GPT-5.4 mini vs Gemini 3.1 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
بدّل وقارن بين أي نموذجين
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GPT-5.4 mini
نموذج استدلال عميقنموذج GPT-5.4 mini المقدم من OpenAI يوفر نافذة سياق تصل إلى 400K رمز، بتكلفة $0.5 لكل مليون رمز إدخال و$4 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.50 / 1M
$4.00 / 1M
400K tokens
68.5%
Gemini 3.1 Flash-Lite
نموذج استدلال عميقنموذج Gemini 3.1 Flash-Lite المقدم من Google يوفر نافذة سياق تصل إلى 1.0M رمز، بتكلفة $0.15 لكل مليون رمز إدخال و$0.6 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.15 / 1M
$0.60 / 1M
1.05M tokens
N/A
أهم الفروقات في لمحة
- 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.
| Feature | 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.4 mini · Strengths & Key Advantages
- بنية Compact agent-ready reasoning model متطورة من OpenAI مع سعة سياق تبلغ 400K رمز
- كفاءة عالية في التكلفة ($0.5/1M إدخال و$4/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Gemini 3.1 Flash-Lite · Strengths & Key Advantages
- بنية Compact efficient multimodal transformer متطورة من Google مع سعة سياق تبلغ 1.0M رمز
- كفاءة عالية في التكلفة ($0.15/1M إدخال و$0.6/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
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 |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: إجابات سريعة (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.
الحكم النهائي
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.
الأسئلة الشائعة (FAQ)
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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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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs