Head-to-Head Model Comparison

Kimi K2.5 vs MiniMax-M2.7

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

AI Multimodal Asli

Kimi K2.5 dari Moonshot AI adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 262K, kecepatan 65 token/detik, indeks kecerdasan 86/100, serta akses gr

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

Model Agen Otonom & Alat

MiniMax-M2.7 dari MiniMax adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 204K, kecepatan 75 token/detik, indeks kecerdasan 85/100, se

Intelligence Index85 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

56%

Perbedaan Utama Sekilas

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%

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 52% · GPQA Diamond 69.5%)
Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 620 ms
Bobot terbuka (1.2T total (36B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

MiniMax-M2.7 · Strengths & Key Advantages

Jendela konteks 204K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 56% · GPQA Diamond 67.5%)
Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 510 ms
Bobot terbuka (Confidential / 220B MoE) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Kimi K2.5 (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun MiniMax-M2.7 (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Kimi K2.5 unggul dalam penalaran dan pemrograman, MiniMax-M2.7 memberikan kecepatan generasi token lebih tinggi, dan Kimi K2.5 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Kimi K2.5 atau MiniMax-M2.7?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Kimi K2.5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Kimi K2.5 vs MiniMax-M2.7?+

Kimi K2.5 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan MiniMax-M2.7 sebesar $0.00 / $0.00. Secara keseluruhan, Kimi K2.5 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Kimi K2.5 mendukung konteks 262.1K token dibandingkan 204K token pada MiniMax-M2.7, sementara MiniMax-M2.7 unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Kimi K2.5

MiniMax-M2.7

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Kimi K2.5 · Strengths & Key Advantages

Considerations & Trade-offs

MiniMax-M2.7 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Kimi K2.5 vs MiniMax-M2.7: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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