GPT-6 Luna vs GPT-5.6 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-6 Luna dari OpenAI dan GPT-5.6 Sol dari OpenAI adalah dua model AI unggulan. GPT-6 Luna menawarkan jendela konteks 272K token dengan harga $0.10/$0.50 per 1 juta token, sedangkan GPT-5.6 Sol menyediakan konteks 1.05M token seharga $4.00/$20.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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GPT-6 Luna
Model Ringan & Hemat BiayaGPT-6 Luna dari OpenAI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 272K, kecepatan 245 token/detik, indeks kecerdasan 67/100, serta tarif $0.1
$0.10 / 1M
$0.50 / 1M
272K tokens
70.5%
GPT-5.6 Sol
Model Unggulan Garis DepanGPT-5.6 Sol dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1.1M, kecepatan 108 token/detik, indeks kecerdasan 89/100, serta tarif
$4.00 / 1M
$20.00 / 1M
1.05M tokens
83.8%
Perbedaan Utama Sekilas
- GPT-6 Luna is 40.0x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Sol has a larger context window (1M vs 272K tokens).
- GPT-6 Luna is faster at 245 tok/s vs 108 tok/s.
- GPT-5.6 Sol leads on SWE-bench coding (83.8% vs 70.5%).
| Feature | GPT-6 Luna | GPT-5.6 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 67 | 89 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.50 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 272K tokens~408 A4 pages of text | 1.05M tokens~1,575 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 245 tok/s | 108 tok/s |
SWE-bench Verified Real-world software engineering | 70.5% | 83.8% |
GPQA Diamond Graduate-level reasoning | 73.5% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.40 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6 Luna · Strengths & Key Advantages
- Jendela konteks 272K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 70.5% · GPQA Diamond 73.5%)
- Kecepatan generasi tinggi 245 token/detik dengan latensi token pertama (TTFT) hanya 380 ms
- Skema harga API kompetitif (tarif $0.1/$0.5 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GPT-5.6 Sol · Strengths & Key Advantages
- Jendela konteks 1.1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 83.8% · GPQA Diamond 82%)
- Kecepatan generasi tinggi 108 token/detik dengan latensi token pertama (TTFT) hanya 1210 ms
- Skema harga API kompetitif (tarif $4/$20 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6 Luna | GPT-5.6 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.13 | $3.30 |
Output Tokens / Task Tokens generated per task | 46K | 64K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 38K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Luna | GPT-5.6 Sol |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-luna | gpt-5-6-sol |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | April 2026 | February 2026 |
| Architecture | Ultra-budget compact reasoning model | Frontier unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 272K window | Flat rate ($4/1M in) across full 1050K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.01/1M (no extra cache-write fee) | Automatic prefix caching at $0.4/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-6 Luna vs GPT-5.6 Sol: Jawaban Cepat (FAQ)
Which is more intelligent, GPT-6 Luna or GPT-5.6 Sol?
GPT-5.6 Sol is more intelligent. It scores 89 compared with 67 on the composite Intelligence Index.
Which model is faster?
GPT-6 Luna is faster, generating 245 tokens per second compared with 108 tokens per second.
Which model is cheaper to run?
GPT-6 Luna is cheaper at $0.08 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-6 Luna has lower latency, with a time to first token of 0.38s compared with 1.21s.
Which model has a larger context window?
GPT-5.6 Sol has a larger context window, supporting 1.05M tokens compared with 272K tokens.
Kesimpulan
Baik GPT-6 Luna (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-5.6 Sol (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-5.6 Sol unggul dalam penalaran dan pemrograman, GPT-6 Luna memberikan kecepatan generasi token lebih tinggi, dan GPT-6 Luna lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, GPT-6 Luna atau GPT-5.6 Sol?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-5.6 Sol memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API GPT-6 Luna vs GPT-5.6 Sol?+
GPT-6 Luna mengenakan biaya $0.10 / $0.50 per 1M token input/output, sedangkan GPT-5.6 Sol sebesar $4.00 / $20.00. Secara keseluruhan, GPT-6 Luna lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
GPT-6 Luna mendukung konteks 272K token dibandingkan 1.05M token pada GPT-5.6 Sol, sementara GPT-6 Luna unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Sumber resmi: Dokumentasi API resmi OpenAI & OpenAI serta evaluasi benchmark independen (2026).