths & Key Advantages

  • Finestra di contesto da 204K con capacità massima di output fino a 16K token (SWE-bench Verified 56% · GPQA Diamond 67.5%)
  • Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 510 ms
  • Pesi aperti (Confidential / 220B 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-M3MiniMax-M2.7
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
28K22K
Reasoning Tokens / Task
Thinking tokens burned per task
10K7K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureMiniMax-M3MiniMax-M2.7
Official API Model ID (SDK)minimax-m3minimax-m2-7
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-062026-03
Knowledge CutoffFebruary 2026January 2026
ArchitectureMultimodal Long-Context Sparse MoE TransformerEnterprise Open MoE Foundation Transformer
ParametersConfidential / 300B MoEConfidential / 220B 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 AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 186 GB VRAM (4-bit Quantized) · 645 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 136 GB VRAM (4-bit Quantized) · 473 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 Modalitiestext, visiontext
Output Modalitiestexttext

MiniMax-M3 vs MiniMax-M2.7: Risposte Rapide (FAQ)

Which is more intelligent, MiniMax-M3 or MiniMax-M2.7?

MiniMax-M3 is more intelligent. It scores 89 compared with 85 on the composite Intelligence Index.

Which model is faster?

MiniMax-M2.7 is faster, generating 75 tokens per second compared with 70 tokens per second.

Which model has lower latency?

MiniMax-M2.7 has lower latency, with a time to first token of 0.51s compared with 0.56s.

Which model has a larger context window?

MiniMax-M3 has a larger context window, supporting 524.3K tokens compared with 204K tokens.

Verdetto

Sia MiniMax-M3 (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) sia MiniMax-M2.7 (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-M3 guida nei benchmark di ragionamento e programmazione, MiniMax-M2.7 garantisce una generazione più rapida e MiniMax-M3 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, MiniMax-M3 o MiniMax-M2.7?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale MiniMax-M3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra MiniMax-M3 e MiniMax-M2.7?+

MiniMax-M3 costa $0.00 / $0.00 per milione di token di input/output, mentre MiniMax-M2.7 costa $0.00 / $0.00. Nel costo combinato, MiniMax-M3 risulta più economico.

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

MiniMax-M3 supporta 524.3K token di contesto contro 204K token di MiniMax-M2.7, mentre MiniMax-M2.7 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).