Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude Sonnet 4.6 by Anthropic 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.
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Claude Sonnet 4.6
Modello di Ragionamento ProfondoClaude Sonnet 4.6 di Anthropic è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $3/1M token in input e $15/1M in output, ideale per coding, ragionamento e automazione aziendale.
$3.00 / 1M
$15.00 / 1M
1M tokens
76.1%
Gemini 3.1 Flash-Lite
Modello di Ragionamento ProfondoGemini 3.1 Flash-Lite di Google è un modello di intelligenza artificiale con finestra di contesto da 1.0M token e tariffazione API di $0.15/1M token in input e $0.6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.15 / 1M
$0.60 / 1M
1.05M tokens
N/A
Differenze Chiave a Colpo d’Occhio
- Gemini 3.1 Flash-Lite is 22.6x cheaper for API usage (input + output tokens combined).
- Gemini 3.1 Flash-Lite has a larger context window (1M vs 1M tokens).
- Gemini 3.1 Flash-Lite is faster at 250 tok/s vs 92 tok/s.
| Feature | Claude Sonnet 4.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 60 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $3.00 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $0.11 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 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 | 92 tok/s | 250 tok/s |
SWE-bench Verified Real-world software engineering | 76.1% | N/A |
GPQA Diamond Graduate-level reasoning | 74% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 1M | $0.04 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Tokens emitted per second during active generation streaming.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Sonnet 4.6 · Strengths & Key Advantages
- Architettura High-Throughput Multimodal Agent Transformer avanzata di Anthropic con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($3/1M input, $15/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Gemini 3.1 Flash-Lite · Strengths & Key Advantages
- Architettura Compact efficient multimodal transformer avanzata di Google con supporto fino a 1.0M token di contesto
- Ottimo rapporto qualità-prezzo ($0.15/1M input, $0.6/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Sonnet 4.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.29 | $0.38 |
Output Tokens / Task Tokens generated per task | 72K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 5K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Sonnet 4.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
| Official API Model ID (SDK) | claude-sonnet-4-6-20260201 | models/gemini-3-1-flash-lite |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-02 | 2026-03 |
| Knowledge Cutoff | December 2025 | November 2025 |
| Architecture | High-Throughput Multimodal Agent Transformer | Compact efficient multimodal transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL | 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 Citations + Code Execution Tool + MCP Connectors | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 |
Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite: Risposte Rapide (FAQ)
Which model is faster?
Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 92 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 $2.31 (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 0.65s.
Which model has a larger context window?
Gemini 3.1 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose Claude Sonnet 4.6 if your priority is Production coding copilots, full-repo code search, customer agent workflows, and automated workflows. 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.
Domande Frequenti (FAQ)
How does Claude Sonnet 4.6 compare to Gemini 3.1 Flash-Lite in API token pricing and cost per token?+
Claude Sonnet 4.6 costs $3.00 per 1M input tokens and $15.00 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: Claude Sonnet 4.6 or Gemini 3.1 Flash-Lite?+
Claude Sonnet 4.6 supports a context window of 1,000K tokens (~750k 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: Claude Sonnet 4.6 or Gemini 3.1 Flash-Lite?+
Claude Sonnet 4.6 scores 76.1% on SWE-bench Verified, while Gemini 3.1 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, Claude Sonnet 4.6 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Claude Sonnet 4.6 and Gemini 3.1 Flash-Lite?+
Claude Sonnet 4.6 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 Claude Sonnet 4.6 and Gemini 3.1 Flash-Lite?+
Claude Sonnet 4.6 generates approximately 92 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 Claude Sonnet 4.6 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 Claude Sonnet 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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs