ChatGPT (GPT-5.2) vs Gemini 3.0 Pro 2026
OpenAI makes ChatGPT. Google makes Gemini. Neither is named the winner.
Deciding factor: Neither product is named the winner.
Key fact: OpenAI makes ChatGPT. Google makes Gemini. Neither is named the winner.
Choose ChatGPT (GPT-5.2) if you prioritize superior reasoning, mathematical problem-solving, and creative writing tasks where nuance and coherence matter most. Choose Gemini 3.0 Pro if you need cost-effective multimodal processing, work with video/audio content, require massive context windows for document analysis, or need the fastest inference speeds—it's 100x cheaper per token and processes diverse content types natively.
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Choose ChatGPT (GPT-5.2) if
Research institutions, creative professionals, mathematicians, and enterprises needing best-in-class reasoning for complex analytical tasks
Choose Gemini (3.0 Pro) if
Video analysts, cost-conscious enterprises, document processing teams, developers, and organizations handling multimodal data streams at scale
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Key Differences at a Glance
- Native Multimodal Support:✓ Gemini (3.0 Pro) wins(Text, image, audio, and video inputs vs Text and image inputs only)
- Context Window Size:✓ Gemini (3.0 Pro) wins(1,000,000 tokens vs 128,000 tokens)
- Training Data Cutoff:✓ Gemini (3.0 Pro) wins(December 2024 vs April 2024)
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Key Facts & Figures
9 numeric metrics compared
| Metric | ChatGPT (GPT-5.2) | Gemini (3.0 Pro) | Ratio |
|---|---|---|---|
| Context Window(tokens) | 128,000 tokens | 1,000,000 tokens | |
| Average Response Latency(seconds) | 1.2 seconds | 0.8 seconds | |
| Cost per 1M Input Tokens(USD) | $2.50 | $0.075 | |
| Output Token Limit(tokens) | 32,000 tokens | 65,000 tokens | |
| Context Memory Window(tokens (with compaction)) | 256,000 tokens | 1,000,000 tokens | |
| Model Parameters(billion) | 175 billion | Estimated 340 billion | |
| Mathematical Reasoning Accuracy (MATH)(percent) | 94.2% | 91.8% | |
| Code Generation Accuracy (HumanEval)(% pass rate) | 89.4% | 92.1% | |
| Native Multimodal Input Types(count) | 2 (text, image) | 4 (text, image, audio, video) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Text and image inputs onlyNative Multimodal SupportText, image, audio, and video inputs(winner)
- 128,000 tokensContext Window Size1,000,000 tokens(winner)
- April 2024Training Data CutoffDecember 2024(winner)
- 1.2 secondsAverage Response Latency0.8 seconds(winner)
- 94.2% accuracy(winner)Reasoning Tasks (MATH Benchmark)91.8% accuracy
- $2.50Cost per 1M Input Tokens$0.075(winner)
- 89.4% pass rateCode Generation (HumanEval)92.1% pass rate(winner)
- Native Multimodal Support
ChatGPT (GPT-5.2)
Text and image inputs only
Gemini (3.0 Pro)
Text, image, audio, and video inputs(winner)
- Context Window Size
ChatGPT (GPT-5.2)
128,000 tokens
Gemini (3.0 Pro)
1,000,000 tokens(winner)
- Training Data Cutoff
ChatGPT (GPT-5.2)
April 2024
Gemini (3.0 Pro)
December 2024(winner)
- Average Response Latency
ChatGPT (GPT-5.2)
1.2 seconds
Gemini (3.0 Pro)
0.8 seconds(winner)
- Reasoning Tasks (MATH Benchmark)
ChatGPT (GPT-5.2)
94.2% accuracy(winner)
Gemini (3.0 Pro)
91.8% accuracy
Full Comparison
| Attribute | ChatGPT (GPT-5.2) | Gemini (3.0 Pro) |
|---|---|---|
| Context Window(tokens) | 128,000 tokens | 1,000,000 tokens(winner) |
| Output Token Limit(tokens) | 32,000 tokens | 65,000 tokens(winner) |
| Context Memory Window(tokens (with compaction)) | 256,000 tokens | 1,000,000 tokens(winner) |
| Average Response Latency(seconds) | 1.2 seconds | 0.8 seconds(winner) |
| Coding Performance(benchmark ranking) | Winner | Second |
| Analytical Reasoning(benchmark ranking) | Slightly superior | Strong |
| Code Generation Accuracy (HumanEval)(% pass rate) | 89.4% | 92.1%(winner) |
| Cost per 1M Input Tokens(USD) | $2.50 | $0.075(winner) |
| API Cost Efficiency(relative pricing) | Standard | 20% cheaper |
| Real-Time Search Integration(null) | Limited | Native |
| Multimodal Capabilities | Text, Image, Basic audio | Text, Image, Video, Audio |
| Model Parameters(billion) | 175 billion | Estimated 340 billion(winner) |
| Mathematical Reasoning Accuracy (MATH)(percent) | 94.2%(winner) | 91.8% |
| Training Data Recency(months old) | April 2024 | December 2024 |
| Native Multimodal Input Types(count) | 2 (text, image) | 4 (text, image, audio, video)(winner) |
Pros & Cons
11 pros·6 cons across both
ChatGPT (GPT-5.2)
Pros
Cons
Gemini (3.0 Pro)
Pros
Cons
Frequently Asked Questions
1 question
Neither is named the winner. OpenAI makes ChatGPT. Google makes Gemini.
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