Head-to-Head Model Comparison

MiniMax-M3 vs Kimi K2.7 Code

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.

MiniMax-M3Profile

Kimi K2.7 CodeProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

MiniMax

MiniMax-M3

AI Multimodal Asli

MiniMax-M3 dari MiniMax adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 524K, kecepatan 70 token/detik, indeks kecerdasan 89/100, serta akses grati

Intelligence Index89 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

524.3K tokens

SWE-bench Verified:

65%

Moonshot AI

Kimi K2.7 Code

Spesialis Kode Open-Weights

Kimi K2.7 Code dari Moonshot AI adalah model AI yang dirancang untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), menghadirkan jendela konteks 262K, kecepatan 75 token/detik, indeks kecerdasan 89/100,

Intelligence Index89 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

62%

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
MiniMax-M3 has a larger context window (524K vs 262K tokens).
Kimi K2.7 Code is faster at 75 tok/s vs 70 tok/s.
MiniMax-M3 leads on SWE-bench coding (65.0% vs 62.0%).

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

Feature	MiniMax-M3	Kimi K2.7 Code
Intelligence Index Composite reasoning & knowledge score (0-100)	89	89
Provider	MiniMax	Moonshot AI
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 text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	32K	32K
Throughput Speed Tokens generated per second	70 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	65%	62%
GPQA Diamond Graduate-level reasoning	72%	73%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

MiniMax-M3

+3.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Kimi K2.7 Code

+7% faster generation

Context

MiniMax-M3

524.3K tokens window

Skor Benchmark Terverifikasi

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

MiniMax-M3Kimi K2.7 Code

SWE-bench Verified (Autonomous Coding)

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

65%vs62%

GPQA Diamond (Doctorate-Level Science & Math)

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

72%vs73%

MMLU-Pro (Multi-task Language Understanding)

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

85%vs85%

LMSYS Chatbot Arena Elo

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

1420ptsvs1415pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs74%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 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

MiniMax-M30.56s

Kimi K2.7 Code0.48s

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

MiniMax-M37.70s

Kimi K2.7 Code7.15s

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)

MiniMax-M3 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Kimi K2.7 Code 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

Jendela konteks 524K dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 65% · GPQA Diamond 72%)
Kecepatan generasi tinggi 70 token/detik dengan latensi token pertama (TTFT) hanya 560 ms
Bobot terbuka (Confidential / 300B 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

Kimi K2.7 Code · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 62% · GPQA Diamond 73%)
Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 480 ms
Bobot terbuka (1.5T total (40B 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	MiniMax-M3	Kimi K2.7 Code
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	28K	26K
Reasoning Tokens / Task Thinking tokens burned per task	10K	5K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

MiniMax-M3 vs Kimi K2.7 Code: Jawaban Cepat (FAQ)

Which model is faster?

Kimi K2.7 Code is faster, generating 75 tokens per second compared with 70 tokens per second.

Which model has lower latency?

Kimi K2.7 Code has lower latency, with a time to first token of 0.48s compared with 0.56s.

Which model has a larger context window?

MiniMax-M3 has a larger context window, supporting 524.3K tokens compared with 262.1K tokens.

Kesimpulan

Baik MiniMax-M3 (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Kimi K2.7 Code (Sangat cocok untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: MiniMax-M3 unggul dalam penalaran dan pemrograman, Kimi K2.7 Code memberikan kecepatan generasi token lebih tinggi, dan MiniMax-M3 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, MiniMax-M3 atau Kimi K2.7 Code?+

Dalam indeks kecerdasan gabungan dan benchmark coding, MiniMax-M3 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API MiniMax-M3 vs Kimi K2.7 Code?+

MiniMax-M3 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Kimi K2.7 Code sebesar $0.00 / $0.00. Secara keseluruhan, MiniMax-M3 lebih hemat biaya.

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

MiniMax-M3 mendukung konteks 524.3K token dibandingkan 262.1K token pada Kimi K2.7 Code, sementara Kimi K2.7 Code 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)

Feature	MiniMax-M3	Kimi K2.7 Code
Official API Model ID (SDK)	minimax-m3	kimi-k2-7-code
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-06	2026-06
Knowledge Cutoff	February 2026	April 2026
Architecture	Multimodal Long-Context Sparse MoE Transformer	Code-Optimized Sparse Mixture-of-Experts Transformer
Parameters	Confidential / 300B MoE	1.5T total (40B active 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)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / 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) · 930 GB VRAM (4-bit Quantized) · 3225 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, vision
Output Modalities	text	text

MiniMax-M3 vs Kimi K2.7 Code

Ganti & Bandingkan Model Apa Saja

MiniMax-M3

Kimi K2.7 Code

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

MiniMax-M3 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K2.7 Code · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

MiniMax-M3 vs Kimi K2.7 Code: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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