Head-to-Head Model Comparison

GLM-5.1 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.

GLM-5.1Profile

GLM-5.2Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-5.1

Spesialis Kode Open-Weights

GLM-5.1 dari Zhipu AI adalah model AI yang dirancang untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), menghadirkan jendela konteks 200K, kecepatan 65 token/detik, indeks kecerdasan 89/100, serta aks

Intelligence Index89 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

63%

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.
GLM-5.2 has a larger context window (1M vs 200K tokens).
GLM-5.1 is faster at 65 tok/s vs 60 tok/s.
GLM-5.2 leads on SWE-bench coding (67.5% vs 63.0%).

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

Feature	GLM-5.1	GLM-5.2
Intelligence Index Composite reasoning & knowledge score (0-100)	89	91
Provider	Zhipu 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	200K tokens~300 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	16K	32K
Throughput Speed Tokens generated per second	65 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	63%	67.5%
GPQA Diamond Graduate-level reasoning	70%	73.5%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

GLM-5.2

+4.5% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GLM-5.1

+8% faster generation

Context

GLM-5.2

1M tokens window

Skor Benchmark Terverifikasi

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

GLM-5.1GLM-5.2

SWE-bench Verified (Autonomous Coding)

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

63%vs67.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

70%vs73.5%

MMLU-Pro (Multi-task Language Understanding)

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

85.5%vs87%

LMSYS Chatbot Arena Elo

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

1410ptsvs1440pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 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

GLM-5.10.61s

GLM-5.20.65s

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

GLM-5.18.30s

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)

GLM-5.1 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

GLM-5.1 · Strengths & Key Advantages

Jendela konteks 200K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 63% · GPQA Diamond 70%)
Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 610 ms
Bobot terbuka (280B 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	GLM-5.1	GLM-5.2
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	26K	30K
Reasoning Tokens / Task Thinking tokens burned per task	9K	12K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-5.1	GLM-5.2
Official API Model ID (SDK)	glm-5-1	glm-5-2
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-06
Knowledge Cutoff	February 2026	February 2026
Architecture	Expanded Code-Centric MoE Frontier Transformer	Massive Million-Token Open MoE Frontier Transformer
Parameters	280B 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	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) · 174 GB VRAM (4-bit Quantized) · 602 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	text
Output Modalities	text	text

GLM-5.1 vs GLM-5.2: Jawaban Cepat (FAQ)

Which is more intelligent, GLM-5.1 or GLM-5.2?

GLM-5.2 is more intelligent. It scores 91 compared with 89 on the composite Intelligence Index.

Which model is faster?

GLM-5.1 is faster, generating 65 tokens per second compared with 60 tokens per second.

Which model has lower latency?

GLM-5.1 has lower latency, with a time to first token of 0.61s compared with 0.65s.

Which model has a larger context window?

GLM-5.2 has a larger context window, supporting 1M tokens compared with 200K tokens.

Kesimpulan

Baik GLM-5.1 (Sangat cocok untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), 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: GLM-5.2 unggul dalam penalaran dan pemrograman, GLM-5.1 memberikan kecepatan generasi token lebih tinggi, dan GLM-5.1 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-5.1 atau GLM-5.2?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GLM-5.2 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GLM-5.1 vs GLM-5.2?+

GLM-5.1 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan GLM-5.2 sebesar $0.00 / $0.00. Secara keseluruhan, GLM-5.1 lebih hemat biaya.

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

GLM-5.1 mendukung konteks 200K token dibandingkan 1M token pada GLM-5.2, sementara GLM-5.1 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-5.1 vs GLM-5.2

Ganti & Bandingkan Model Apa Saja

GLM-5.1

GLM-5.2

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-5.1 · Strengths & Key Advantages

Considerations & Trade-offs

GLM-5.2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-5.1 vs GLM-5.2: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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