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Head-to-Head Model Comparison

MiniMax-M3 vs MiniMax-M2.7

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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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 dari MiniMax dan MiniMax-M2.7 dari MiniMax adalah dua model AI unggulan. MiniMax-M3 menawarkan jendela konteks 524.3K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan MiniMax-M2.7 menyediakan konteks 204K token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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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%

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.
  • 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%

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

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

Simulator Biaya Token Interaktif

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

  • 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

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.

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

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 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: MiniMax-M3 unggul dalam penalaran dan pemrograman, MiniMax-M2.7 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 MiniMax-M2.7?+

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 MiniMax-M2.7?+

MiniMax-M3 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan MiniMax-M2.7 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 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 & 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.

Sumber resmi: Dokumentasi API resmi MiniMax & MiniMax serta evaluasi benchmark independen (2026).