Gemini 3.5 Flash-Lite vs Grok 4.6
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3.5 Flash-Lite by Google and Grok 4.6 by xAI 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 3.5 Flash-Lite holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.5 Flash-Lite is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.5 Flash-Lite provides an expanded memory horizon with 1,049K 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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Gemini 3.5 Flash-Lite
深度推理模型Google 推出的 Gemini 3.5 Flash-Lite 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0.4/百万 Token，输出价格为 $2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.40 / 1M
$2.00 / 1M
1.05M tokens
N/A
Grok 4.6
深度推理模型xAI 推出的 Grok 4.6 人工智能模型，具备 500K 上下文窗口，输入价格为 $2/百万 Token，输出价格为 $6/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.00 / 1M
$6.00 / 1M
500K tokens
75.8%
核心差异一览
- Gemini 3.5 Flash-Lite is 3.6x cheaper for API usage (input + output tokens combined).
- Gemini 3.5 Flash-Lite has a larger context window (1M vs 500K tokens).
- Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 115 tok/s.
| Feature | Gemini 3.5 Flash-Lite | Grok 4.6 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 69 | 77 |
Provider | xAI | |
Input Price Cost per million input tokens | $0.40 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $6.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.35 / 1M | $1.35 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 500K tokens~750 A4 pages of text |
Max Output Tokens Per single request | 33K | 66K |
Throughput Speed Tokens generated per second | 240 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 75.8% |
GPQA Diamond Graduate-level reasoning | N/A | 77.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.50 / 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.
Gemini 3.5 Flash-Lite · Strengths & Key Advantages
- 由 Google 研发的高精度 Lightweight 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 $0.4/1M，输出 $2/1M），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
Grok 4.6 · Strengths & Key Advantages
- 由 xAI 研发的高精度 1.5T Colossus Sparse MoE 架构，支持 500K 上下文窗口
- 极具竞争力的 API 定价（输入 $2/1M，输出 $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 | Gemini 3.5 Flash-Lite | Grok 4.6 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.55 | $0.65 |
Output Tokens / Task Tokens generated per task | 18K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 22K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.5 Flash-Lite | Grok 4.6 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-5-flash-lite | grok-4-6-latest |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | xAI REST API (OpenAI-Compatible) |
| Release Date | 2026-07 | 2026-08 |
| Knowledge Cutoff | March 2026 | April 2026 |
| Architecture | Lightweight efficient multimodal transformer | 1.5T Colossus Sparse MoE |
| Parameters | Confidential | 1.5T (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($2/1M in) across full 500K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | Cached input at $0.5/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Real-time Web & X (Twitter) Search Grounding + Code Execution |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | API Prompt Engineering & RAG 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 | Google DeepMind Frontier Safety Framework Verified | xAI Production Safety & Alignment 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 |
Gemini 3.5 Flash-Lite vs Grok 4.6: 常见问题解答 (FAQ)
Which is more intelligent, Gemini 3.5 Flash-Lite or Grok 4.6?
Grok 4.6 is more intelligent. It scores 77 compared with 69 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Gemini 3.5 Flash-Lite is cheaper at $0.35 per 1M tokens blended, compared with $1.35 (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 0.58s.
Which model has a larger context window?
Gemini 3.5 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 500K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Gemini 3.5 Flash-Lite if your priority is Autonomous subagent loops, summarization, and data transformation. Its performance in demanding tasks justifies its operational token cost. Choose Grok 4.6 if your workflow prioritizes Medium-to-large software engineering tasks, data synthesis, and complex analysis. 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 Gemini 3.5 Flash-Lite compare to Grok 4.6 in API token pricing and cost per token?+
Gemini 3.5 Flash-Lite costs $0.40 per 1M input tokens and $2.00 per 1M output tokens, whereas Grok 4.6 costs $2.00 input and $6.00 output. Overall, Gemini 3.5 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: Gemini 3.5 Flash-Lite or Grok 4.6?+
Gemini 3.5 Flash-Lite supports a context window of 1,049K tokens (~786k words), while Grok 4.6 supports 500K tokens (~375k words). Therefore, Gemini 3.5 Flash-Lite can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 3.5 Flash-Lite or Grok 4.6?+
Gemini 3.5 Flash-Lite scores at the frontier of coding benchmarks, while Grok 4.6 scores 75.8%. For complex autonomous debugging and agentic engineering, Grok 4.6 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini 3.5 Flash-Lite and Grok 4.6?+
Gemini 3.5 Flash-Lite features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Grok 4.6 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 3.5 Flash-Lite and Grok 4.6?+
Gemini 3.5 Flash-Lite generates approximately 240 tokens per second, while Grok 4.6 delivers 115 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 Gemini 3.5 Flash-Lite and Grok 4.6 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.5 Flash-Lite to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Grok 4.6. 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
- Gemini 3.6 Flash vs Gemini 3.5 Flash-Lite: Benchmarks, Pricing & Speed Comparison对比
- GPT-5.6 Luna vs Gemini 3.5 Flash-Lite: Benchmarks, Pricing & Speed Comparison对比
- Grok 4.6 vs Grok-4: Benchmarks, Pricing & Speed Comparison对比
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed Comparison对比
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparison对比
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparison对比
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