Gemini 3.5 Flash-Lite vs Grok 4.6
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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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.
किसी भी मॉडल को बदलें और तुलना करें
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Gemini 3.5 Flash-Lite
डीप रीजनिंग मॉडलGoogle द्वारा विकसित Gemini 3.5 Flash-Lite एक उन्नत AI मॉडल है, जिसमें 1.0M टोकन का कॉन्टेक्स्ट विंडो और $0.4/1M इनपुट तथा $2/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.40 / 1M
$2.00 / 1M
1.05M tokens
N/A
Grok 4.6
डीप रीजनिंग मॉडलxAI द्वारा विकसित Grok 4.6 एक उन्नत AI मॉडल है, जिसमें 500K टोकन का कॉन्टेक्स्ट विंडो और $2/1M इनपुट तथा $6/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$2.00 / 1M
$6.00 / 1M
500K tokens
75.8%
एक नज़र में मुख्य अंतर
- 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 |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkEvaluations ongoing
सत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.5 Flash-Lite · Strengths & Key Advantages
- Google की उन्नत Lightweight efficient multimodal transformer आर्किटेक्चर और 1.0M कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0.4/1M इनपुट, $2/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Grok 4.6 · Strengths & Key Advantages
- xAI की उन्नत 1.5T Colossus Sparse MoE आर्किटेक्चर और 500K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($2/1M इनपुट, $6/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
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 |
तकनीकी विशिष्टताएँ (Specs)
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: त्वरित प्रश्नोत्तर (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.
निष्कर्ष
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.
अक्सर पूछे जाने वाले प्रश्न (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.
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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.
स्रोत: GetInfoUs Independent AI Benchmarks & Official API Specs