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.
Changez et Comparez les Modèles
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GPT-5.6 Luna
Efficace 5.6un economical GPT-5.6 palier pour rapide, élevé-volume travail.
$0.20 / 1M
$1.20 / 1M
1.05M tokens
63.5%
Gemini 3.5 Flash-Lite
Flash efficacesous-dollar multimodal raisonnement moteur tuned pour massif d'entreprise traffic et en temps réel subagents.
$0.40 / 1M
$2.00 / 1M
1.05M tokens
N/A
Différences Clés en un Coup d’Œil
- 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.
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.6 Luna · Strengths & Key Advantages
- très cheap tandis que keeping le 1M fenêtre de contexte
- rapide enough pour en temps réel pipelines
- très solide débit par dollar
Considerations & Trade-offs
- Weak raisonnement par rapport au Sol et Terra tiers
- pas suitable pour complexe analyse ou long plans
Gemini 3.5 Flash-Lite · Strengths & Key Advantages
- Economical élevé-volume tarification
- rapide exécution avec faible latence
- 1M fenêtre de contexte
Considerations & Trade-offs
- moins adept à profond mathématique proofs
- texte et vision seulement (no audio entrée)
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (FAQ)
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.
Questions fréquentes
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.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs