GPT-5.4 mini vs Gemini 3.1 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.4 mini by OpenAI and Gemini 3.1 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, Gemini 3.1 Flash-Lite holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.1 Flash-Lite is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.1 Flash-Lite provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.1 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.
Cambia y Compara Cualquier Modelo
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GPT-5.4 mini
GPT pequeño y fuerteun sólido pequeño GPT para codificación sub-agentes y alto-volumen trabajo.
$0.50 / 1M
$4.00 / 1M
400K tokens
68.5%
Gemini 3.1 Flash-Lite
Lite de alto volumencosto-optimized alto-volumen Flash modelo desarrollado para responsive interactivo chatbots y extracción canalizaciones.
$0.15 / 1M
$0.60 / 1M
1.05M tokens
N/A
Diferencias Clave de un Vistazo
- Gemini 3.1 Flash-Lite is 5.4x cheaper for API usage (input + output tokens combined).
- Gemini 3.1 Flash-Lite has a larger context window (1M vs 400K tokens).
- Gemini 3.1 Flash-Lite is faster at 250 tok/s vs 175 tok/s.
| Característica | GPT-5.4 mini | Gemini 3.1 Flash-Lite |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 59 | 60 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.50 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $4.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.53 / 1M | $0.11 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 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 | 175 tok/s | 250 tok/s |
SWE-bench Verified Real-world software engineering | 68.5% | N/A |
GPQA Diamond Graduate-level reasoning | 72.5% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.04 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.4 mini · Strengths & Key Advantages
- Excellent precio-un-capacidad para sub-agentes
- rápido enough para parallel herramienta bucles
- confiable estructurado salida
- excelente rendimiento para parallel sub-agente fleets
Considerations & Trade-offs
- Weaker que GPT-5.4 en hard razonamiento
- Shorter contexto que el 5.4 insignia
Gemini 3.1 Flash-Lite · Strengths & Key Advantages
- Extremely bajo costo por request
- Blazing rápido 250 tps transmisión
- 1M ventana de contexto
Considerations & Trade-offs
- menor complejo razonamiento capacidad que Pro
- Simplified llamada un herramientas constraints
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.4 mini | Gemini 3.1 Flash-Lite |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.62 | $0.38 |
Output Tokens / Task Tokens generated per task | 52K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 27K | 5K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GPT-5.4 mini | Gemini 3.1 Flash-Lite |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-4-mini | models/gemini-3-1-flash-lite |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-03 | 2026-03 |
| Knowledge Cutoff | August 2025 | November 2025 |
| Architecture | Compact agent-ready reasoning model | Compact efficient multimodal transformer |
| 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 ($0.5/1M in) across full 400K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.05/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.035/1M 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-5.4 mini vs Gemini 3.1 Flash-Lite: Respuestas Rápidas (FAQ)
Which is more intelligent, GPT-5.4 mini or Gemini 3.1 Flash-Lite?
Gemini 3.1 Flash-Lite is more intelligent. It scores 60 compared with 59 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 175 tokens per second.
Which model is cheaper to run?
Gemini 3.1 Flash-Lite is cheaper at $0.11 per 1M tokens blended, compared with $0.53 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.1 Flash-Lite has lower latency, with a time to first token of 0.27s compared with 0.76s.
Which model has a larger context window?
Gemini 3.1 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 400K tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose GPT-5.4 mini if your priority is Sub-agents, parallel tool calls and high-volume coding tasks. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.1 Flash-Lite if your workflow prioritizes High-throughput microservices, translation, and large-scale document parsing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does GPT-5.4 mini compare to Gemini 3.1 Flash-Lite in API token pricing and cost per token?+
GPT-5.4 mini costs $0.50 per 1M input tokens and $4.00 per 1M output tokens, whereas Gemini 3.1 Flash-Lite costs $0.15 input and $0.60 output. Overall, Gemini 3.1 Flash-Lite 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-5.4 mini or Gemini 3.1 Flash-Lite?+
GPT-5.4 mini supports a context window of 400K tokens (~300k words), while Gemini 3.1 Flash-Lite supports 1,049K tokens (~786k words). Therefore, Gemini 3.1 Flash-Lite can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.4 mini or Gemini 3.1 Flash-Lite?+
GPT-5.4 mini scores 68.5% on SWE-bench Verified, while Gemini 3.1 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, GPT-5.4 mini provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT-5.4 mini and Gemini 3.1 Flash-Lite?+
GPT-5.4 mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.1 Flash-Lite provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.4 mini and Gemini 3.1 Flash-Lite?+
GPT-5.4 mini generates approximately 175 tokens per second, while Gemini 3.1 Flash-Lite delivers 250 tokens per second. In latency-sensitive production environments, Gemini 3.1 Flash-Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.4 mini and Gemini 3.1 Flash-Lite to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.1 Flash-Lite to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.4 mini. 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs