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ChatGPT (GPT-5.2) vs Gemini (3.0 Pro)

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ChatGPT (GPT-5.2)

OpenAI's advanced conversational AI with 175B parameters optimized for reasoning and creative tasks

Research institutions, creative professionals, mathematicians, and enterprises needing best-in-class reasoning for complex analytical tasks

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Gemini (3.0 Pro)

Google's multimodal LLM processing text, image, audio, and video natively with 1M token context

Video analysts, cost-conscious enterprises, document processing teams, developers, and organizations handling multimodal data streams at scale

Short Answer

ChatGPT (GPT-5.2) excels in conversational coherence and creative writing with 175 billion parameters, while Gemini 3.0 Pro focuses on multimodal capabilities, processing images, audio, and video natively within a single model framework. Both are enterprise-grade LLMs, but they optimize for different use cases.

Our Verdict

AI-assisted

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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ChatGPT (GPT-5.2)5.6
9.4Gemini (3.0 Pro)

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)
See all 7 differences

Key Facts & Figures

MetricChatGPT (GPT-5.2)Gemini (3.0 Pro)Diff
Output Token Limit(tokens)32,000 tokens65,000 tokens-51%
Input Token Capacity(tokens)1,000,000 tokens1,000,000 tokens
Context Memory Window(tokens (with compaction))256,000 tokens1,000,000 tokens-74%
Model Parameters(billion)175 billionEstimated 340 billion-49%
Context Window(tokens)128,000 tokens1,000,000 tokens-87%
Cost per 1M Input Tokens(USD)$2.50$0.075+3233%
Average Response Latency(milliseconds)1.2 seconds0.8 seconds+50%
Mathematical Reasoning Accuracy (MATH)(percent)94.2%91.8%+3%
Code Generation Accuracy (HumanEval)(percent)89.4%92.1%-3%
Native Multimodal Input Types(count)2 (text, image)4 (text, image, audio, video)-50%

All figures sourced from publicly available data. Last updated Jun 2026.

Key Differences

Native Multimodal Support

ChatGPT (GPT-5.2)

Text and image inputs only

Gemini (3.0 Pro)

Text, image, audio, and video inputs🏆

Context Window Size

ChatGPT (GPT-5.2)

128,000 tokens

Gemini (3.0 Pro)

1,000,000 tokens🏆

Training Data Cutoff

ChatGPT (GPT-5.2)

April 2024

Gemini (3.0 Pro)

December 2024🏆

Average Response Latency

ChatGPT (GPT-5.2)

1.2 seconds

Gemini (3.0 Pro)

0.8 seconds🏆

Reasoning Tasks (MATH Benchmark)

ChatGPT (GPT-5.2)

94.2% accuracy🏆

Gemini (3.0 Pro)

91.8% accuracy

Cost per 1M Input Tokens

ChatGPT (GPT-5.2)

$2.50

Gemini (3.0 Pro)

$0.075🏆

Code Generation (HumanEval)

ChatGPT (GPT-5.2)

89.4% pass rate

Gemini (3.0 Pro)

92.1% pass rate🏆

Full Comparison

ChatGPT (GPT-5.2)
Gemini (3.0 Pro)
Output Token Limit(tokens)
32,000 tokens
65,000 tokens
Input Token Capacity(tokens)
1,000,000 tokens
1,000,000 tokens
Context Memory Window(tokens (with compaction))
256,000 tokens
1,000,000 tokens
Coding Performance(benchmark ranking)
Winner
Second
Analytical Reasoning(benchmark ranking)
Slightly superior
Strong
Average Response Latency(milliseconds)
1.2 seconds
0.8 seconds
Real-Time Search Integration(null)
Limited
Native
Multimodal Capabilities(supported types)
Text, Image, Basic audio
Text, Image, Video, Audio
API Cost Efficiency(relative pricing)
Standard
20% cheaper
Cost per 1M Input Tokens(USD)
$2.50
$0.075
Model Parameters(billion)
175 billion
Estimated 340 billion
Context Window(tokens)
128,000 tokens
1,000,000 tokens
Mathematical Reasoning Accuracy (MATH)(percent)
94.2%
91.8%
Code Generation Accuracy (HumanEval)(percent)
89.4%
92.1%
Training Data Recency(month cutoff)
April 2024
December 2024
Native Multimodal Input Types(count)
2 (text, image)
4 (text, image, audio, video)

Visual Comparison

Side-by-side comparison of numeric attributes

Pros & Cons

ChatGPT (GPT-5.2)

5 pros3 cons

Pros

  • 94.2% accuracy on mathematical reasoning (MATH benchmark)
  • Superior narrative coherence and creative writing quality
  • Extensive fine-tuning for instruction-following across 100+ languages
  • Mature API ecosystem with 15,000+ third-party integrations
  • Advanced reasoning chains for complex multi-step problems

Cons

  • Context window 8x smaller than Gemini (128K vs 1M tokens)
  • No native audio or video processing—requires preprocessing
  • Training data cutoff April 2024 (8 months stale vs competitors)

Gemini (3.0 Pro)

6 pros3 cons

Pros

  • 1,000,000 token context window enables processing 300+ page documents
  • Native audio and video understanding without conversion preprocessing
  • 92.1% code generation accuracy (HumanEval benchmark)
  • 0.075 USD per 1M input tokens—100x cheaper than ChatGPT
  • 0.8 second average latency—33% faster inference
  • December 2024 training data—8 months more current

Cons

  • 91.8% mathematical reasoning (1.4% lower than ChatGPT on MATH)
  • Shorter historical training reduces knowledge of pre-2023 events
  • Less developed third-party integration ecosystem vs ChatGPT

Frequently Asked Questions

ChatGPT (GPT-5.2) achieves 94.2% accuracy on the MATH benchmark versus Gemini's 91.8%, making it superior for complex mathematical reasoning, physics problems, and multi-step derivations. However, the 2.4% difference is modest—Gemini excels for applied problem-solving, while ChatGPT wins for pure mathematical rigor.

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Last updated: June 22, 2026AI generated