Head-to-Head Model Comparison

Llama 5 vs Kimi K3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

Beliebige Modelle wechseln & vergleichen

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Llama 5Profile

Kimi K3Profile

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5-Model Matrix Workbench

Popular Matchups:

Llama 5

Open-Weights-Basismodell

Llama 5 von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 5M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und kostenlosem Open-Weights-Zugang.

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Moonshot AI

Kimi K3

Open-Weights-Reasoning-Modell

Kimi K3 von Moonshot AI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 1.0M Kontextfenster, 58 Token/s Durchsatz, einem Intelligenz-Index von 93/100 und kostenlosem Open-Wei

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.5%

Wichtigste Unterschiede auf einen Blick

Both models cost the same under this workload — choose on capability, not price.
Llama 5 has a larger context window (5M vs 1M tokens).
Llama 5 is faster at 65 tok/s vs 58 tok/s.
Kimi K3 leads on SWE-bench coding (74.5% vs 74.0%).

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

Feature	Llama 5	Kimi K3
Intelligence Index Composite reasoning & knowledge score (0-100)	94	93
Provider	Meta	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	5M tokens~7,500 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	128K	131K
Throughput Speed Tokens generated per second	65 tok/s	58 tok/s
SWE-bench Verified Real-world software engineering	74%	74.5%
GPQA Diamond Graduate-level reasoning	84%	79%

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Kimi K3

+0.5% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Llama 5

+12% faster generation

Context

Llama 5

5M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Llama 5Kimi K3

SWE-bench Verified (Autonomous Coding)

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

74%vs74.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

84%vs79%

MMLU-Pro (Multi-task Language Understanding)

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

87%vs88.8%

LMSYS Chatbot Arena Elo

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

1485ptsvs1480pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vsN/A

AIME (Advanced Math Competition)

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

88%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs58 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

Llama 50.85s

Kimi K30.72s

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

Llama 58.54s

Kimi K39.34s

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)

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Kimi K3 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

Llama 5 · Strengths & Key Advantages

5M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 84%)
Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
Offene Modellgewichte (600B+ total (~40-80B )) 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 K3 · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 131K maximale Output-Token (SWE-bench Verified 74.5% · GPQA Diamond 79%)
Hohe Generierungsgeschwindigkeit von 58 Token/Sekunde bei nur 720 ms Erstlatenz (TTFT)
Offene Modellgewichte (2.8T total (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	Llama 5	Kimi K3
Cost per Task Estimated USD to finish one task	$0.00	$2.00
Output Tokens / Task Tokens generated per task	40K	35K
Reasoning Tokens / Task Thinking tokens burned per task	20K	18K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Llama 5 vs Kimi K3: Schnelle Antworten (FAQ)

Which is more intelligent, Llama 5 or Kimi K3?

Llama 5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.

Which model is faster?

Llama 5 is faster, generating 65 tokens per second compared with 58 tokens per second.

Which model has lower latency?

Kimi K3 has lower latency, with a time to first token of 0.72s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 1.05M tokens.

Fazit

Sowohl Llama 5 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Kimi K3 (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 5 führt bei komplexem Reasoning und Coding, Llama 5 liefert den höheren Token-Durchsatz und Llama 5 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 5 oder Kimi K3?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Llama 5 und Kimi K3?+

Llama 5 kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Kimi K3 liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Llama 5 günstiger.

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

Llama 5 bietet 5M Tokens Kontext gegenüber 1.05M Tokens bei Kimi K3. Bei der Generierungsgeschwindigkeit liegt Llama 5 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)

Feature	Llama 5	Kimi K3
Official API Model ID (SDK)	meta-llama/Llama-5	kimi-k3
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-07
Knowledge Cutoff	January 2026	March 2026
Architecture	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)	Colossus-Scale Multimodal Sparse Mixture-of-Experts
Parameters	600B+ total (~40-80B active)	2.8T total (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	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 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, vision, video, audio	text, vision
Output Modalities	text	text

Llama 5 vs Kimi K3

Beliebige Modelle wechseln & vergleichen

Llama 5

Kimi K3

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Llama 5 vs Kimi K3: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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