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Head-to-Head Model Comparison

Kimi K3 vs GLM-5.2

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
Kimi K3 vs GLM-5.2: Benchmarks, Pricing & Speed Comparison

Kimi K3 di Moonshot AI e GLM-5.2 di Zhipu AI sono due modelli di IA di riferimento. Kimi K3 offre una finestra di contesto di 1.05M token a $0.00/$0.00 per milione di token, mentre GLM-5.2 mette a disposizione 1M 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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Moonshot AI

Kimi K3

Ragionamento a Pesi Aperti

Kimi K3 di Moonshot 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 1.0M, velocità di 58 token/s, indice di intelligenza di 93/100 e tariffa di

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

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.
  • 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 →
FeatureKimi K3GLM-5.2
Intelligence Index
Composite reasoning & knowledge score (0-100)
9391
Provider
Moonshot 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
1.05M tokens~1,573 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
131K32K
Throughput Speed
Tokens generated per second
58 tok/s60 tok/s
SWE-bench Verified
Real-world software engineering
74.5%67.5%
GPQA Diamond
Graduate-level reasoning
79%73.5%

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

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
Kimi K30.72s
GLM-5.20.65s
Est. End-to-End Response (500 tokens)TTFT + generation time
Kimi K39.34s
GLM-5.28.98s

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)
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

  • Finestra di contesto da 1.0M con capacità massima di output fino a 131K token (SWE-bench Verified 74.5% · GPQA Diamond 79%)
  • Throughput elevato di 58 token/secondo con latenza al primo token (TTFT) di soli 720 ms
  • Pesi aperti (2.8T total (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.

MetricKimi K3GLM-5.2
Cost per Task
Estimated USD to finish one task
$2.00$0.00
Output Tokens / Task
Tokens generated per task
35K30K
Reasoning Tokens / Task
Thinking tokens burned per task
18K12K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureKimi K3GLM-5.2
Official API Model ID (SDK)kimi-k3glm-5-2
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-072026-06
Knowledge CutoffMarch 2026February 2026
ArchitectureColossus-Scale Multimodal Sparse Mixture-of-ExpertsMassive Million-Token Open MoE Frontier Transformer
Parameters2.8T 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 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 AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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 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 Modalitiestext, visiontext
Output Modalitiestexttext

Kimi K3 vs GLM-5.2: Risposte Rapide (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.

Verdetto

Sia Kimi K3 (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, 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: Kimi K3 guida nei benchmark di ragionamento e programmazione, GLM-5.2 garantisce una generazione più rapida e Kimi K3 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Kimi K3 o GLM-5.2?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Kimi K3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Kimi K3 e GLM-5.2?+

Kimi K3 costa $0.00 / $0.00 per milione di token di input/output, mentre GLM-5.2 costa $0.00 / $0.00. Nel costo combinato, Kimi K3 risulta più economico.

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

Kimi K3 supporta 1.05M token di contesto contro 1M token di GLM-5.2, mentre GLM-5.2 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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 Moonshot AI e Zhipu AI, schede di sistema e benchmark indipendenti (2026).