Head-to-Head Model Comparison

Kimi K3 vs GLM-5.2

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 K3Profile

GLM-5.2Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Moonshot AI

Kimi K3

Penalaran Bobot Terbuka

Kimi K3 dari Moonshot AI adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 1.0M, kecepatan 58 token/detik, indeks kecerdasan 93/100, serta a

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.5%

Zhipu AI

GLM-5.2

Model Dasar Bobot Terbuka

GLM-5.2 dari Zhipu AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 60 token/detik, indeks kecerdasan 91/100, serta akses gratis open-

Intelligence Index91 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

67.5%

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
Kimi K3 has a larger context window (1M vs 1M tokens).
GLM-5.2 is faster at 60 tok/s vs 58 tok/s.
Kimi K3 leads on SWE-bench coding (74.5% vs 67.5%).

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

Feature	Kimi K3	GLM-5.2
Intelligence Index Composite reasoning & knowledge score (0-100)	93	91
Provider	Moonshot AI	Zhipu 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	1.05M tokens~1,573 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	131K	32K
Throughput Speed Tokens generated per second	58 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	74.5%	67.5%
GPQA Diamond Graduate-level reasoning	79%	73.5%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Kimi K3

+7.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GLM-5.2

+3% faster generation

Context

Kimi K3

1.05M tokens window

Skor Benchmark Terverifikasi

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

Kimi K3GLM-5.2

SWE-bench Verified (Autonomous Coding)

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

74.5%vs67.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

79%vs73.5%

MMLU-Pro (Multi-task Language Understanding)

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

88.8%vs87%

LMSYS Chatbot Arena Elo

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

1480ptsvs1440pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

58 tok/s vs60 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 K30.72s

GLM-5.20.65s

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

Kimi K39.34s

GLM-5.28.98s

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 K3 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

GLM-5.2 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 K3 · Strengths & Key Advantages

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 131K token (SWE-bench Verified 74.5% · GPQA Diamond 79%)
Kecepatan generasi tinggi 58 token/detik dengan latensi token pertama (TTFT) hanya 720 ms
Bobot terbuka (2.8T total (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

GLM-5.2 · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 67.5% · GPQA Diamond 73.5%)
Kecepatan generasi tinggi 60 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
Bobot terbuka (350B 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 K3	GLM-5.2
Cost per Task Estimated USD to finish one task	$2.00	$0.00
Output Tokens / Task Tokens generated per task	35K	30K
Reasoning Tokens / Task Thinking tokens burned per task	18K	12K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Kimi K3	GLM-5.2
Official API Model ID (SDK)	kimi-k3	glm-5-2
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-07	2026-06
Knowledge Cutoff	March 2026	February 2026
Architecture	Colossus-Scale Multimodal Sparse Mixture-of-Experts	Massive Million-Token Open MoE Frontier Transformer
Parameters	2.8T total (MoE)	350B 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) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 217 GB VRAM (4-bit Quantized) · 753 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 K3 vs GLM-5.2: Jawaban Cepat (FAQ)

Which is more intelligent, Kimi K3 or GLM-5.2?

Kimi K3 is more intelligent. It scores 93 compared with 91 on the composite Intelligence Index.

Which model is faster?

GLM-5.2 is faster, generating 60 tokens per second compared with 58 tokens per second.

Which model has lower latency?

GLM-5.2 has lower latency, with a time to first token of 0.65s compared with 0.72s.

Which model has a larger context window?

Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1M tokens.

Kesimpulan

Baik Kimi K3 (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GLM-5.2 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Kimi K3 unggul dalam penalaran dan pemrograman, GLM-5.2 memberikan kecepatan generasi token lebih tinggi, dan Kimi K3 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Kimi K3 atau GLM-5.2?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Kimi K3 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Kimi K3 vs GLM-5.2?+

Kimi K3 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan GLM-5.2 sebesar $0.00 / $0.00. Secara keseluruhan, Kimi K3 lebih hemat biaya.

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

Kimi K3 mendukung konteks 1.05M token dibandingkan 1M token pada GLM-5.2, sementara GLM-5.2 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 K3 vs GLM-5.2

Ganti & Bandingkan Model Apa Saja

Kimi K3

GLM-5.2

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Kimi K3 · Strengths & Key Advantages

Considerations & Trade-offs

GLM-5.2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Kimi K3 vs GLM-5.2: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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