Gemini 3.1 Pro vs Claude Sonnet 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3.1 Pro by Google and Claude Sonnet 4.5 by Anthropic 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 Pro holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.1 Pro is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.1 Pro provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.1 Pro 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 3.1 Pro
Modello di Ragionamento ProfondoGemini 3.1 Pro di Google è un modello di intelligenza artificiale con finestra di contesto da 1.0M token e tariffazione API di $2/1M token in input e $12/1M in output, ideale per coding, ragionamento e automazione aziendale.
$2.00 / 1M
$12.00 / 1M
1.05M tokens
74.5%
Claude Sonnet 4.5
Modello di Ragionamento ProfondoClaude Sonnet 4.5 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
77.8%
Differenze Chiave a Colpo d’Occhio
- Gemini 3.1 Pro is 1.3x cheaper for API usage (input + output tokens combined).
- Gemini 3.1 Pro has a larger context window (1M vs 1M tokens).
- Gemini 3.1 Pro is faster at 110 tok/s vs 90 tok/s.
- Claude Sonnet 4.5 leads on SWE-bench coding (77.8% vs 74.5%).
| Feature | Gemini 3.1 Pro | Claude Sonnet 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 91 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $2.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $12.00 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.95 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 64K |
Throughput Speed Tokens generated per second | 110 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | 74.5% | 77.8% |
GPQA Diamond Graduate-level reasoning | 86.5% | 70.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.30 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi 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.
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.
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.
Gemini 3.1 Pro · Strengths & Key Advantages
- Architettura Pathways High-Capacity Reasoning Architecture avanzata di Google con supporto fino a 1.0M token di contesto
- Ottimo rapporto qualità-prezzo ($2/1M input, $12/1M 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
Claude Sonnet 4.5 · Strengths & Key Advantages
- Architettura Hybrid Reasoning 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.1 Pro | Claude Sonnet 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.85 | $2.10 |
Output Tokens / Task Tokens generated per task | 52K | 55K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 30K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.1 Pro | Claude Sonnet 4.5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-1-pro | claude-sonnet-4-5-20251101 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-02 | 2025-11 |
| Knowledge Cutoff | November 2025 | March 2025 |
| Architecture | Pathways High-Capacity Reasoning Architecture | Hybrid Reasoning |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 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 | Implicit + Explicit Context Caching at $0.5/1M with configurable TTL | $0.3/1M cached read (90% off); 5-min default & 1-hour extended 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 3.1 Pro vs Claude Sonnet 4.5: Risposte Rapide (FAQ)
Which is more intelligent, Gemini 3.1 Pro or Claude Sonnet 4.5?
Claude Sonnet 4.5 is more intelligent. It scores 91 compared with 86 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Pro is faster, generating 110 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Gemini 3.1 Pro is cheaper at $1.95 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.1 Pro has lower latency, with a time to first token of 0.78s compared with 0.90s.
Which model has a larger context window?
Gemini 3.1 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose Gemini 3.1 Pro if your priority is Autonomous coding bots, deep mathematical proofs, and complex repository refactoring. Its performance in demanding tasks justifies its operational token cost. Choose Claude Sonnet 4.5 if your workflow prioritizes Code generation, developer tools, API development, and cost-sensitive agentic engineering. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Gemini 3.1 Pro compare to Claude Sonnet 4.5 in API token pricing and cost per token?+
Gemini 3.1 Pro costs $2.00 per 1M input tokens and $12.00 per 1M output tokens, whereas Claude Sonnet 4.5 costs $3.00 input and $15.00 output. Overall, Gemini 3.1 Pro 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 3.1 Pro or Claude Sonnet 4.5?+
Gemini 3.1 Pro supports a context window of 1,049K tokens (~786k words), while Claude Sonnet 4.5 supports 1,000K tokens (~750k words). Therefore, Gemini 3.1 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 3.1 Pro or Claude Sonnet 4.5?+
Gemini 3.1 Pro scores 74.5% on SWE-bench Verified while Claude Sonnet 4.5 scores 77.8%. For autonomous software engineering and multi-file debugging, Claude Sonnet 4.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemini 3.1 Pro and Claude Sonnet 4.5?+
Gemini 3.1 Pro features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Sonnet 4.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 3.1 Pro and Claude Sonnet 4.5?+
Gemini 3.1 Pro generates approximately 110 tokens per second, while Claude Sonnet 4.5 delivers 90 tokens per second. In latency-sensitive production environments, Gemini 3.1 Pro provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3.1 Pro and Claude Sonnet 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.1 Pro to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Sonnet 4.5. 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