GPT-5.6 Luna vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-5.6 Luna by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
Beliebige Modelle wechseln & vergleichen
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-5.6 Luna
Kompaktes Effizienz-ModellGPT-5.6 Luna von OpenAI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 1.1M Kontextfenster, 230 Token/s Durchsatz, einem Intelligenz-Index von 57/100 und $0.2/$1.2 pro 1M Token.
$0.20 / 1M
$1.20 / 1M
1.05M tokens
63.5%
Kimi K3
Open-Weights-Reasoning-ModellKimi 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
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Wichtigste Unterschiede auf einen Blick
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Luna has a larger context window (1M vs 1M tokens).
- GPT-5.6 Luna is faster at 230 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 63.5%).
| Feature | GPT-5.6 Luna | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 57 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.20 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.17 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 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 | 230 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 63.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 70.5% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.00 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.6 Luna · Strengths & Key Advantages
- 1.1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 63.5% · GPQA Diamond 70.5%)
- Hohe Generierungsgeschwindigkeit von 230 Token/Sekunde bei nur 420 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($0.2/$1.2 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
- Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
- 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 | GPT-5.6 Luna | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.19 | $2.00 |
Output Tokens / Task Tokens generated per task | 46K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 18K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.6 Luna | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-6-luna | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-07 |
| Knowledge Cutoff | February 2026 | March 2026 |
| Architecture | Efficient compact reasoning model | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 1050K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.02/1M (no extra cache-write fee) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | 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 | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-5.6 Luna vs Kimi K3: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-5.6 Luna or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 57 on the composite Intelligence Index.
Which model is faster?
GPT-5.6 Luna is faster, generating 230 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $0.17 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.6 Luna has lower latency, with a time to first token of 0.42s compared with 0.72s.
Which model has a larger context window?
GPT-5.6 Luna has a larger context window, supporting 1.05M tokens compared with 1.05M tokens.
Fazit
Choose GPT-5.6 Luna for optimal für maximale kosteneffizienz pro token und ressourcenschonende produktions-workloads, enterprise-rag-systeme und skalierbare api-pipelines., or choose Kimi K3 for optimal für open-weights-reasoning mit transparenter gedankenketten-verarbeitung (chain-of-thought), enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: GPT-5.6 Luna or Kimi K3?+
GPT-5.6 Luna costs $0.2/1M input and $1.2/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-5.6 Luna or Kimi K3?+
GPT-5.6 Luna supports 1.1M tokens whereas Kimi K3 supports 1.0M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Aion-3.0 vs. Aion-Mini: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aion-3.0 vs. Gemini 3.6 Flash: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aion-Mini vs. Laguna XS 2.1: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 32B vs. Llama 3.2 90B Vision: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 8B vs. Command R+: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 8B vs. Qwen 2.5 7B Instruct: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
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.
Offizielle Quellen: OpenAI & Moonshot AI Official Documentation