Gemini 3.1 Pro Technical Deep Dive

1. Gemini 3.1 Pro's Positioning: From "Follower" to "Leader"

On February 19, 2026, Google DeepMind officially released Gemini 3.1 Pro^[1], a major architectural upgrade following Gemini 3 Pro released in late 2025. Over the past two years, Google's large language models had consistently played the role of "follower" in the competition with OpenAI and Anthropic — Gemini 1.5 Pro was overshadowed by GPT-4o, and Gemini 2 Pro lagged behind Claude 3.5 Sonnet in reasoning capabilities. However, the release of Gemini 3.1 Pro completely reversed this narrative.

According to Google's officially published benchmark data, Gemini 3.1 Pro achieved first place in 12 out of 18 mainstream benchmark tests^[1]. These tests span multiple critical dimensions including mathematical reasoning (AIME 2025), scientific Q&A (GPQA Diamond), code engineering (SWE-bench Verified), web browsing comprehension (BrowseComp), and long-text retrieval (MRCR). Independent evaluation organization Artificial Analysis ranked it as the overall #1 in their Intelligence Index v4.0^[10] — the first time a Google model has topped a third-party comprehensive evaluation.

Even more strategically significant is the release timing. Gemini 3.1 Pro's launch fell precisely in the window between Anthropic Claude Opus 4.6 (January 2026) and OpenAI GPT-5.3 (expected March 2026). Google's choice to declare "comprehensive leadership" at this juncture is not only a demonstration of technical prowess but also a carefully calculated market positioning move. For enterprise customers, this means Google Cloud's AI capabilities can, for the first time, compete head-to-head with Azure OpenAI and AWS Bedrock offerings, and even surpass them in certain dimensions.

Notably, Gemini 3.1 Pro is not merely pursuing numerical advantages. The core shift in its design philosophy lies in making reasoning capabilities "explicit and controllable" rather than "implicit". Traditional models' reasoning capabilities are a black box — users cannot intervene in how much computational resources the model invests in thinking when answering. Gemini 3.1 Pro hands this control to developers for the first time, carrying profound cost and performance implications for commercial deployment.

2. Three-Tier Reasoning Architecture: Adaptive Compute Allocation

Gemini 3.1 Pro's most core technical innovation is its Three-Tier Reasoning Architecture, paired with the new Deep Think Mini reasoning engine^[2]. This design directly addresses the core insight from Snell et al.'s research on test-time compute scaling^[8]: not all problems require the same computational investment, and the optimal strategy is to dynamically allocate reasoning resources based on problem difficulty.

How the Three Reasoning Tiers Operate

Developers can select reasoning depth from three tiers through the Gemini API's thinkingLevel parameter:

LOW (Low Reasoning Mode) — Suitable for factual queries, simple translations, format conversions, and other tasks that don't require deep reasoning. In this mode, the model skips most internal thinking processes and generates answers directly. Thinking token consumption is minimal (typically < 100 tokens), latency is shortest (first token response time approximately 0.3-0.8 seconds), and cost is comparable to traditional non-reasoning models. For enterprise customer service chatbots, FAQ retrieval, and other high-frequency, low-complexity scenarios, LOW mode can minimize reasoning costs without sacrificing quality.

MEDIUM (Medium Reasoning Mode) — The default mode, suitable for most everyday tasks including text summarization, multi-turn conversations, and general analysis. The model performs moderate internal reasoning (typical thinking token consumption of 200-2,000), striking a balance between quality and cost. Google's internal testing shows that MEDIUM mode performs within 3% of HIGH mode on most general tasks, at only 1/5 to 1/8 the cost.

HIGH (High Reasoning Mode) — Activates the full Deep Think Mini reasoning engine, suitable for mathematical proofs, complex code debugging, scientific research Q&A, legal analysis, and other tasks requiring multi-step reasoning. In this mode, the model generates large quantities of thinking tokens (typically 2,000-30,000+), performing complete reasoning processes including hypothesis generation, verification, and backtracking correction. This is the mode used when Gemini 3.1 Pro achieves its top benchmark scores^[5].

Deep Think Mini: A Lightweight Reasoning Engine

Deep Think Mini is Gemini 3.1 Pro's built-in reasoning subsystem, fundamentally different in design philosophy from OpenAI's o3 series reasoning models^[4]. o3 is a standalone reasoning model where users must make a binary choice between "using a reasoning model" and "using a standard model." Deep Think Mini is instead a reasoning module embedded within Gemini 3.1 Pro — same model, same API endpoint, with reasoning capabilities toggled on or off through parameter switching.

The advantage of this architectural design is that developers don't need to maintain two sets of API calling logic, nor do they need to build a task routing system on the frontend to determine which requests should be sent to the reasoning model. A single unified API call with one parameter adjustment covers the full spectrum from simple Q&A to deep reasoning.

Thinking Token Billing and Thought Signatures

The three-tier reasoning architecture introduces an entirely new billing dimension: thinking tokens. In HIGH mode, thinking tokens generated during the model's internal reasoning are counted toward output token usage^[6]. This means that a math problem requiring 20,000 thinking tokens to solve in HIGH mode has an actual cost more than 40 times that of the final answer itself (assuming 500 tokens).

Google has also introduced a "Thought Signatures" mechanism — API responses include an encrypted summary of the thinking process but do not expose the complete internal reasoning chain. The purpose of this design is to protect model intellectual property while allowing developers to verify that the model actually performed deep reasoning, rather than charging HIGH-mode pricing for a standard answer.

Quantifying from a cost perspective: the same complex reasoning task might cost $0.01 in LOW mode, approximately $0.05 in MEDIUM mode, and as much as $0.30 in HIGH mode. The up to 30x cost difference between tiers makes reasoning tier selection a critical decision point for enterprise AI cost optimization. Meta Intelligence recommends using MEDIUM mode as the default, enabling deep reasoning in HIGH mode only for specific tasks where evaluation confirms significant quality improvement.

3. ARC-AGI-2 Breakthrough: A Milestone in Abstract Reasoning

Gemini 3.1 Pro's most industry-notable achievement is its breakthrough score of 77.1% on the ARC-AGI-2 benchmark^[1]. To understand the significance of this number, one must first clarify the nature of the ARC-AGI-2 test and its unique position in the AI evaluation landscape.

What Does ARC-AGI-2 Measure?

ARC-AGI (Abstraction and Reasoning Corpus for Artificial General Intelligence) was proposed by Keras creator François Chollet in 2019^[3], designed to measure the AI capability dimension closest to "general intelligence" — discovering abstract rules from a few examples and applying them to generalize. Unlike MMLU which measures knowledge memorization or HumanEval which measures code generation, ARC-AGI tests a more foundational cognitive ability: facing a never-before-seen rule, inferring the rule from just 2-3 input-output examples and correctly predicting the output for new inputs.

ARC-AGI-2 is an advanced version of the original ARC-AGI with significantly increased difficulty. Its test items are based on visual grids, involving spatial transformations, symmetry recognition, object counting, conditional logic combinations, and various other abstract reasoning patterns. The average untrained human can achieve 85-95% accuracy, while as of late 2025, the strongest AI models scored only in the 30-55% range on ARC-AGI-2.

From 31.1% to 77.1%: A 46 Percentage Point Leap

Gemini 3 Pro scored 31.1% on ARC-AGI-2, while Gemini 3.1 Pro pushed this to 77.1% — a net increase of 46 percentage points, a relative improvement of 148%^[5]. This is the largest single-version-iteration improvement since ARC-AGI-2 was released.

Placing this score in competitive context further reveals its significance:

Gemini 3.1 Pro leads second-place Claude Opus 4.6 by 8.3 percentage points and GPT-5.3 preview by 24.2 percentage points. This gap is extremely rare in frontier model competition — typically the differences between top models are only 1-3 percentage points. Notably, the 77.1% score means Gemini 3.1 Pro has entered the lower range of the human baseline (85%), making AI approach human-level abstract reasoning for the first time.

Technical Attribution for the Breakthrough

Google DeepMind attributes the ARC-AGI-2 breakthrough in the Model Card to three technical factors^[2]: (1) The Deep Think Mini reasoning engine's multi-step hypothesis-verification loop in HIGH mode, enabling systematic search for abstract rules; (2) The native multimodal architecture's visual grid comprehension, allowing the model to directly "see" spatial relationships rather than relying on text descriptions; (3) Enhanced few-shot generalization, enabling the model to extract high-level abstract rules from just 2-3 examples.

However, independent researchers have noted that the 77.1% ARC-AGI-2 score was achieved in HIGH mode (maximum compute budget), with per-inference costs far exceeding typical tasks. In MEDIUM mode, Gemini 3.1 Pro's ARC-AGI-2 score drops to approximately 58-62%, significantly narrowing the gap with Claude Opus 4.6. This again highlights the cost-performance tradeoffs of the three-tier reasoning architecture.

4. Comprehensive Benchmark Analysis

ARC-AGI-2 is only one dimension where Gemini 3.1 Pro shines. To comprehensively evaluate this model's capability boundaries, we need systematic analysis across multiple benchmark dimensions^[5]. The following table compiles Gemini 3.1 Pro's performance on key benchmarks, compared with Claude Opus 4.6 and OpenAI GPT-5.3.

Core Benchmark Score Comparison

Highlights Analysis

GPQA Diamond 94.3% is an impressive achievement. This test, designed by PhD-level researchers, covers high-difficulty science questions in physics, chemistry, biology, and more — many requiring careful thought even from domain experts. Gemini 3.1 Pro leads Claude Opus 4.6 by 4.6 percentage points on this item, demonstrating its advantage in deep scientific reasoning.

SWE-bench Verified 80.6% means Gemini 3.1 Pro can successfully fix over 80% of real GitHub Issues. SWE-bench is currently recognized as the benchmark most representative of "AI software engineer" practical capabilities, requiring models to understand complete codebases, locate bugs, propose fixes, and generate patches that pass tests. The growth from GPT-4's 23% in early 2024 to Gemini 3.1 Pro's 80.6% in 2026 reflects the astonishing progress of frontier models in code engineering capabilities.

BrowseComp 85.9% tests the model's comprehension and operation capabilities in complex web browsing tasks — including form filling, multi-page navigation, information extraction, and cross-referencing. Gemini 3.1 Pro's advantage on this item (leading Claude by 14.6 percentage points) may be partly attributable to Google's long-term technical accumulation in search and web comprehension.

LiveCodeBench 2887 Elo is a dynamically updated code competition benchmark with questions regularly sourced from platforms like Codeforces and LeetCode, avoiding data contamination issues with static benchmarks. An Elo of 2887 is approximately equivalent to the Candidate Master level on the Codeforces platform.

Critical Examination of Google's "13 of 16 Leading" Claim

Google claimed at its launch event that Gemini 3.1 Pro leads in 13 out of 16 benchmarks^[1]. However, independent analysis organization SmartScope pointed out several noteworthy issues^[5]:

First, the 16 benchmarks Google selected are not the industry-recognized standard test suite but a curated subset. For example, Google did not include Terminal-Bench (where GPT-5.3 clearly leads) and GDPval-AA (where GPT-5.3 leads by 289 points) in its promotional benchmark list. When we expand to the full 18 mainstream benchmarks, Gemini 3.1 Pro's "wins" drop to 12 (not 13), and its lead on 3 of those is less than 2 percentage points — which may not be statistically significant.

Second, most benchmark scores were achieved in HIGH reasoning mode, while most requests in actual enterprise deployment scenarios would use MEDIUM or even LOW mode. Comparison data in MEDIUM mode was not fully disclosed by Google.

This is not to deny Gemini 3.1 Pro's technical achievements — it is undeniably one of the strongest frontier models of February 2026 — but rather to remind enterprise readers: benchmark interpretation needs to account for test selection bias, compute budget settings, and statistical significance.

5. Technical Architecture

Gemini 3.1 Pro's architecture inherits and deepens the design philosophy consistent throughout the Gemini series: Sparse Dynamic Computation, TPU-native co-design, and natively multimodal fusion^[2].

Sparse Mixture-of-Experts

Gemini 3.1 Pro uses a Sparse MoE architecture, where each Transformer layer contains multiple "expert" sub-networks, but only a small subset is activated when processing each token. This allows the model's total parameter count to be very large (providing broader knowledge coverage) while actual inference computational cost corresponds only to the scale of activated parameters. Google DeepMind has not disclosed Gemini 3.1 Pro's exact parameter count, but industry estimates based on inference latency and throughput suggest total parameters may exceed 1 trillion (1T), with approximately 50-80B parameters activated per token.

Another advantage of the MoE architecture is Expert Specialization. Different expert sub-networks naturally differentiate during training, each responsible for different knowledge domains or capability dimensions — for example, some experts excel at mathematical reasoning, some at language generation, and some at code comprehension. The Router mechanism dynamically selects the most suitable expert combination based on input token characteristics. This mechanism forms an interesting complement with the three-tier reasoning architecture: thinkingLevel controls macro-level reasoning depth, while MoE routing controls micro-level expert selection.

TPU Co-Design

Unlike OpenAI and Anthropic which primarily rely on NVIDIA GPUs, Gemini series models have been deeply co-designed with Google's proprietary TPUs (Tensor Processing Units) from the architecture design phase. Gemini 3.1 Pro was trained on TPU v5p clusters, with hardware-level optimizations for the communication patterns of large-scale MoE models, including Inter-Chip Interconnect (ICI) topology design and All-to-All communication hardware acceleration.

The direct benefit of TPU co-design is: at equivalent inference quality, Gemini 3.1 Pro's per-token marginal cost is lower than competing models based on NVIDIA H100. This partially explains why Google can offer a model that leads on most benchmarks at $2/$12 pricing — its hardware cost structure inherently has an advantage.

Natively Multimodal Architecture

Gemini 3.1 Pro continues the "Natively Multimodal" design that has been present since Gemini 1.0 — the model was jointly trained on mixed data of text, images, audio, and video from day one, rather than training a text model first and then "grafting" a visual encoder. This architecture makes cross-modal reasoning more natural and accurate.

Specifically supported modalities include:

• Images: Supports JPEG, PNG, WebP, GIF and other formats; can handle chart analysis, OCR, visual reasoning, and other tasks
• Audio: Natively understands speech content, supporting multilingual speech recognition and speech sentiment analysis
• Video: Can process video inputs up to several hours, performing temporal understanding, action recognition, and content summarization
• PDF: Natively parses PDF document structure, preserving associations between tables, charts, and text

1M Token Context Window

Gemini 3.1 Pro's 1M (one million) token context window has officially entered GA stage^[9]. This capacity is sufficient to process approximately 750,000 English words (or approximately 500,000 Chinese characters) in a single inference, equivalent to a complete technical book or an entire day's meeting recordings. By comparison, Claude Opus 4.6's context window is 200K tokens and GPT-5.3's is 256K tokens.

The MRCR (Multi-Round Context Retrieval) benchmark validates the practical effectiveness of long context: at 128K context, Gemini 3.1 Pro achieves 96.8% retrieval accuracy, notably superior to Claude's 91.2% and GPT-5.3's 88.5%. This means that in long document analysis, large codebase comprehension, and similar scenarios, Gemini 3.1 Pro not only can accommodate more content but is also more reliable in "needle-in-a-haystack" precise retrieval.

6. Pricing and Competitive Analysis

Gemini 3.1 Pro's pricing strategy is a key pillar of its competitiveness^[6]. Google has adopted a "volume pricing" strategy, attracting enterprise customers to migrate to the Google Cloud ecosystem with unit prices significantly lower than Anthropic and OpenAI's flagship models.

Base Pricing

Gemini 3.1 Pro's input pricing ($2.00) is only 13% of Claude Opus 4.6 ($15.00), and output pricing ($12.00) is only 16% of Opus ($75.00). Even compared to the "mid-tier" Claude Sonnet 4.6, Gemini 3.1 Pro's input price is still 33% lower, with a 5x larger context window. Compared to GPT-5.3, input price is 20% and output price is 40%.

Cost Optimization Mechanisms

Beyond base pricing advantages, Google also offers several cost optimization mechanisms:

Batch API (50% discount) — For non-real-time tasks (such as batch document analysis, overnight data processing), Batch API offers a 50% price discount. Input cost drops to $1.00/million tokens, output cost to $6.00/million tokens, further expanding Gemini 3.1 Pro's cost advantage in batch processing scenarios.

Context Caching (up to 75% discount) — When multiple requests share the same system prompt or reference documents, Context Caching can dramatically reduce the cost of repeated inputs. Cached tokens are billed at 25% of the normal price ($0.50/million tokens), and caches can be shared across all requests within the same project during their TTL (time-to-live). For typical RAG systems — where each request includes the same enterprise knowledge fragments — this mechanism can reduce input costs by 60-75%.

Free Tier — Google maintains a free quota for the Gemini API: 15 requests per minute, 1 million input tokens per day, sufficient for prototyping and small-scale testing. This free quota is the most generous among the three major providers.

Total Cost of Ownership (TCO) Analysis

Using a typical enterprise AI application scenario (100,000 API calls per day, average 2,000 input tokens, average 500 output tokens, 80% using MEDIUM reasoning, 20% using HIGH reasoning):

In this simulated scenario, Gemini 3.1 Pro's monthly cost is approximately 12% of Claude Opus 4.6 and 27% of GPT-5.3. Even considering Claude Sonnet 4.6 as an alternative (monthly cost approximately $27,000), Gemini 3.1 Pro still has approximately a 10% cost advantage, while providing a larger context window and higher benchmark scores.

7. Enterprise Deployment Practices

Model capabilities and pricing are only half the enterprise decision equation. The other half — often overlooked in technical articles — is deployment architecture, compliance requirements, and operational stability^[9].

Vertex AI Regionalized Endpoints

Google Cloud's Vertex AI is the primary pathway for enterprise deployment of Gemini 3.1 Pro. Unlike Google AI Studio (a direct API for developers), Vertex AI provides enterprise-grade security, compliance, and management capabilities. As of February 2026, Gemini 3.1 Pro is available in the following Vertex AI regions:

• Asia Pacific: Tokyo (asia-northeast1), Singapore (asia-southeast1), Sydney (australia-southeast1)
• Americas: US East (us-east1, us-east4), US Central (us-central1), US West (us-west1)
• Europe: London (europe-west2), Frankfurt (europe-west3), Netherlands (europe-west4)

Data Residency

For enterprises, data residency is a critical compliance consideration when choosing cloud AI services^[7]. Vertex AI's data residency guarantees encompass the following levels:

Data-at-rest residency — User-uploaded training data, fine-tuned model weights, evaluation results, and other static data are stored in the Google Cloud region selected by the user, without cross-region replication. The nearest options for enterprises are the Tokyo or Singapore regions.

Inference data processing — API requests (input prompts and output responses) are processed on user-specified regional endpoints. Enterprises selecting the asia-northeast1 (Tokyo) endpoint will have their data remain within Tokyo data centers during inference. However, it should be noted that Google's internal model serving architecture may involve cross-region load balancing — Google commits in the Model Card that "inference data will not persist outside the selected region," but the details of transient data flows during inference have not been fully disclosed^[7].

Gemini Enterprise Plan

For large enterprise customers, Google Cloud offers the Gemini Enterprise plan^[9], which includes:

• Dedicated Model Endpoints: Independent inference infrastructure, avoiding multi-tenant model performance fluctuations
• Advanced SLA: 99.9% availability guarantee (standard Vertex AI is 99.5%)
• Priority Access: 2-4 week early access to new model versions
• Custom Fine-Tuning: Parameter fine-tuning of Gemini 3.1 Pro using enterprise private data on Vertex AI
• Model Version Pinning: Ability to specify use of a specific date's model snapshot, avoiding behavioral changes from automatic updates

Custom Tools Endpoint

Gemini 3.1 Pro offers a customtools endpoint on Vertex AI, allowing enterprises to register internal APIs as tools within the model's reasoning workflow. The model can autonomously call these tools during reasoning — such as querying enterprise CRM systems, retrieving knowledge bases, performing calculations — enabling true Agent-style workflows. This functionality is similar to Anthropic's Tool Use and OpenAI's Function Calling, but Google's implementation advantage lies in deep native integration with Google Cloud services (BigQuery, Cloud Functions, Pub/Sub).

Rate Limits and Quotas

Note that since HIGH reasoning mode consumes far more tokens per request than LOW/MEDIUM, effective RPM varies by reasoning mode. A request consuming 20,000 thinking tokens in HIGH mode would use 0.5% of the 4M TPM quota, meaning in HIGH mode, a maximum of approximately 200 complex reasoning requests per minute can be processed (assuming 20K thinking tokens + 2K input + 500 output per request).

8. Limitations and Risks

Despite Gemini 3.1 Pro's outstanding performance across most dimensions, any responsible technical evaluation must squarely address its limitations. Below are the main weaknesses and risks identified through our actual testing and third-party analysis.

GDPval-AA Assessment: A 289-Point Trust Gap

GDPval-AA (General-Domain Preference Validation - Adversarial Accuracy) is a comprehensive trustworthiness evaluation framework developed by Artificial Analysis^[10], measuring overall reliability across dimensions including factual consistency, hallucination rate, self-contradiction rate, and safety boundary compliance. Gemini 3.1 Pro scored 1,411 on GDPval-AA, trailing GPT-5.3's 1,700 by 289 points and also falling below Claude Opus 4.6's 1,523.

The practical implication of this gap is: in scenarios requiring high factual reliability (such as legal consulting, medical information, financial reporting), Gemini 3.1 Pro's hallucination risk may be higher than its competitors. Enterprises should consider additional fact-verification mechanisms in such scenarios, or cross-validate Gemini 3.1 Pro outputs using Claude Opus 4.6.

Terminal-Bench: A Shortcoming in System Operations

Terminal-Bench measures a model's ability to execute system administration, DevOps, and infrastructure operation tasks in terminal environments. GPT-5.3's 51.2% clearly leads Gemini 3.1 Pro's 44.7%. This means that in scenarios where AI Agents need to directly operate servers, execute shell commands, or manage containers, GPT-5.3 is currently the more reliable choice.

This weakness may be related to Gemini's training data distribution — Google's training data may have a higher proportion of web content and academic literature, with relatively fewer terminal operation examples. As Gemini CLI (Google's newly released command-line tool) brings more terminal interaction data, this gap is expected to narrow in subsequent versions.

Implicit Risks of "Preview" Status

As of February 25, 2026, Gemini 3.1 Pro remains in "Preview" status for some features. According to Google Cloud's classification, Preview means: (1) API behavior may change without notice; (2) SLA guarantees are not provided (except for Enterprise Tier); (3) it is not recommended for production critical paths.

Specifically, the following features are still in Preview:

• Behavioral stability of Deep Think Mini's HIGH mode in certain edge cases
• Ultra-long context reasoning exceeding 500K tokens
• Video input processing exceeding 2 hours
• Thought Signatures encryption format (may change in the official version)

Enterprises deploying Gemini 3.1 Pro at this stage should establish model behavior monitoring mechanisms and prepare strategies for rapid response when model updates cause behavioral changes — such as maintaining model version pinning, or keeping a backup model (such as Claude Sonnet 4.6) as a fallback.

Structural Issues with Benchmark Selection Bias

As discussed earlier, Google selectively emphasized the benchmarks where Gemini 3.1 Pro performs best when promoting it^[5]. This is not unique to Google — OpenAI and Anthropic similarly cherry-pick favorable benchmarks when releasing models. But the important reminder for enterprise customers is: never base procurement decisions solely on the benchmark leaderboard self-selected by the vendor.

Meta Intelligence's recommendation is: enterprises should build internal evaluation suites on their own actual task data, measuring model performance in their specific business scenarios. A model leading by 5 percentage points on GPQA Diamond does not mean it also leads by 5 percentage points on your customer service conversation quality score. Benchmarks are the starting point for screening; internal evaluation is the endpoint for decisions.