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.
किसी भी मॉडल को बदलें और तुलना करें
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
डीप रीजनिंग मॉडलOpenAI द्वारा विकसित GPT-5.6 Luna एक उन्नत AI मॉडल है, जिसमें 1.1M टोकन का कॉन्टेक्स्ट विंडो और $0.2/1M इनपुट तथा $1.2/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.20 / 1M
$1.20 / 1M
1.05M tokens
63.5%
Kimi K3
ओपन-वेट्स मॉडलMoonshot AI द्वारा विकसित Kimi K3 एक उन्नत AI मॉडल है, जिसमें 1.0M टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
एक नज़र में मुख्य अंतर
- 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 |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkसत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.6 Luna · Strengths & Key Advantages
- OpenAI की उन्नत Efficient compact reasoning model आर्किटेक्चर और 1.1M कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0.2/1M इनपुट, $1.2/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Kimi K3 · Strengths & Key Advantages
- Moonshot AI की उन्नत Colossus-Scale Multimodal Sparse Mixture-of-Experts आर्किटेक्चर और 1.0M कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
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 |
तकनीकी विशिष्टताएँ (Specs)
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: त्वरित प्रश्नोत्तर (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.
निष्कर्ष
Choose GPT-5.6 Luna for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और openai आधारित प्रोडक्शन सिस्टम, or choose Kimi K3 for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और moonshot ai आधारित प्रोडक्शन सिस्टम.
अक्सर पूछे जाने वाले प्रश्न (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, Pricing & Speed Comparisonतुलना
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparisonतुलना
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparisonतुलना
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed Comparisonतुलना
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed Comparisonतुलना
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed Comparisonतुलना
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.
स्रोत: OpenAI & Moonshot AI Official Documentation