Head-to-Head Model Comparison

GLM-4.7 vs Kimi K2 Thinking

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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GLM-4.7Profile

Kimi K2 ThinkingProfile

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Popular Matchups:

Zhipu AI

GLM-4.7

Open-Weights-Basismodell

GLM-4.7 von Zhipu AI ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 205K Kontextfenster, 72 Token/s Durchsatz, einem Intelligenz-Index von 84/100 und kostenlosem Open-Weights-Zugang.

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

Open-Weights-Reasoning-Modell

Kimi K2 Thinking von Moonshot AI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 262K Kontextfenster, 55 Token/s Durchsatz, einem Intelligenz-Index von 85/100 und kostenlosem

Intelligence Index85 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

50%

Wichtigste Unterschiede auf einen Blick

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%

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

205K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 52% · GPQA Diamond 66%)
Hohe Generierungsgeschwindigkeit von 72 Token/Sekunde bei nur 540 ms Erstlatenz (TTFT)
Offene Modellgewichte (180B MoE) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Kimi K2 Thinking · Strengths & Key Advantages

262K Kontextfenster und bis zu 32K maximale Output-Token (SWE-bench Verified 50% · GPQA Diamond 70%)
Hohe Generierungsgeschwindigkeit von 55 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
Offene Modellgewichte (1T total (32B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl GLM-4.7 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Kimi K2 Thinking (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Kimi K2 Thinking führt bei komplexem Reasoning und Coding, GLM-4.7 liefert den höheren Token-Durchsatz und GLM-4.7 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GLM-4.7 oder Kimi K2 Thinking?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Kimi K2 Thinking vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GLM-4.7 und Kimi K2 Thinking?+

GLM-4.7 kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Kimi K2 Thinking liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist GLM-4.7 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

GLM-4.7 bietet 204.8K Tokens Kontext gegenüber 262.1K Tokens bei Kimi K2 Thinking. Bei der Generierungsgeschwindigkeit liegt GLM-4.7 vorn.

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

Beliebige Modelle wechseln & vergleichen

GLM-4.7

Kimi K2 Thinking

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GLM-4.7 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K2 Thinking · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GLM-4.7 vs Kimi K2 Thinking: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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