Head-to-Head Model Comparison

GLM-4.7 vs Kimi K2 Thinking

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-4.7Profile

Kimi K2 ThinkingProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.7

Model Dasar Bobot Terbuka

GLM-4.7 dari Zhipu AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 205K, kecepatan 72 token/detik, indeks kecerdasan 84/100, serta akses gratis ope

Intelligence Index84 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204.8K tokens

SWE-bench Verified:

52%

Moonshot AI

Kimi K2 Thinking

Penalaran Bobot Terbuka

Kimi K2 Thinking dari Moonshot AI adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 262K, kecepatan 55 token/detik, indeks kecerdasan 85/100

Intelligence Index85 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

50%

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
Kimi K2 Thinking has a larger context window (262K vs 205K tokens).
GLM-4.7 is faster at 72 tok/s vs 55 tok/s.
GLM-4.7 leads on SWE-bench coding (52.0% vs 50.0%).

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

Feature	GLM-4.7	Kimi K2 Thinking
Intelligence Index Composite reasoning & knowledge score (0-100)	84	85
Provider	Zhipu AI	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	204.8K tokens~307 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	16K	32K
Throughput Speed Tokens generated per second	72 tok/s	55 tok/s
SWE-bench Verified Real-world software engineering	52%	50%
GPQA Diamond Graduate-level reasoning	66%	70%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

GLM-4.7

+2.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GLM-4.7

+31% faster generation

Context

Kimi K2 Thinking

262.1K tokens window

Skor Benchmark Terverifikasi

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

GLM-4.7Kimi K2 Thinking

SWE-bench Verified (Autonomous Coding)

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

52%vs50%

GPQA Diamond (Doctorate-Level Science & Math)

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

66%vs70%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs82.5%

LMSYS Chatbot Arena Elo

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

1345ptsvs1375pts

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

72 tok/s vs55 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-4.70.54s

Kimi K2 Thinking0.95s

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

GLM-4.77.48s

Kimi K2 Thinking10.04s

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 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Kimi K2 Thinking 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

GLM-4.7 · Strengths & Key Advantages

Jendela konteks 205K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 52% · GPQA Diamond 66%)
Kecepatan generasi tinggi 72 token/detik dengan latensi token pertama (TTFT) hanya 540 ms
Bobot terbuka (180B 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 Thinking · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 50% · GPQA Diamond 70%)
Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 950 ms
Bobot terbuka (1T total (32B 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	Kimi K2 Thinking
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	20K	28K
Reasoning Tokens / Task Thinking tokens burned per task	7K	16K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.7	Kimi K2 Thinking
Official API Model ID (SDK)	glm-4-7	kimi-k2-thinking
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-11
Knowledge Cutoff	October 2025	September 2025
Architecture	Mature Dense/MoE Hybrid Autoregressive Transformer	Deliberate Reasoning Open Sparse Mixture-of-Experts
Parameters	180B MoE	1T total (32B 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	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) · 112 GB VRAM (4-bit Quantized) · 387 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 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 vs Kimi K2 Thinking: Jawaban Cepat (FAQ)

Which is more intelligent, GLM-4.7 or Kimi K2 Thinking?

Kimi K2 Thinking is more intelligent. It scores 85 compared with 84 on the composite Intelligence Index.

Which model is faster?

GLM-4.7 is faster, generating 72 tokens per second compared with 55 tokens per second.

Which model has lower latency?

GLM-4.7 has lower latency, with a time to first token of 0.54s compared with 0.95s.

Which model has a larger context window?

Kimi K2 Thinking has a larger context window, supporting 262.1K tokens compared with 204.8K tokens.

Kesimpulan

Baik GLM-4.7 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Kimi K2 Thinking (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Kimi K2 Thinking unggul dalam penalaran dan pemrograman, GLM-4.7 memberikan kecepatan generasi token lebih tinggi, dan GLM-4.7 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-4.7 atau Kimi K2 Thinking?+

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

Berapa perbandingan harga API GLM-4.7 vs Kimi K2 Thinking?+

GLM-4.7 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Kimi K2 Thinking sebesar $0.00 / $0.00. Secara keseluruhan, GLM-4.7 lebih hemat biaya.

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

GLM-4.7 mendukung konteks 204.8K token dibandingkan 262.1K token pada Kimi K2 Thinking, sementara GLM-4.7 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)

GLM-4.7 vs Kimi K2 Thinking

Ganti & Bandingkan Model Apa Saja

GLM-4.7

Kimi K2 Thinking

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-4.7 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K2 Thinking · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-4.7 vs Kimi K2 Thinking: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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