GPT-5.6 Luna vs Gemini 3.5 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.6 Luna by OpenAI and Gemini 3.5 Flash-Lite by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-5.6 Luna holds a notable pricing advantage for token ingestion. On output generation, GPT-5.6 Luna is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.6 Luna provides an expanded memory horizon with 1,050K tokens. In latency-sensitive production environments, Gemini 3.5 Flash-Lite leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GPT-5.6 Luna
Efficient 5.6An economical GPT-5.6 tier for fast, high-volume work.
$0.20 / 1M
$1.20 / 1M
1.05M tokens
63.5%
Gemini 3.5 Flash-Lite
Efficient FlashSub-dollar multimodal reasoning engine tuned for massive enterprise traffic and real-time subagents.
$0.40 / 1M
$2.00 / 1M
1.05M tokens
N/A
Key Differences at a Glance
- GPT-5.6 Luna is 1.8x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Luna has a larger context window (1M vs 1M tokens).
- Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 230 tok/s.
| Feature | GPT-5.6 Luna | Gemini 3.5 Flash-Lite |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 57 | 69 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.20 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.17 / 1M | $0.35 / 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 | 33K |
Throughput Speed Tokens generated per second | 230 tok/s | 240 tok/s |
SWE-bench Verified Real-world software engineering | 63.5% | N/A |
GPQA Diamond Graduate-level reasoning | 70.5% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.10 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.6 Luna · Strengths & Key Advantages
- Very cheap while keeping the 1M context window
- Fast enough for real-time pipelines
- Very strong throughput per dollar
Considerations & Trade-offs
- Weak reasoning relative to the Sol and Terra tiers
- Not suitable for complex analysis or long plans
Gemini 3.5 Flash-Lite · Strengths & Key Advantages
- Economical high-volume pricing
- Fast execution with low latency
- 1M context window
Considerations & Trade-offs
- Less adept at deep mathematical proofs
- Text and vision only (no audio input)
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.6 Luna | Gemini 3.5 Flash-Lite |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.19 | $0.55 |
Output Tokens / Task Tokens generated per task | 46K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 7K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.6 Luna | Gemini 3.5 Flash-Lite |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-6-luna | models/gemini-3-5-flash-lite |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-07 | 2026-07 |
| Knowledge Cutoff | February 2026 | March 2026 |
| Architecture | Efficient compact reasoning model | Lightweight efficient multimodal transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 1050K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.02/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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 | Google DeepMind Frontier Safety Framework Verified |
| 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-5.6 Luna vs Gemini 3.5 Flash-Lite: Quick Answers
Which is more intelligent, GPT-5.6 Luna or Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite is more intelligent. It scores 69 compared with 57 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 230 tokens per second.
Which model is cheaper to run?
GPT-5.6 Luna is cheaper at $0.17 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 0.42s.
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.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-5.6 Luna if your priority is High-throughput classification, extraction and routing. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.5 Flash-Lite if your workflow prioritizes Autonomous subagent loops, summarization, and data transformation. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does GPT-5.6 Luna compare to Gemini 3.5 Flash-Lite in API token pricing and cost per token?+
GPT-5.6 Luna costs $0.20 per 1M input tokens and $1.20 per 1M output tokens, whereas Gemini 3.5 Flash-Lite costs $0.40 input and $2.00 output. Overall, GPT-5.6 Luna is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-5.6 Luna or Gemini 3.5 Flash-Lite?+
GPT-5.6 Luna supports a context window of 1,050K tokens (~788k words), while Gemini 3.5 Flash-Lite supports 1,049K tokens (~786k words). Therefore, GPT-5.6 Luna can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.6 Luna or Gemini 3.5 Flash-Lite?+
GPT-5.6 Luna scores 63.5% on SWE-bench Verified, while Gemini 3.5 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, GPT-5.6 Luna provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT-5.6 Luna and Gemini 3.5 Flash-Lite?+
GPT-5.6 Luna features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.5 Flash-Lite provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.6 Luna and Gemini 3.5 Flash-Lite?+
GPT-5.6 Luna generates approximately 230 tokens per second, while Gemini 3.5 Flash-Lite delivers 240 tokens per second. In latency-sensitive production environments, Gemini 3.5 Flash-Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.6 Luna and Gemini 3.5 Flash-Lite to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.6 Luna to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.5 Flash-Lite. This tiered routing pattern cuts overall API bills by up to 70%.
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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs