Head-to-Head Model Comparison

GLM-4.7-Flash vs MiniMax-M2.5

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.

GLM-4.7-FlashProfile

MiniMax-M2.5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.7-Flash

Model Ringan & Hemat Biaya

GLM-4.7-Flash dari Zhipu AI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 200K, kecepatan 160 token/detik, indeks kecerdasan 74/100, serta tarif

Intelligence Index74 / 100

Input Token:

$0.01 / 1M

Output Token:

$0.03 / 1M

Context Window:

200K tokens

SWE-bench Verified:

N/A

MiniMax

MiniMax-M2.5

Spesialis Kode Open-Weights

MiniMax-M2.5 dari MiniMax adalah model AI yang dirancang untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), menghadirkan jendela konteks 204K, kecepatan 80 token/detik, indeks kecerdasan 82/100, serta

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204K tokens

SWE-bench Verified:

48%

Perbedaan Utama Sekilas

MiniMax-M2.5 is 1.0x cheaper for API usage (input + output tokens combined).
MiniMax-M2.5 has a larger context window (204K vs 200K tokens).
GLM-4.7-Flash is faster at 160 tok/s vs 80 tok/s.

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

Feature	GLM-4.7-Flash	MiniMax-M2.5
Intelligence Index Composite reasoning & knowledge score (0-100)	74	82
Provider	Zhipu AI	MiniMax
Input Price Cost per million input tokens	$0.01 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.03 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.01 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	200K tokens~300 A4 pages of text	204K tokens~306 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	160 tok/s	80 tok/s
SWE-bench Verified Real-world software engineering	N/A	48%
GPQA Diamond Graduate-level reasoning	N/A	64%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

MiniMax-M2.5

1.0x cheaper monthly runtime

Speed

GLM-4.7-Flash

+100% faster generation

Context

MiniMax-M2.5

204K tokens window

Skor Benchmark Terverifikasi

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

GLM-4.7-FlashMiniMax-M2.5

SWE-bench Verified (Autonomous Coding)

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

N/Avs48%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs64%

MMLU-Pro (Multi-task Language Understanding)

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

72%vs78.5%

LMSYS Chatbot Arena Elo

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

1270ptsvs1340pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs80 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

GLM-4.7-Flash0.24s

MiniMax-M2.50.47s

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

GLM-4.7-Flash3.37s

MiniMax-M2.56.72s

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)

GLM-4.7-Flash Monthly API Bill

$0.05 / month

$0.015/1M in · $0.03/1M out

MiniMax-M2.5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

MiniMax-M2.5 saves approximately $0.05 / month (100%) under this workload.1.0x Savings

GLM-4.7-Flash · Strengths & Key Advantages

Jendela konteks 200K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 72% · Arena Elo 1270)
Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 240 ms
Bobot terbuka 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-M2.5 · Strengths & Key Advantages

Jendela konteks 204K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 48% · GPQA Diamond 64%)
Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 470 ms
Bobot terbuka (Confidential / 160B 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	GLM-4.7-Flash	MiniMax-M2.5
Cost per Task Estimated USD to finish one task	$0.01	$0.00
Output Tokens / Task Tokens generated per task	12K	18K
Reasoning Tokens / Task Thinking tokens burned per task	0K	6K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.7-Flash	MiniMax-M2.5
Official API Model ID (SDK)	glm-4-7-flash	minimax-m2-5
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-01	2026-02
Knowledge Cutoff	November 2025	December 2025
Architecture	Optimized High-Speed Lightweight Transformer	Coding-Centric Sparse MoE Transformer
Parameters	Confidential	Confidential / 160B 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)	Flat rate ($0.015/1M in) across full 200K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.002/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / 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 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)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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 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	text
Output Modalities	text	text

GLM-4.7-Flash vs MiniMax-M2.5: Jawaban Cepat (FAQ)

Which is more intelligent, GLM-4.7-Flash or MiniMax-M2.5?

MiniMax-M2.5 is more intelligent. It scores 82 compared with 74 on the composite Intelligence Index.

Which model is faster?

GLM-4.7-Flash is faster, generating 160 tokens per second compared with 80 tokens per second.

Which model has lower latency?

GLM-4.7-Flash has lower latency, with a time to first token of 0.24s compared with 0.47s.

Which model has a larger context window?

MiniMax-M2.5 has a larger context window, supporting 204K tokens compared with 200K tokens.

Kesimpulan

Baik GLM-4.7-Flash (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun MiniMax-M2.5 (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-M2.5 unggul dalam penalaran dan pemrograman, GLM-4.7-Flash memberikan kecepatan generasi token lebih tinggi, dan MiniMax-M2.5 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-4.7-Flash atau MiniMax-M2.5?+

Dalam indeks kecerdasan gabungan dan benchmark coding, MiniMax-M2.5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GLM-4.7-Flash vs MiniMax-M2.5?+

GLM-4.7-Flash mengenakan biaya $0.01 / $0.03 per 1M token input/output, sedangkan MiniMax-M2.5 sebesar $0.00 / $0.00. Secara keseluruhan, MiniMax-M2.5 lebih hemat biaya.

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

GLM-4.7-Flash mendukung konteks 200K token dibandingkan 204K token pada MiniMax-M2.5, sementara GLM-4.7-Flash 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)

GLM-4.7-Flash vs MiniMax-M2.5

Ganti & Bandingkan Model Apa Saja

GLM-4.7-Flash

MiniMax-M2.5

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-4.7-Flash · Strengths & Key Advantages

Considerations & Trade-offs

MiniMax-M2.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-4.7-Flash vs MiniMax-M2.5: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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