Executive Summary
On December 11, 2025, OpenAI launched GPT-5.2 in direct response to Google's Gemini 3 Pro, which had briefly seized the AI performance crown in late November 2025. This comprehensive comparison analyzes real benchmark data across coding, reasoning, scientific knowledge, multimodal capabilities, and professional knowledge work to determine which model leads in different domains.
Key Takeaways:
- GPT-5.2 outperforms Gemini 3 Pro in coding, professional knowledge work, and abstract reasoning
- Gemini 3 Pro maintains advantages in multimodal tasks and context length
- GPT-5.2 achieved expert-level performance on 70.9% of professional tasks vs Gemini 3 Pro's 53.3%
- Both models are now considered neck-and-neck in overall capabilities, with specific strengths in different areas
The “Code Red” Context
OpenAI's rapid release came after CEO Sam Altman issued an internal “Code Red” directive following Gemini 3 Pro's strong performance on LMArena leaderboards and other benchmarks. The emergency mobilization accelerated GPT-5.2's development, which arrived less than one month after GPT-5.1 (released November 12, 2025).
Comprehensive Benchmark Comparison Tables
Table 1: Professional Knowledge Work Performance
The GDPval benchmark measures performance on well-specified knowledge work tasks across 44 occupations, including spreadsheet creation, document drafting, and presentation building.
| Model | GDPval Score | Performance vs Experts | Speed Advantage | Cost Advantage |
|---|---|---|---|---|
| GPT-5.2 Thinking | 70.9% | Beats/ties 70.9% of time | 11x faster | <1% of cost |
| Claude Opus 4.5 | 59.6% | Beats/ties 59.6% of time | Not disclosed | Not disclosed |
| Gemini 3 Pro | 53.3% | Beats/ties 53.3% of time | Not disclosed | Not disclosed |
| GPT-5 | 38.8% | Beats/ties 38.8% of time | — | — |
Winner: GPT-5.2 – Achieves first-ever expert-level performance on professional knowledge work tasks with 17.6 percentage point lead over Gemini 3 Pro.
Table 2: Software Engineering & Coding Benchmarks
| Benchmark | GPT-5.2 Thinking | Gemini 3 Pro | Claude Opus 4.5 | GPT-5.1 Thinking |
|---|---|---|---|---|
| SWE-Bench Pro | 55.6% | 43.3% | 52.0% | 50.8% |
| SWE-Bench Verified | 80.0% | Not disclosed | 80.9% | 76.3% |
| Terminal-bench 2.0 | Not disclosed | Not disclosed | 59.3% | Not disclosed |
Analysis:
- GPT-5.2 leads Gemini 3 Pro by 12.3 percentage points on SWE-Bench Pro
- Claude Opus 4.5 maintains slight edge on SWE-Bench Verified (80.9% vs 80.0%)
- GPT-5.2 improved 4.8 points over its predecessor on SWE-Bench Pro
- Anthropic leads in command-line coding proficiency (Terminal-bench 2.0)
Winner: GPT-5.2 vs Gemini 3 Pro – Clear advantage in real-world software engineering tasks
Table 3: Abstract Reasoning & Logic
Abstract reasoning measures fluid intelligence and novel problem-solving without relying on memorization.
| Benchmark | GPT-5.2 Thinking | GPT-5.2 Pro | Gemini 3 Pro | Gemini 3 Deep Think | Claude Opus 4.5 | GPT-5.1 |
|---|---|---|---|---|---|---|
| ARC-AGI-2 | 52.9% | 54.2% | 31.1% | 45.1% | 37.6% | 17.6% |
| ARC-AGI-1 | 86.2% | 90.5% | 75.0% | Not disclosed | Not disclosed | Not disclosed |
| Humanity's Last Exam | Not disclosed | Not disclosed | 37.5% | 41.0% | Not disclosed | 26.5% |
Key Insights:
- GPT-5.2 Pro achieved 90.5% on ARC-AGI-1, the first model to cross 90% threshold
- GPT-5.2 Thinking improved 200% over GPT-5.1 on ARC-AGI-2 (52.9% vs 17.6%)
- GPT-5.2 surpasses Gemini 3 Pro by 21.8 points on ARC-AGI-2
- Gemini 3 Deep Think leads on Humanity's Last Exam (41.0% without tools)
Winner: GPT-5.2 – Dramatic breakthrough in abstract reasoning, especially on ARC-AGI benchmarks
Table 4: Mathematical Reasoning
| Benchmark | GPT-5.2 Thinking | GPT-5.2 Pro | Gemini 3 Pro (with tools) | GPT-5.1 | Details |
|---|---|---|---|---|---|
| AIME 2025 | 100% | 100% | 100% | 94% | Competition mathematics (30 problems) |
| FrontierMath | 40.3% | Not disclosed | Not disclosed | 31.0% | Research-level mathematics |
| FrontierMath (Tier 1-4) | 14.6% | Not disclosed | 18.8% | Not disclosed | Hardest tier problems |
Analysis:
- GPT-5.2 achieved perfect 100% on AIME 2025 without tools, matching Gemini 3 Pro's performance with code execution enabled
- 9.3 percentage point improvement over GPT-5.1 on FrontierMath
- Gemini 3 Pro maintains slight edge on hardest tier problems (18.8% vs 14.6%)
- Both models show exceptional mathematical reasoning capability
Winner: Tie – Both achieve perfect AIME scores, with trade-offs at highest difficulty levels
Table 5: Graduate-Level Scientific Knowledge
GPQA Diamond tests PhD-level scientific understanding across physics, chemistry, and biology.
| Model | GPQA Diamond Score | Improvement vs Previous |
|---|---|---|
| GPT-5.2 Pro | 93.2% | +5.1% vs GPT-5.1 |
| Gemini 3 Deep Think | 93.8% | — |
| GPT-5.2 Thinking | 92.4% | +4.3% vs GPT-5.1 |
| Gemini 3 Pro | 91.9% | — |
| GPT-5.1 Thinking | 88.1% | — |
| Claude Opus 4.5 | 87.0% | — |
Analysis:
- Gemini 3 Deep Think holds slight lead (93.8%)
- GPT-5.2 Pro nearly matches at 93.2% (0.6 point difference)
- GPT-5.2 Thinking surpasses Gemini 3 Pro by 0.5 points (92.4% vs 91.9%)
- Essentially tied performance at the highest level of scientific reasoning
Winner: Virtually Tied – Margin of difference negligible at this performance level
Table 6: Visual & Multimodal Understanding
| Benchmark | GPT-5.2 | Gemini 3 Pro | GPT-5.1 | Focus Area |
|---|---|---|---|---|
| CharXiv Reasoning | 88.7% | 81.4% | 80.3% | Scientific diagram interpretation |
| ScreenSpot-Pro | 86.3% | Not disclosed | 64.2% | UI element understanding |
| MMMU-Pro | ~76% | 81.0% | 76% | Multi-modal understanding |
| Video-MMMU | Not disclosed | 87.6% | Not disclosed | Video understanding |
Analysis:
- GPT-5.2 leads in scientific figure interpretation (+7.3 points over Gemini)
- GPT-5.2 shows dramatic 22.1 point improvement in UI understanding
- Gemini 3 Pro maintains advantage in comprehensive multimodal benchmarks
- Gemini excels particularly in video understanding with unified architecture
Winner: Split Decision
- GPT-5.2: Static visual reasoning and diagram analysis
- Gemini 3 Pro: Comprehensive multimodal (especially video/audio)
Table 7: Tool Use & Agentic Performance
| Benchmark | GPT-5.2 Thinking | GPT-5.1 Thinking | Gemini 3 Pro | Description |
|---|---|---|---|---|
| Tau2-bench-Telecom | 98.7% | 95.6% | Not disclosed | Multi-tool customer service scenarios |
| 4-Needle MRCR (256K tokens) | ~100% | Not disclosed | Not disclosed | Long-context information retrieval |
| Vending-Bench 2 | Not disclosed | $2,021 net worth | $5,478 (+272%) | Year-long agentic simulation |
Key Insights:
- GPT-5.2 achieved near-perfect tool calling accuracy (98.7%)
- First model to reach ~100% on 4-Needle test at 256,000 tokens
- Gemini 3 Pro demonstrated superior long-horizon planning on Vending-Bench 2
- Gemini's 272% higher net worth indicates better sustained decision-making
Winner: Mixed Results
- GPT-5.2: Tool calling precision and long-context retrieval
- Gemini 3 Pro: Long-horizon agentic planning and consistency
Table 8: Error Rates & Reliability
| Metric | GPT-5.2 Thinking | GPT-5.1 Thinking | Improvement |
|---|---|---|---|
| Responses with ≥1 Error | 6.2% | 8.8% | -30% |
| Error Rate Reduction | — | Baseline | 38% fewer errors overall |
| Hallucination Frequency | Lower | Baseline | 30% reduction |
Analysis:
- GPT-5.2 produces significantly more reliable outputs
- 30% reduction in error-containing responses
- Particularly important for professional decision-making and research applications
- Makes model “more dependable for everyday knowledge work”
Winner: GPT-5.2 – Substantial reliability improvements over predecessor
Table 9: Context Window & Processing Capacity
| Feature | GPT-5.2 | Gemini 3 Pro | Advantage |
|---|---|---|---|
| Context Window | 400,000 tokens | 1,000,000 tokens | Gemini (+150%) |
| Max Output | 128,000 tokens | Not disclosed | Likely similar |
| Context Quality | Improved coherence | Standard | GPT-5.2 |
| Knowledge Cutoff | August 31, 2025 | Not disclosed | GPT-5.2 (recency) |
Analysis:
- Gemini 3 Pro can process 2.5x more content in single request
- Gemini's 1M token window can handle entire books or massive codebases
- GPT-5.2 focuses on better utilization of existing context
- GPT-5.2 less prone to “losing track” in long conversations
Winner: Gemini 3 Pro – Significantly larger context window for document-heavy workflows
Table 10: API Pricing Comparison (Per Million Tokens)
| Model Tier | Input Cost | Output Cost | vs Previous Generation |
|---|---|---|---|
| GPT-5.2 Thinking | $1.75 | $14 | +40% vs GPT-5.1 |
| GPT-5.2 Pro | $21 | $168 | +40% vs GPT-5 Pro |
| Gemini 3 Pro | $2.00 | $12 | — |
| Claude Opus 4.5 | $5.00 | $25 | — |
| GPT-5.1 Thinking | $1.25 | $10 | Reference |
Cost Analysis:
- GPT-5.2 Thinking slightly cheaper than Gemini 3 Pro on input (-12.5%)
- GPT-5.2 more expensive on output vs Gemini (+16.7%)
- Both significantly cheaper than Claude Opus 4.5
- 40% price increase justified by 30% error reduction and higher quality
- Cached inputs receive 90% discount for both models
Winner: Gemini 3 Pro – Better pricing, especially for output-heavy applications
Head-to-Head: Strengths & Weaknesses
GPT-5.2 Strengths
- Professional Knowledge Work – Industry-leading 70.9% on GDPval benchmark
- Coding Excellence – 55.6% on SWE-Bench Pro vs Gemini's 43.3%
- Abstract Reasoning – Breakthrough 52.9% on ARC-AGI-2, 21.8 points ahead
- Reliability – 30% fewer errors than predecessor, 38% overall error reduction
- Tool Calling – Near-perfect 98.7% accuracy on complex multi-tool scenarios
- Long Context Retrieval – First to achieve ~100% on 4-Needle MRCR at 256K tokens
- Scientific Diagrams – 88.7% on CharXiv vs Gemini's 81.4%
- Speed – Delivers results 11x faster than human experts on knowledge work
GPT-5.2 Weaknesses
- Multimodal Breadth – Weaker on comprehensive multimodal benchmarks (76% vs 81%)
- Context Window – 400K tokens vs Gemini's 1M tokens (60% smaller)
- Video Understanding – No unified video architecture like Gemini
- Long-Horizon Planning – Lower performance on Vending-Bench 2 agentic simulation
- API Pricing – 40% more expensive than GPT-5.1, slightly higher output costs vs Gemini
- Image Generation – No improvements announced; still uses DALL-E 3
- Hardest Math – Trails Gemini on FrontierMath Tier 1-4 (14.6% vs 18.8%)
Gemini 3 Pro Strengths
- Multimodal Architecture – Unified handling of text, images, audio, video
- Context Window – 1 million tokens can process entire books/repositories
- MMMU-Pro – 81.0% vs GPT's ~76% in comprehensive multimodal understanding
- Video Analysis – 87.6% on Video-MMMU with temporal reasoning
- Long-Horizon Agents – 272% higher net worth on Vending-Bench 2
- Pricing – Competitive $2/$12 per million tokens
- Google Integration – Seamless across Google Cloud, Maps, BigQuery via MCP
- Scientific Knowledge – 93.8% with Deep Think mode (highest available)
- Humanity's Last Exam – 41.0% without tools, leading on hardest reasoning test
Gemini 3 Pro Weaknesses
- Professional Tasks – 53.3% vs GPT-5.2's 70.9% on GDPval (17.6 point gap)
- Coding – 43.3% on SWE-Bench Pro vs GPT-5.2's 55.6% (12.3 point deficit)
- Abstract Reasoning – 31.1% on ARC-AGI-2 vs GPT-5.2's 52.9% (21.8 point gap)
- Market Perception – Lost top LMArena position to GPT-5.2 release
- Tool Calling Precision – No comparable public benchmarks to GPT's 98.7%
- UI Understanding – Weaker on ScreenSpot-Pro tasks
Use Case Recommendations
Choose GPT-5.2 Thinking When:
✅ Coding & Software Development – Superior performance on real-world engineering tasks
✅ Professional Knowledge Work – Spreadsheets, presentations, complex document creation
✅ Abstract Problem-Solving – Novel challenges requiring fluid intelligence
✅ Tool-Heavy Workflows – Applications requiring precise multi-tool orchestration
✅ Error-Sensitive Applications – Research, analysis, decision support where reliability critical
✅ Long-Context Information Retrieval – Finding specific information in 200K+ token documents
✅ Scientific Figure Analysis – Interpreting complex diagrams, charts, technical illustrations
Choose Gemini 3 Pro When:
✅ Multimodal Projects – Heavy use of images, audio, video alongside text
✅ Massive Documents – Processing entire books, large codebases, extensive research papers
✅ Video Analysis – Understanding temporal sequences, visual narratives
✅ Long-Horizon Agents – Tasks requiring sustained decision-making over extended periods
✅ Google Ecosystem – Deep integration with Google Cloud services needed
✅ Cost-Sensitive Deployments – Lower pricing for high-volume output generation
✅ Graduate-Level Science – Maximum scientific knowledge (93.8% with Deep Think)
✅ Extreme Reasoning – Humanity's Last Exam-type challenges (41% without tools)
Model Variants Explained
GPT-5.2 Variants
GPT-5.2 Instant
- Optimized for: Speed, information retrieval, how-tos, study guides
- Use cases: Quick questions, translations, skill-building
- Latency: ~40% faster than Thinking mode
- Best for: Everyday work and learning
GPT-5.2 Thinking
- Optimized for: Complex reasoning, professional tasks
- Use cases: Coding, document analysis, multi-step projects
- Performance: Featured in most benchmarks
- Best for: Professional knowledge work
GPT-5.2 Pro
- Optimized for: Maximum accuracy and reliability
- Use cases: Mission-critical programming, research
- Performance: Highest scores on most benchmarks
- Best for: Domains requiring utmost precision
Gemini 3 Modes
Gemini 3 Pro (Standard)
- Standard reasoning and processing
- Featured in most benchmark comparisons
- Balanced speed and capability
Gemini 3 Deep Think
- Extended reasoning time for complex problems
- Achieves highest scores on science and reasoning
- Trades speed for maximum accuracy
Real-World Performance Insights
Enterprise Feedback: GPT-5.2
Data Science Platforms
- Databricks, Hex, Triple Whale: “Exceptional at agentic data science”
- 40% faster document information extraction (Box)
- 40% boost in reasoning accuracy for Life Sciences (Box)
Coding Tools
- Cognition, Warp, Charlie Labs, JetBrains: “State-of-the-art agentic coding”
- Measurable improvements in interactive coding and bug finding
- Better at multi-step code refactoring
Knowledge Management
- Notion, Shopify, Harvey, Zoom: “State-of-the-art long-horizon reasoning”
- Improved tool-calling performance across platforms
Enterprise Feedback: Gemini 3 Pro
Google Ecosystem Integration
- Seamless MCP servers for Maps, BigQuery
- Better than GPT for automated presentation generation (Google Labs Mixboard)
- Native integration across Google Workspace
Multimodal Workflows
- Superior for video analysis and visual interpretation
- Better text rendering in generated images
- Stronger performance on image-heavy documents
Independent Verification Status
Important Context: Most benchmarks in this comparison are vendor-reported. Independent verification is ongoing as of December 2025. Key considerations:
- OpenAI Benchmarks – GDPval is OpenAI's proprietary benchmark
- Google Benchmarks – Some Gemini scores use Deep Think mode vs standard comparisons
- Contamination Risk – Both models may have been optimized for public benchmarks
- Real-World Performance – May differ from controlled benchmark conditions
Third-party evaluations from LMArena, Humanity's Last Exam, and other independent sources show both models performing at similar levels, with specific advantages in different domains.
Technical Specifications Comparison
| Specification | GPT-5.2 | Gemini 3 Pro |
|---|---|---|
| Architecture | Transformer-based with reasoning tokens | Unified multimodal transformer |
| Context Window | 400,000 tokens | 1,000,000 tokens |
| Max Output | 128,000 tokens | Not disclosed |
| Modalities | Text, images | Text, images, audio, video |
| Knowledge Cutoff | August 31, 2025 | Not publicly disclosed |
| Reasoning Mode | Yes (Thinking mode) | Yes (Deep Think mode) |
| Release Date | December 11, 2025 | Mid-November 2025 |
| Pretraining Improvements | Confirmed | Confirmed |
| Post-training Improvements | Confirmed | Confirmed |
Competitive Landscape Analysis
Market Position December 2025
Before GPT-5.2 Release:
- Gemini 3 Pro – Leading on LMArena leaderboard
- Claude Opus 4.5 – Strong on coding (SWE-Bench Verified)
- GPT-5.1 – Sixth place on LMArena
- Grok 3 (xAI) – Competitive in select benchmarks
After GPT-5.2 Release:
- GPT-5.2 reclaimed performance leadership in most categories
- Three-way competition between OpenAI, Google, Anthropic
- Each company leapfrogging others every few months
- No single clear winner across all domains
Development Velocity
Release Timeline:
- GPT-5: August 7, 2025
- GPT-5.1: November 12, 2025 (3 months later)
- Gemini 3 Pro: Mid-November 2025
- Claude Opus 4.5: November 24, 2025
- GPT-5.2: December 11, 2025 (less than 1 month after 5.1)
This unprecedented pace suggests:
- Intense competitive pressure driving rapid iteration
- Significant remaining room for AI capability improvements
- High compute and development costs
- Market leaders pushing boundaries simultaneously
Future Outlook
OpenAI Roadmap
- Project Garlic: More fundamental architectural shift targeting early 2026
- Image Generation: Improvements promised but not in GPT-5.2
- Consumer Features: Better personality, speed improvements expected January 2026
- Safety: Enhanced mental health response and teen age verification
Google Gemini Development
- Nano Banana Pro: Enhanced image generation already released
- Google Integration: Continued deepening across product ecosystem
- MCP Servers: Expanding agent connectivity to Google services
- Multimodal Leadership: Likely to maintain video/audio advantage
Competitive Dynamics
- Models now updated every 3-6 weeks at frontier
- $1.4+ trillion infrastructure investments from OpenAI
- Google leveraging existing cloud infrastructure
- Anthropic focusing on safety and coding excellence
- Winner depends on specific deployment needs, not universal superiority
Conclusion: Which Model Wins?
The Verdict: Context-Dependent Tie
Neither GPT-5.2 nor Gemini 3 Pro can claim universal superiority. The choice depends entirely on your specific use case:
GPT-5.2 Wins For:
- Professional knowledge workers needing maximum reliability
- Software engineers requiring best-in-class coding assistance
- Applications demanding abstract reasoning and novel problem-solving
- Users prioritizing error reduction and factual accuracy
- Tool-heavy workflows requiring precise orchestration
Gemini 3 Pro Wins For:
- Multimodal applications involving video, audio, images
- Processing massive documents (entire books, large codebases)
- Long-horizon agentic tasks requiring sustained planning
- Google Cloud ecosystem integration
- Cost-conscious deployments with high output volume
Both Excel At:
- Graduate-level scientific reasoning (93%+ performance)
- Competition mathematics (100% AIME 2025)
- Complex professional tasks
- Multi-step logical reasoning
Bottom Line: GPT-5.2's December 2025 release successfully recaptured performance leadership from Gemini 3 Pro across coding, professional work, and abstract reasoning. However, Gemini maintains clear advantages in multimodal capabilities and context length. The AI arms race continues, with users benefiting from rapid improvements both companies are delivering.
For most enterprise applications, evaluate both models against your specific workload before committing. The 12-point coding advantage, 17-point professional work lead, and 30% error reduction make GPT-5.2 the current front-runner for text-based knowledge work, while Gemini 3 Pro remains superior for multimedia and massive-document applications.
Frequently Asked Questions
Q: Is GPT-5.2 always better than Gemini 3 Pro?
A: No. GPT-5.2 leads in coding, professional work, and abstract reasoning. Gemini 3 Pro excels at multimodal tasks, video understanding, and processing very large documents.
Q: How much does each model cost?
A: GPT-5.2 Thinking costs $1.75/$14 per million input/output tokens. Gemini 3 Pro costs $2.00/$12 per million tokens. For most use cases, pricing is comparable.
Q: Which model is faster?
A: Both offer fast response times. GPT-5.2 claims 11x faster than human experts on knowledge work. Gemini 3 also emphasizes low latency. Real-world speed depends on task complexity.
Q: Can I use both models?
A: Yes. Many organizations use GPT-5.2 for coding/analysis and Gemini 3 for multimodal workflows, selecting the best tool for each task.
Q: What about Claude Opus 4.5?
A: Claude Opus 4.5 leads on SWE-Bench Verified (80.9%), Terminal-bench 2.0 (59.3%), and prompt injection resistance. It's the most expensive option but excels at specific coding tasks.
Q: Will these models improve further?
A: Yes. OpenAI plans Project Garlic for early 2026. Google continues enhancing Gemini. Expect major updates every 1-2 months given current competitive intensity.
Q: Which model is more reliable?
A: GPT-5.2 shows 30% fewer error-containing responses vs GPT-5.1. Specific reliability comparisons with Gemini 3 Pro await independent verification.
Q: Does model choice affect business outcomes?
A: Yes, significantly. Box reported 40% faster document processing and 40% higher reasoning accuracy in healthcare applications using GPT-5.2. Choose based on your specific metrics.
Last Updated: December 12, 2025 | All benchmark data from vendor announcements and third-party evaluations | Independent verification ongoing
