GPT-5.4 nano vs Gemini 3.1 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.4 nano 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, GPT-5.4 nano 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.
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GPT-5.4 nano
深度推理模型OpenAI 推出的 GPT-5.4 nano 人工智能模型，具备 400K 上下文窗口，输入价格为 $0.1/百万 Token，输出价格为 $0.8/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.10 / 1M
$0.80 / 1M
400K tokens
48%
Gemini 3.1 Flash-Lite
深度推理模型Google 推出的 Gemini 3.1 Flash-Lite 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0.15/百万 Token，输出价格为 $0.6/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.15 / 1M
$0.60 / 1M
1.05M tokens
N/A
核心差异一览
- Gemini 3.1 Flash-Lite is 1.1x 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 235 tok/s.
| Feature | GPT-5.4 nano | Gemini 3.1 Flash-Lite |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 60 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.10 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 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 | 235 tok/s | 250 tok/s |
SWE-bench Verified Real-world software engineering | 48% | N/A |
GPQA Diamond Graduate-level reasoning | 61.5% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.04 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.4 nano · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Nano agent-ready reasoning model 架构，支持 400K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.1/1M，输出 $0.8/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Gemini 3.1 Flash-Lite · Strengths & Key Advantages
- 由 Google 研发的高精度 Compact efficient multimodal transformer 架构，支持 1.0M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.15/1M，输出 $0.6/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.4 nano | Gemini 3.1 Flash-Lite |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.11 | $0.38 |
Output Tokens / Task Tokens generated per task | 44K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 19K | 5K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.4 nano | Gemini 3.1 Flash-Lite |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-4-nano | 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 | Nano 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 ($0.1/1M 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 $0.01/1M (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 nano vs Gemini 3.1 Flash-Lite: 常见问题解答 (FAQ)
Which is more intelligent, GPT-5.4 nano or Gemini 3.1 Flash-Lite?
Gemini 3.1 Flash-Lite is more intelligent. It scores 60 compared with 45 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 235 tokens per second.
Which model has lower latency?
Gemini 3.1 Flash-Lite has lower latency, with a time to first token of 0.27s compared with 0.40s.
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.
综合结论
Verdict & Model Selection Recommendation: Choose GPT-5.4 nano if your priority is Bulk automation, tagging and cheap agent routing. 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.
常见问题 (FAQ)
How does GPT-5.4 nano compare to Gemini 3.1 Flash-Lite in API token pricing and cost per token?+
GPT-5.4 nano costs $0.10 per 1M input tokens and $0.80 per 1M output tokens, whereas Gemini 3.1 Flash-Lite costs $0.15 input and $0.60 output. Overall, GPT-5.4 nano 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 nano or Gemini 3.1 Flash-Lite?+
GPT-5.4 nano 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 nano or Gemini 3.1 Flash-Lite?+
GPT-5.4 nano scores 48% 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 nano provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT-5.4 nano and Gemini 3.1 Flash-Lite?+
GPT-5.4 nano 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 nano and Gemini 3.1 Flash-Lite?+
GPT-5.4 nano generates approximately 235 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 nano and Gemini 3.1 Flash-Lite to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.4 nano to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.1 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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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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs