Head-to-Head Model Comparison

GLM-4.7 vs Kimi K2 Thinking

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-4.7Profile

Kimi K2 ThinkingProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.7

Modello Base a Pesi Aperti

GLM-4.7 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 205K, velocità di 72 token/s, indice di intelligenza di 84/100 e tari

Intelligence Index84 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204.8K tokens

SWE-bench Verified:

52%

Moonshot AI

Kimi K2 Thinking

Ragionamento a Pesi Aperti

Kimi K2 Thinking 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 262K, velocità di 55 token/s, indice di intelligenza di 85/100 e ta

Intelligence Index85 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

50%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Kimi K2 Thinking has a larger context window (262K vs 205K tokens).
GLM-4.7 is faster at 72 tok/s vs 55 tok/s.
GLM-4.7 leads on SWE-bench coding (52.0% vs 50.0%).

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

Feature	GLM-4.7	Kimi K2 Thinking
Intelligence Index Composite reasoning & knowledge score (0-100)	84	85
Provider	Zhipu AI	Moonshot 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	204.8K tokens~307 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	16K	32K
Throughput Speed Tokens generated per second	72 tok/s	55 tok/s
SWE-bench Verified Real-world software engineering	52%	50%
GPQA Diamond Graduate-level reasoning	66%	70%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GLM-4.7

+2.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GLM-4.7

+31% faster generation

Context

Kimi K2 Thinking

262.1K tokens window

Punteggi Benchmark Verificati

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

GLM-4.7Kimi K2 Thinking

SWE-bench Verified (Autonomous Coding)

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

52%vs50%

GPQA Diamond (Doctorate-Level Science & Math)

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

66%vs70%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs82.5%

LMSYS Chatbot Arena Elo

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

1345ptsvs1375pts

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

72 tok/s vs55 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-4.70.54s

Kimi K2 Thinking0.95s

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

GLM-4.77.48s

Kimi K2 Thinking10.04s

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-4.7 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Kimi K2 Thinking 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-4.7 · Strengths & Key Advantages

Finestra di contesto da 205K con capacità massima di output fino a 16K token (SWE-bench Verified 52% · GPQA Diamond 66%)
Throughput elevato di 72 token/secondo con latenza al primo token (TTFT) di soli 540 ms
Pesi aperti (180B 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

Kimi K2 Thinking · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 32K token (SWE-bench Verified 50% · GPQA Diamond 70%)
Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 950 ms
Pesi aperti (1T total (32B 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-4.7	Kimi K2 Thinking
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	20K	28K
Reasoning Tokens / Task Thinking tokens burned per task	7K	16K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.7	Kimi K2 Thinking
Official API Model ID (SDK)	glm-4-7	kimi-k2-thinking
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-11
Knowledge Cutoff	October 2025	September 2025
Architecture	Mature Dense/MoE Hybrid Autoregressive Transformer	Deliberate Reasoning Open Sparse Mixture-of-Experts
Parameters	180B MoE	1T total (32B active 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) · 112 GB VRAM (4-bit Quantized) · 387 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 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-4.7 vs Kimi K2 Thinking: Risposte Rapide (FAQ)

Which is more intelligent, GLM-4.7 or Kimi K2 Thinking?

Kimi K2 Thinking is more intelligent. It scores 85 compared with 84 on the composite Intelligence Index.

Which model is faster?

GLM-4.7 is faster, generating 72 tokens per second compared with 55 tokens per second.

Which model has lower latency?

GLM-4.7 has lower latency, with a time to first token of 0.54s compared with 0.95s.

Which model has a larger context window?

Kimi K2 Thinking has a larger context window, supporting 262.1K tokens compared with 204.8K tokens.

Verdetto

Sia GLM-4.7 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Kimi K2 Thinking (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Kimi K2 Thinking guida nei benchmark di ragionamento e programmazione, GLM-4.7 garantisce una generazione più rapida e GLM-4.7 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GLM-4.7 o Kimi K2 Thinking?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Kimi K2 Thinking. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GLM-4.7 e Kimi K2 Thinking?+

GLM-4.7 costa $0.00 / $0.00 per milione di token di input/output, mentre Kimi K2 Thinking costa $0.00 / $0.00. Nel costo combinato, GLM-4.7 risulta più economico.

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

GLM-4.7 supporta 204.8K token di contesto contro 262.1K token di Kimi K2 Thinking, mentre GLM-4.7 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-4.7 vs Kimi K2 Thinking

Cambia e Confronta Qualsiasi Modello

GLM-4.7

Kimi K2 Thinking

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GLM-4.7 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K2 Thinking · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GLM-4.7 vs Kimi K2 Thinking: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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