GPT-5.2 vs Gemini 3: Complete Benchmark Analysis for AI Infrastructure Teams

One hundred percent. That perfect score on AIME 2025 marks the first time a major language model has exhausted a competition-level math benchmark without external tools ¹. OpenAI's GPT-5.2 achieved the milestone in December 2025, while Google's Gemini 3 Pro matched it with code execution enabled, setting the stage for a frontier model competition that reshapes enterprise AI infrastructure decisions in 2026 ².

TL;DR

GPT-5.2 and Gemini 3 Pro represent the frontier of commercial AI capabilities in February 2026. GPT-5.2 leads in mathematical reasoning (100% AIME), multi-language coding (55.6% SWE-Bench Pro), and hallucination reduction (6.2% rate). Gemini 3 Pro dominates multimodal processing and long-context applications with its 1M token context window and 45.1% ARC-AGI-2 score in Deep Think mode. Claude Opus 4.5 holds the coding crown at 80.9% SWE-bench Verified. OpenAI's new GPT-oss open-weight models under Apache 2.0 license signal a strategic shift toward open-source competition.

Model Specifications Compared

The February 2026 frontier model landscape offers distinct architectural choices for different workload profiles ³.

Context Window and Token Handling

Gemini 3 Pro's 1M token context window represents a 2.5x advantage over GPT-5.2, enabling processing of entire codebases, lengthy documents, or extended conversation histories in single inference calls ⁴. GPT-5.2 compensates with superior context accuracy, maintaining near-100% retrieval accuracy across its full 400K window, compared to degradation observed in previous model generations ⁵.

Reasoning Capabilities

GPT-5.2 leads on raw mathematical reasoning without tool assistance, achieving the first perfect AIME score through pure model capability ⁶. Gemini 3 Pro's Deep Think mode delivers superior performance on complex science questions, evaluating multiple hypotheses simultaneously and synthesizing insights across parallel reasoning chains ⁷.

Coding Performance Analysis

Coding benchmarks reveal nuanced performance differences depending on task complexity and language coverage ⁸.

SWE-Bench Results

The SWE-Bench Pro results prove particularly illuminating. GPT-5.2's 55.6% score confirms superior capability on complex, multi-file engineering tasks across four programming languages, surpassing Gemini's 43.3% by a significant margin ⁹.

However, Gemini 3 Flash delivers a surprising result: 78% on SWE-bench Verified, outperforming both Gemini 3 Pro (76.2%) and matching or exceeding GPT-5.2 on this specific benchmark ¹⁰. Google achieved this performance while delivering inference at less than a quarter the cost of Gemini 3 Pro and running 3x faster.

Claude Opus 4.5 maintains the coding crown at 80.9% on SWE-bench Verified, proving particularly reliable for agentic coding workflows where implementation consistency matters more than raw benchmark scores ¹¹.

Code Quality Assessment

Independent code quality analysis from Sonar reveals additional performance characteristics across production workloads ¹²:

GPT-5.2's "High" reasoning mode produces consistently lower defect rates across all categories, though the cost premium of extended reasoning tokens impacts total cost of ownership for high-volume applications.

Hallucination and Accuracy

Hallucination reduction represents a critical enterprise concern, with GPT-5.2 claiming significant improvements over previous generations ¹³.

Reported Hallucination Rates

OpenAI reports a 30% hallucination reduction from 8.8% in GPT-5.1 to 6.2% in GPT-5.2 ¹⁴. Independent testing by Vectara found an 8.4% rate, trailing DeepSeek's 6.3% ¹⁵. The variance between reported and measured rates suggests benchmark methodology significantly impacts results.

Context Accuracy

GPT-5.2 demonstrates dramatic improvements in context utilization ¹⁶:

• GPT-5.1: Accuracy degraded from 90% at 8K tokens to below 50% at 256K tokens
• GPT-5.2: Near-100% accuracy maintained across the full context window
• Four-Needle Challenge: First model achieving near-perfect accuracy recalling four specific facts across 200,000 words

The context accuracy improvement addresses a longstanding limitation of large context windows, where models struggled to retrieve information from the middle of long inputs.

Multimodal and Vision Capabilities

Gemini 3 Pro leads decisively in multimodal processing, a core architectural advantage from Google's training approach ¹⁷.

Vision Performance

Gemini 3's multimodal capabilities extend to video understanding and state-of-the-art spatial reasoning, enabling applications like architectural analysis, manufacturing quality inspection, and medical imaging interpretation that remain challenging for text-primary models ¹⁸.

Pricing and Cost Analysis

Enterprise deployment requires understanding total cost of ownership across different usage patterns ¹⁹.

API Pricing Comparison

GPT-5.2 pricing represents approximately a 40% increase over GPT-5.1 base rates ²⁰. The 90% discount on cached input tokens offers significant savings for applications with repetitive context, reducing costs to just $0.18 per million tokens.

Gemini 3 Flash emerges as the cost-efficiency leader, achieving 78% SWE-bench Verified at less than 5% of Gemini 3 Pro's cost while maintaining faster response times ²¹.

Reasoning Token Costs

GPT-5.2's "Thinking" models generate internal reasoning tokens billed at output rates ($14/1M), substantially increasing costs for complex queries requiring extensive reasoning chains ²². A query generating 10,000 reasoning tokens adds $0.14 to each inference call.

OpenAI's Open-Weight Pivot

OpenAI's release of GPT-oss models under Apache 2.0 license signals a strategic shift toward open-source competition ²³.

GPT-oss Model Specifications

The Apache 2.0 license enables commercial use, modification, and redistribution without copyleft restrictions or patent risk ²⁴. Organizations can download weights, run on private infrastructure, and fine-tune for specific domains.

GPT-oss-120b outperforms OpenAI's o3-mini and matches or exceeds o4-mini on competition coding, general problem solving, tool calling, and health-related queries ²⁵. The models support deployment on vLLM, Ollama, and llama.cpp inference stacks.

Infrastructure Implications

For organizations planning AI infrastructure investments, the frontier model landscape presents several strategic considerations.

Compute Requirements

Self-hosted GPT-oss deployment requires significant GPU infrastructure, but eliminates per-token API costs and enables complete data sovereignty ²⁶. Organizations processing millions of tokens daily may achieve cost parity within months.

Model Selection Framework

Strategic model selection depends on workload characteristics:

Choose GPT-5.2 when:

• Mathematical reasoning dominates requirements
• Multi-language coding across Python, JavaScript, TypeScript, and Go
• Hallucination reduction proves critical for compliance
• Context accuracy matters more than context length

Choose Gemini 3 Pro when:

• Document processing exceeds 400K tokens
• Video understanding or spatial reasoning required
• Multimodal applications drive primary use cases
• Cost optimization for high-volume inference

Choose Gemini 3 Flash when:

• Coding assistance at scale
• Cost-sensitive applications
• Latency-critical deployments
• Everyday tasks with simpler reasoning needs

Choose Claude Opus 4.5 when:

• Production code generation requiring reliability
• Agentic workflows with tool use
• Long-form content generation
• Applications requiring nuanced instruction-following

Choose GPT-oss for self-hosting when:

• Data sovereignty requirements prohibit cloud APIs
• Token volume justifies infrastructure investment
• Fine-tuning for specific domains required
• Regulatory compliance demands on-premises deployment

Competitive Dynamics

The frontier model race intensifies with Chinese competitors achieving notable benchmarks ²⁷.

Global Competition

Kimi K2.5 delivers unparalleled autonomous task handling with integrated video generation ²⁸. Alibaba's Qwen3-Max-Thinking achieved benchmark leadership on challenging exam-based evaluations. DeepSeek V3.2 offers the lowest measured hallucination rate while maintaining competitive pricing.

Model Routing Strategies

Enterprise deployments increasingly adopt model routing to optimize cost and capability ²⁹:

Sophisticated orchestration layers route requests based on query complexity, cost constraints, and latency requirements, achieving 60-80% cost reduction compared to single-model deployments ³⁰.

Key Takeaways

For Infrastructure Planners

The 2026 frontier models require strategic planning around context window requirements (400K vs 1M), self-hosting capabilities (GPT-oss), and model routing infrastructure. Organizations should evaluate workload patterns before committing to single-vendor strategies.

For Operations Teams

Gemini 3 Flash's 78% SWE-bench at 3x faster inference and <25% cost challenges assumptions about flagship model requirements. Evaluate whether production workloads actually require Pro-tier capabilities or can benefit from Flash-tier efficiency.

For Strategic Decision-Makers

OpenAI's GPT-oss release fundamentally changes the build-versus-buy calculus for organizations processing high token volumes. The Apache 2.0 license enables new deployment patterns previously impossible with API-only access. Consider hybrid strategies combining cloud APIs for burst capacity with self-hosted models for baseline workloads.

References