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Head-to-Head Model Comparison

MiniMax-M2.7 vs MiniMax-M2.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
MiniMax-M2.7 vs MiniMax-M2.5: Benchmarks, Pricing & Speed Comparison

MiniMax-M2.7 da MiniMax e MiniMax-M2.5 da MiniMax são dois modelos de IA de destaque. O MiniMax-M2.7 oferece uma janela de contexto de 204K tokens a $0.00/$0.00 por milhão de tokens, enquanto o MiniMax-M2.5 oferece 204K tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.

Comparação Direta (VS)

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MiniMax

MiniMax-M2.7

IA Agêntica e Uso de Ferramentas

O MiniMax-M2.7 da MiniMax é um modelo de IA projetado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, oferecendo janela de contexto de 204K, velocidade de 75 tokens/s, índice de inteligência de 85/100 e tari

Intelligence Index85 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

56%

MiniMax

MiniMax-M2.5

Especialista Aberto em Código

O MiniMax-M2.5 da MiniMax é um modelo de IA projetado para desenvolvimento de software e geração de código com pesos abertos auto-hospedáveis, oferecendo janela de contexto de 204K, velocidade de 80 tokens/s, índice de inteligência de 82/10

Intelligence Index82 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

48%

Principais Diferenças em Resumo

  • Both models cost the same under this workload — choose on capability, not price.
  • MiniMax-M2.5 is faster at 80 tok/s vs 75 tok/s.
  • MiniMax-M2.7 leads on SWE-bench coding (56.0% vs 48.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMiniMax-M2.7MiniMax-M2.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
8582
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 text204K tokens~306 A4 pages of text
Max Output Tokens
Per single request
16K16K
Throughput Speed
Tokens generated per second
75 tok/s80 tok/s
SWE-bench Verified
Real-world software engineering
56%48%
GPQA Diamond
Graduate-level reasoning
67.5%64%

Quem vence em cada categoria?

At-a-glance decision framework
Coding
MiniMax-M2.7
+8.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
MiniMax-M2.5
+7% faster generation
Context
Tied
204K tokens window

Pontuações de Benchmarks Verificadas

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

MiniMax-M2.7MiniMax-M2.5
SWE-bench Verified (Autonomous Coding)

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

56%vs48%
GPQA Diamond (Doctorate-Level Science & Math)

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

67.5%vs64%
MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs78.5%
LMSYS Chatbot Arena Elo

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

1375ptsvs1340pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs80 tok/s

Latência e Tempo de Resposta

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-M2.70.51s
MiniMax-M2.50.47s
Est. End-to-End Response (500 tokens)TTFT + generation time
MiniMax-M2.77.18s
MiniMax-M2.56.72s

Simulador Interativo de Custo de Tokens

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-M2.7 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
MiniMax-M2.5 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-M2.7 · Strengths & Key Advantages

  • Janela de contexto de 204K com limite máximo de saída de 16K tokens (SWE-bench Verified 56% · GPQA Diamond 67.5%)
  • Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 510 ms
  • Pesos abertos (Confidential / 220B MoE) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
  • Requer servidores GPU com memória VRAM adequada para execução local em precisão total
  • Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

MiniMax-M2.5 · Strengths & Key Advantages

  • Janela de contexto de 204K com limite máximo de saída de 16K tokens (SWE-bench Verified 48% · GPQA Diamond 64%)
  • Taxa de transferência rápida de 80 tokens/segundo com latência inicial (TTFT) de 470 ms
  • Pesos abertos (Confidential / 160B MoE) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
  • Requer servidores GPU com memória VRAM adequada para execução local em precisão total
  • Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

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

MetricMiniMax-M2.7MiniMax-M2.5
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
22K18K
Reasoning Tokens / Task
Thinking tokens burned per task
7K6K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

FeatureMiniMax-M2.7MiniMax-M2.5
Official API Model ID (SDK)minimax-m2-7minimax-m2-5
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-032026-02
Knowledge CutoffJanuary 2026December 2025
ArchitectureEnterprise Open MoE Foundation TransformerCoding-Centric Sparse MoE Transformer
ParametersConfidential / 220B MoEConfidential / 160B 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) · 136 GB VRAM (4-bit Quantized) · 473 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 99 GB VRAM (4-bit Quantized) · 344 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-M2.7 vs MiniMax-M2.5: Respostas Rápidas (FAQ)

Which is more intelligent, MiniMax-M2.7 or MiniMax-M2.5?

MiniMax-M2.7 is more intelligent. It scores 85 compared with 82 on the composite Intelligence Index.

Which model is faster?

MiniMax-M2.5 is faster, generating 80 tokens per second compared with 75 tokens per second.

Which model has lower latency?

MiniMax-M2.5 has lower latency, with a time to first token of 0.47s compared with 0.51s.

Veredito

Tanto MiniMax-M2.7 (Indicado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, arquiteturas RAG corporativas e integração escalável via API.) quanto MiniMax-M2.5 (Indicado para desenvolvimento de software e geração de código com pesos abertos auto-hospedáveis, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: MiniMax-M2.7 lidera em inteligência geral e programação, MiniMax-M2.5 oferece geração mais rápida e MiniMax-M2.7 apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, MiniMax-M2.7 ou MiniMax-M2.5?+

Nas avaliações de inteligência e código, MiniMax-M2.7 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, MiniMax-M2.7 ou MiniMax-M2.5?+

O MiniMax-M2.7 custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o MiniMax-M2.5 custa $0.00 / $0.00. No custo combinado, MiniMax-M2.7 é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O MiniMax-M2.7 suporta 204K tokens de contexto contra 204K tokens do MiniMax-M2.5, enquanto MiniMax-M2.5 entrega maior velocidade de geração.

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.

Fontes oficiais: Documentação oficial de API da MiniMax e MiniMax e benchmarks independentes (2026).