Head-to-Head Model Comparison

GLM-4.7 vs Kimi K2 Thinking

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

Cambia y Compara Cualquier Modelo

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

Modelo Base de Pesos Abiertos

GLM-4.7 de Zhipu AI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 205K, velocidad de 72 tokens/s, índice de inteligencia de 84/100 y tarifa

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

Razonamiento de Pesos Abiertos

Kimi K2 Thinking de Moonshot AI es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 262K, velocidad de 55 tokens/s, índice de inteligencia de 85

Intelligence Index85 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

50%

Diferencias Clave de un Vistazo

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 →

Característica	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%

¿Quién gana en cada área?

At-a-glance decision framework

Código

GLM-4.7

+2.0% SWE-bench lead

Costo

Tied

Identical monthly runtime cost

Velocidad

GLM-4.7

+31% faster generation

Contexto

Kimi K2 Thinking

262.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Latencia y Tiempo de Respuesta

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

Simulador Interactivo de Costos de Tokens

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

Ventana de contexto de 205K con hasta 16K tokens de salida máxima (SWE-bench Verified 52% · GPQA Diamond 66%)
Rendimiento ágil de 72 tokens/segundo con latencia inicial (TTFT) de solo 540 ms
Pesos abiertos (180B MoE) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Kimi K2 Thinking · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 32K tokens de salida máxima (SWE-bench Verified 50% · GPQA Diamond 70%)
Rendimiento ágil de 55 tokens/segundo con latencia inicial (TTFT) de solo 950 ms
Pesos abiertos (1T total (32B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

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

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	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: Respuestas Rápidas (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.

Veredicto

Tanto GLM-4.7 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Kimi K2 Thinking (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Kimi K2 Thinking lidera en inteligencia general y programación, GLM-4.7 ofrece mayor velocidad de generación de tokens y GLM-4.7 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, GLM-4.7 o Kimi K2 Thinking?+

En las pruebas de inteligencia compuesta y resolución de código, Kimi K2 Thinking obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de GLM-4.7 frente a Kimi K2 Thinking?+

GLM-4.7 cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Kimi K2 Thinking cuesta $0.00 / $0.00. En costo combinado, GLM-4.7 es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

GLM-4.7 cuenta con 204.8K tokens de contexto frente a 262.1K tokens de Kimi K2 Thinking. En velocidad de inferencia, GLM-4.7 ofrece un rendimiento superior.

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 y Compara Cualquier Modelo

GLM-4.7

Kimi K2 Thinking

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

GLM-4.7 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K2 Thinking · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

GLM-4.7 vs Kimi K2 Thinking: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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