Head-to-Head Model Comparison

GLM-4.5 vs GLM-4.5-Air

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GLM-4.5 vs GLM-4.5-Air: Benchmarks, Pricing & Speed Comparison

GLM-4.5 di Zhipu AI e GLM-4.5-Air di Zhipu AI sono due modelli di IA di riferimento. GLM-4.5 offre una finestra di contesto di 131.1K token a $0.00/$0.00 per milione di token, mentre GLM-4.5-Air mette a disposizione 131.1K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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GLM-4.5

Ragionamento a Pesi Aperti

GLM-4.5 di Zhipu AI è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 131K, velocità di 75 token/s, indice di intelligenza di 80/100 e tariffa di acc

Intelligence Index80 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

42%

Zhipu AI

GLM-4.5-Air

Modello Leggero ed Economico

GLM-4.5-Air di Zhipu AI è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 131K, velocità di 120 token/s, indice di intelligenza di 74/100 e tariffa di

Intelligence Index74 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

  • Both models cost the same under this workload — choose on capability, not price.
  • GLM-4.5-Air is faster at 120 tok/s vs 75 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGLM-4.5GLM-4.5-Air
Intelligence Index
Composite reasoning & knowledge score (0-100)
8074
Provider
Zhipu AIZhipu 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
131.1K tokens~197 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
75 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
42%N/A
GPQA Diamond
Graduate-level reasoning
61%52%

Chi vince in ogni categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
GLM-4.5-Air
+60% faster generation
Context
Tied
131.1K tokens window

Punteggi Benchmark Verificati

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

GLM-4.5GLM-4.5-Air
SWE-bench Verified (Autonomous Coding)

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

42%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

61%vs52%
MMLU-Pro (Multi-task Language Understanding)

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

75%vs70%
LMSYS Chatbot Arena Elo

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

1290ptsvs1255pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs120 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-4.50.55s
GLM-4.5-Air0.36s
Est. End-to-End Response (500 tokens)TTFT + generation time
GLM-4.57.22s
GLM-4.5-Air4.53s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
GLM-4.5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
GLM-4.5-Air 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-4.5 · Strengths & Key Advantages

  • Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 42% · GPQA Diamond 61%)
  • Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 550 ms
  • Pesi aperti (130B 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-4.5-Air · Strengths & Key Advantages

  • Finestra di contesto da 131K con capacità massima di output fino a 8K token (GPQA Diamond 52% · MMLU-Pro 70%)
  • Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 360 ms
  • Pesi aperti (30B 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.

MetricGLM-4.5GLM-4.5-Air
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
16K12K
Reasoning Tokens / Task
Thinking tokens burned per task
6K2K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGLM-4.5GLM-4.5-Air
Official API Model ID (SDK)glm-4-5glm-4-5-air
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-072025-07
Knowledge CutoffMay 2025May 2025
ArchitectureHybrid Reasoning Mixture-of-Experts TransformerLightweight Sparse Mixture-of-Experts Transformer
Parameters130B MoE30B MoE
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

GLM-4.5 vs GLM-4.5-Air: Risposte Rapide (FAQ)

Which is more intelligent, GLM-4.5 or GLM-4.5-Air?

GLM-4.5 is more intelligent. It scores 80 compared with 74 on the composite Intelligence Index.

Which model is faster?

GLM-4.5-Air is faster, generating 120 tokens per second compared with 75 tokens per second.

Which model has lower latency?

GLM-4.5-Air has lower latency, with a time to first token of 0.36s compared with 0.55s.

Verdetto

Sia GLM-4.5 (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia GLM-4.5-Air (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GLM-4.5 guida nei benchmark di ragionamento e programmazione, GLM-4.5-Air garantisce una generazione più rapida e GLM-4.5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GLM-4.5 o GLM-4.5-Air?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GLM-4.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GLM-4.5 e GLM-4.5-Air?+

GLM-4.5 costa $0.00 / $0.00 per milione di token di input/output, mentre GLM-4.5-Air costa $0.00 / $0.00. Nel costo combinato, GLM-4.5 risulta più economico.

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

GLM-4.5 supporta 131.1K token di contesto contro 131.1K token di GLM-4.5-Air, mentre GLM-4.5-Air 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.

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Fonti ufficiali: Documentazione API ufficiale di Zhipu AI e Zhipu AI, schede di sistema e benchmark indipendenti (2026).