GPT-6 Luna
Ultra-fast low-latency frontier model in the GPT-6 generation, streaming at 130+ tps across a 1-million-token context window at $0.20/$0.80 economics.
1,000k
~750k words input
$0.2 / $0.8
Prompt / Completion per 1M
78.4 % resolved
SWE-bench Verified
Cloud Managed
Zero local VRAM
Holistic Capability Distribution
Normalized 0-100 rating vectors balanced across intelligence, throughput, and economics.
Verified LLM Benchmarks
Empirical evaluations across academic reasoning, software engineering, agentic execution, and human preference.
Inference Latency & Generation Throughput
Empirical telemetry benchmarking generation throughput (Tokens/Second) and Time to First Token (TTFT).
Measured under concurrent production load
p95 latency: 280 ms
Across 6,200 monitored requests
Architectural Strengths vs. Operational Considerations
An objective technical balance sheet analyzing deployment advantages and production constraints.
Key Architectural Strengths
- 1,000,000-token context window in a compact fast tier.
- Sub-110ms time-to-first-token with 135 tps generation.
- Scores 78.4% on SWE-bench Verified at $0.20/$0.80 pricing.
Operational Considerations
- Compared against diffusion-based alternatives like Mercury 3 on raw token throughput.
Hardware & Execution Parameters
Genealogical Provenance
Low-latency fast-tier model of the GPT-6 generation.
openai gpt 5 6 luna
GPT-6 Luna
Architecture Engineering & Capability Deep-Dive
Topology & Attention Mechanics
High-density dense transformer optimized for speculative decoding and low-latency prefill.
Evaluation Profile & Reasoning Capabilities
Fast code edits, real-time agentic tool chaining, and document extraction.
Inference Economics & Cost Optimization
$0.20 / $0.80 per 1M tokens with $0.02 cache reads.
Primary Production Workloads & Suitability
Cloud Deployment Architecture & Hardware Sizing
Cloud Deployment Architecture
Distributed enterprise serving infrastructure, autoscaling semantics, and runtime execution profile.
Proprietary commercial model hosted on managed multi-region clusters with automatic autoscaling, high availability, and prompt prefix caching.
Multi-Tenant Cloud API
Continuous autoscaling with regional failover
0 GB Local VRAM
Inference executed on hyperscaler accelerator clusters
1,000k tokens
Supported with automated KV cache prefix compression
SDK Installation
Official Repositories & Papers
Compare Showdowns
GPT-6 Luna vs Mercury 3
Compare GPT-6 Luna against Mercury 3's verified benchmarks, context window, and pricing.
GPT-6 Luna vs Claude Haiku 5.5
Compare GPT-6 Luna against Claude Haiku 5.5's verified benchmarks, context window, and pricing.
GPT-6 Luna vs GPT-6 Sol
Compare GPT-6 Luna against GPT-6 Sol's verified benchmarks, context window, and pricing.
GPT-6 Luna vs OpenAI Decisions
Compare GPT-6 Luna against OpenAI Decisions's verified benchmarks, context window, and pricing.
Commercial Token Rates & Cost Estimator
$0.20 / 1M
Standard prompt context ingestion
$0.80 / 1M
Generated completion & thinking tokens
$0.02 / 1M
Re-used prompt prefix context
Monthly Production Cost Simulator
$43.20
Includes prompt caching
-$10.80
Saved via prefix context caching
Comparable Foundation Architectures
Alternative foundation models with comparable intelligence benchmarks, context windows, and deployment profiles.
Mercury 3
Mercury 3 is Inception's frontier diffusion large language model (dLLM) co-founded by Stanford Professor Stefano Ermon, delivering an unprecedented 1,068 tokens per second inference speed via parallel denoising across a native 1-million-token context window.
Claude Haiku 5.5
Sub-100ms ultra-fast foundation model delivering Claude 4 Sonnet-level capability at edge prices ($0.50/$2.50) with 500k context.
GPT-6 Sol
Primary solar flagship model of the GPT-6 generation, delivering 91.0% SWE-bench resolution, 1M context, and $4/$16 pricing for frontier developer automation.
OpenAI Decisions
OpenAI Decisions is OpenAI's dedicated high-throughput decision-making API powered by GPT-6 Luna, accessed via the POST /v1/decisions endpoint. Designed for agent routing, automated content moderation, and classification, it processes typed questions (predicates, categorical choices, and rubric scores) roughly 10x faster than standard generation, billed at $0.10 per million input tokens with zero output-token fees.
SDK Installation & API Integration
import OpenAI from 'openai';
const client = new OpenAI();
const completion = await client.chat.completions.create({
model: 'gpt-6-luna',
messages: [{ role: 'user', content: 'Ping' }],
});
console.log(completion.choices[0].message.content);Frequently Asked Questions about GPT-6 Luna
Engineering specifications, VRAM sizing, commercial licensing terms, token economics, and empirical benchmark evaluations.
GPT-6 Luna is an autoregressive 1M-context model generating at ~135 tps, while Mercury 3 uses a parallel diffusion architecture reaching 1,068 tps.