Gemini 3.1 Pro API pricing is officially set at $2.00 per 1M input tokens and $12.00 per 1M output tokens for standard context windows (up to 200K), representing a massive leap in reasoning-to-cost efficiency. While these rates appear straightforward, many developers find themselves hitting a wall with Google’s strict “Tier 2” requirements, which mandate a $250 cumulative spend and a 30-day waiting period before unlocking production-ready rate limits.
These administrative bottlenecks and regional payment restrictions often lead to fragmented workflows and delayed project launches. GlobalGPT solves this friction by providing an enterprise-grade gateway that bypasses traditional tier-jumping, offering instant high-quota access without the need for overseas credit cards or regional verification.
By leveraging our all-in-one platform, you can orchestrate agentic workflows across industry-leading models like GPT-5.2, Claude 4.5 et Gemini 3 Pro through a single, unified interface. With a Plan de base starting at just $5.8, GlobalGPT delivers a high-performance environment with no rigid region locks and significantly higher usage caps than official individual subscriptions, making it the most choix économique for developers in 2026.
Gemini 3.1 Pro API Pricing: How Much Does It Really Cost per 1M Tokens?
Gemini 3.1 Pro pricing is structured by context length and token type. For standard requests under 200,000 tokens, the cost is $2.00 per 1 million input tokens and $12.00 per 1 million output tokens.
Standard vs. Long-Context Billing
Costs increase when processing long context windows. Once a prompt exceeds the 200,000-token threshold, input pricing doubles to $4.00 per 1M tokens, and output pricing rises to $18.00 per 1M tokens.
The “Thinking Token” Tax
Gemini 3.1 Pro uses internal chain-of-thought reasoning. These “Thinking Tokens” are billed at standard output rates. High-complexity reasoning tasks generate more internal tokens, which can significantly increase the total cost per request compared to non-reasoning models.
Free Tier vs. Paid Tier
Le Free Tier allows 15 RPM et 100 RPD for the Pro model. However, data sent through the Free Tier is used to improve Google’s models. Paid Tier users pay per token, but their data remains private and excluded from training sets.
What Are the Key Upgrades in Gemini 3.1 Pro Compared to Gemini 3.0?
The primary upgrade in Gemini 3.1 Pro is its reasoning capability. While it maintains the same price as the 3.0 version, its logical performance in abstract tasks has more than doubled.
ARC-AGI-2 Breakthrough
Gemini 3.1 Pro scores 77.1% on the ARC-AGI-2 benchmark, a massive increase from the 31.1% achieved by Gemini 3.0 Pro. This metric indicates a superior ability to solve novel logical patterns that were not part of the training data.
New Thinking Levels
Developers can now adjust the thinking_level parameter. Options include Low, Medium, and High. Higher levels improve accuracy for complex coding and math but increase latency and token consumption.
Multimodal Mastery
The model natively supports 1M context windows for text, images, video, and PDF. It can process up to 1 hour of video or 30,000 lines of code in a single prompt with high retrieval accuracy.
Why is the Gemini 3.1 Pro Output Limit Capped at 8K by Default and How to Unlock 64K?
Gemini 3.1 Pro supports a 65,536 (64K) token output, yet most users receive truncated answers. This is due to a default API configuration that limits output to ensure lower latency and cost protection.
| Fonctionnalité | Default Setting | Maximum Capability |
| Limite de jetons de sortie | 8,192 | 65,536 (64K) |
| Cost (at Max Output) | ~$0.10 | ~$0.78 |
| Word Count Approx. | 6 000 mots | 49 000 mots |
Configuring maxOutputTokens
To access the full 64K capacity, developers must explicitly set the max_output_tokens parameter in their API call. Failure to do so results in the model stopping at the 8,192-token mark, even if the response is incomplete.
Use Cases for 64K Output
Long-form output is essential for generating complete software modules, legal contracts, or technical manuals. With 64K tokens, the model can generate approximately 50,000 words in a single turn.
How Do I Fix “Rate Limit Reached” and the Strict RPD 250 Limit in Google AI Studio?
Google AI Studio imposes strict quotas that stall production. Even paid Tier 1 users are often limited to 250 Requests Per Day (RPD) for preview models, which is insufficient for high-traffic applications. models, which is insufficient for high-traffic applications.
The Tier 2 Barrier
Upgrading to Tier 2 requires a $250 cumulative spend and an account age of at least 30 days. For new teams or individual developers, this creates a significant barrier to scaling their AI tools.
Bypassing Region Locks
Many developers face “Service unavailable” errors due to regional restrictions on Google Cloud billing. This prevents access even if the developer is willing to pay.
Professional API Relays
Using an API relay or a unified platform like GlobalGPT allows developers to access these high-performance models without the restrictive Tier 2 spending requirements. These platforms aggregate resources to provide higher rate limits and immediate access.
| Tier Level | RPD Limit (Pro) | Exigence |
| Niveau gratuit | 100 | $0 Spend |
| Paid Tier 1 | 250 | Billing enabled |
| Paid Tier 2 | 2,000+ | $250+ Spend |
| GlobalGPT | Elastic/High | $5.8 Plan de base |
Gemini 3.1 Pro vs. Claude 4.5 vs. GPT-5.2: Which API Offers the Best ROI for Developers?
In 2026, choosing an API depends on the specific task. Gemini 3.1 Pro leads in science and reasoning, while competitors maintain edges in creative writing and tool orchestration.
Coding Benchmarks
Sur le site SWE-Bench Verified test, Claude 4.5 and Gemini 3.1 Pro are nearly tied at ~80.6%. Gemini offers a better ROI for high-volume coding due to its lower input costs compared to Claude’s premium pricing.
Science & Math Supremacy
Gemini 3.1 Pro’s 94.3% on GPQA Diamond makes it the preferred model for research-heavy industries. It outperforms GPT-5.2 in complex PhD-level scientific reasoning tasks.
How to Use Context Caching and Tiered Routing to Reduce Your API Costs by 90%?
API costs can be optimized through engineering strategies. Using official features like Context Caching can drop input costs from $2.00 down to $0,50 par 1 million de jetons.
Context Caching 101
If your application uses a 50K-token system prompt (e.g., a codebase or product manual), caching allows you to pay only for “Cache Hits” on subsequent requests. This is ideal for RAG-based systems.
Tiered Routing Logic
Developers should route simple queries to Gemini 3 Flash ($0.10/1M) and reserve Gemini 3.1 Pro only for tasks with a high complexity score. This hybrid approach maintains quality while slashing the monthly bill.
What is the Best Way to Access Gemini 3.1 Pro Without an Overseas Credit Card?
Accessing official Google API keys often requires a US or European billing address and credit card. For global developers, this is the primary obstacle to using Gemini 3.1 Pro.
GlobalGPT: The Unified Solution
GlobalGPT removes these barriers by allowing users to pay via local methods like Alipay or WeChat. A single subscription provides access to Gemini 3.1 Pro, Claude 4.5, and GPT-5.2 without managing multiple accounts.
Subscription Logic
Instead of paying $20/month for each platform, the $5.8 Plan de base on GlobalGPT provides a consolidated pool of credits. This is the most efficient way to test and deploy multi-model workflows.
Questions fréquemment posées
Q1: How much does the Gemini 3.1 Pro API cost per 1 million tokens?
For standard context (≤200K), it costs $2.00 per 1M input tokens et $12.00 per 1M output tokens. If the context exceeds 200K, the input price doubles to $4.00 per 1M tokens.
Q2: Why is my Gemini 3.1 Pro API response being cut off or truncated?
By default, the API is capped at 8 192 jetons to manage latency. To unlock the full 64,536 (64K) token output, you must manually adjust the max_output_tokens parameter in your request configuration.
Q3: How can I bypass the Gemini API “Tier 2” $250 spend requirement?
Reaching Tier 2 for higher rate limits normally requires spending $250 and waiting 30 days. GlobalGPT provides an immediate workaround, offering high-quota access to Gemini 3.1 Pro without the cumulative spend barrier.
Conclusion: Is Gemini 3.1 Pro the Right Choice for Your 2026 AI Workflow?
Gemini 3.1 Pro is currently the most powerful reasoning model for scientific and abstract logic tasks. While its pricing is standard for the industry, its ability to process 1M context windows and output 64K tokens makes it a unique tool for long-form automation.
- Choose Gemini 3.1 Pro for: PhD-level science, 1M context RAG, and abstract reasoning.
- Choose Claude 4.5 for: Human-like nuance and high-stakes document auditing.
- Choose GPT-5.2 for: Robust tool-use and established agent frameworks.
