Head-to-Head Model Comparison

Kimi K2.5 vs MiniMax-M2.7

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.

Kimi K2.5Profile

MiniMax-M2.7Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Moonshot AI

Kimi K2.5

IA Multimodale Nativa

Kimi K2.5 di Moonshot AI 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 262K, velocità di 65 token/s, indice di intelligenza di 86/100 e

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

52%

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.
Kimi K2.5 has a larger context window (262K vs 204K tokens).
MiniMax-M2.7 is faster at 75 tok/s vs 65 tok/s.
MiniMax-M2.7 leads on SWE-bench coding (56.0% vs 52.0%).

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

Feature	Kimi K2.5	MiniMax-M2.7
Intelligence Index Composite reasoning & knowledge score (0-100)	86	85
Provider	Moonshot 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	262.1K tokens~393 A4 pages of text	204K tokens~306 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	65 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	52%	56%
GPQA Diamond Graduate-level reasoning	69.5%	67.5%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

MiniMax-M2.7

+4.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

MiniMax-M2.7

+15% faster generation

Context

Kimi K2.5

262.1K tokens window

Punteggi Benchmark Verificati

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

Kimi K2.5MiniMax-M2.7

SWE-bench Verified (Autonomous Coding)

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

52%vs56%

GPQA Diamond (Doctorate-Level Science & Math)

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

69.5%vs67.5%

MMLU-Pro (Multi-task Language Understanding)

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

82%vs81.5%

LMSYS Chatbot Arena Elo

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

1380ptsvs1375pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 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

Kimi K2.50.62s

MiniMax-M2.70.51s

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

Kimi K2.58.31s

MiniMax-M2.77.18s

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)

Kimi K2.5 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

Kimi K2.5 · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 16K token (SWE-bench Verified 52% · GPQA Diamond 69.5%)
Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 620 ms
Pesi aperti (1.2T total (36B 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.

Metric	Kimi K2.5	MiniMax-M2.7
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	24K	22K
Reasoning Tokens / Task Thinking tokens burned per task	8K	7K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Kimi K2.5	MiniMax-M2.7
Official API Model ID (SDK)	kimi-k2-5	minimax-m2-7
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-01	2026-03
Knowledge Cutoff	November 2025	January 2026
Architecture	Multimodal Open Mixture-of-Experts Transformer	Enterprise Open MoE Foundation Transformer
Parameters	1.2T total (36B active MoE)	Confidential / 220B 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	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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) · 744 GB VRAM (4-bit Quantized) · 2580 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 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

Kimi K2.5 vs MiniMax-M2.7: Risposte Rapide (FAQ)

Which is more intelligent, Kimi K2.5 or MiniMax-M2.7?

Kimi K2.5 is more intelligent. It scores 86 compared with 85 on the composite Intelligence Index.

Which model is faster?

MiniMax-M2.7 is faster, generating 75 tokens per second compared with 65 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 0.62s.

Which model has a larger context window?

Kimi K2.5 has a larger context window, supporting 262.1K tokens compared with 204K tokens.

Verdetto

Sia Kimi K2.5 (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: Kimi K2.5 guida nei benchmark di ragionamento e programmazione, MiniMax-M2.7 garantisce una generazione più rapida e Kimi K2.5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Kimi K2.5 o MiniMax-M2.7?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Kimi K2.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Kimi K2.5 e MiniMax-M2.7?+

Kimi K2.5 costa $0.00 / $0.00 per milione di token di input/output, mentre MiniMax-M2.7 costa $0.00 / $0.00. Nel costo combinato, Kimi K2.5 risulta più economico.

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

Kimi K2.5 supporta 262.1K 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Kimi K2.5 vs MiniMax-M2.7

Cambia e Confronta Qualsiasi Modello

Kimi K2.5

MiniMax-M2.7

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Kimi K2.5 · Strengths & Key Advantages

Considerations & Trade-offs

MiniMax-M2.7 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Kimi K2.5 vs MiniMax-M2.7: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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