Head-to-Head Model Comparison

GLM-5.1 vs GLM-5.2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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

Specialista Codice Open-Weights

GLM-5.1 di Zhipu AI è un modello di IA progettato per lo sviluppo software e la generazione di codice con pesi aperti auto-ospitabili, con finestra di contesto da 200K, velocità di 65 token/s, indice di intelligenza di 89/100 e tariffa di a

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

Modello Base a Pesi Aperti

GLM-5.2 di Zhipu AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 1M, velocità di 60 token/s, indice di intelligenza di 91/100 e tariff

Intelligence Index91 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

67.5%

Differenze Chiave a Colpo d’Occhio

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%

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 200K con capacità massima di output fino a 16K token (SWE-bench Verified 63% · GPQA Diamond 70%)
Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 610 ms
Pesi aperti (280B MoE) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GLM-5.2 · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 32K token (SWE-bench Verified 67.5% · GPQA Diamond 73.5%)
Throughput elevato di 60 token/secondo con latenza al primo token (TTFT) di soli 650 ms
Pesi aperti (350B MoE) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia GLM-5.1 (Ideale per lo sviluppo software e la generazione di codice con pesi aperti auto-ospitabili, sistemi RAG aziendali e integrazioni API scalabili.) sia GLM-5.2 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GLM-5.2 guida nei benchmark di ragionamento e programmazione, GLM-5.1 garantisce una generazione più rapida e GLM-5.1 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GLM-5.1 o GLM-5.2?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GLM-5.2. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GLM-5.1 e GLM-5.2?+

GLM-5.1 costa $0.00 / $0.00 per milione di token di input/output, mentre GLM-5.2 costa $0.00 / $0.00. Nel costo combinato, GLM-5.1 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

GLM-5.1 supporta 200K token di contesto contro 1M token di GLM-5.2, mentre GLM-5.1 offre una velocità di generazione superiore.

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

Cambia e Confronta Qualsiasi Modello

GLM-5.1

GLM-5.2

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GLM-5.1 · Strengths & Key Advantages

Considerations & Trade-offs

GLM-5.2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GLM-5.1 vs GLM-5.2: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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