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ChatGPT (GPT-5.2) vs Gemini 3.0 Pro 2026

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.

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

Score63%
Get ChatGPT (GPT-5.2)
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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

Score67%
Get Gemini (3.0 Pro)
16 attributes7 differences17 pros/cons
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Quick Answer

AI Summary

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.

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Video Comparison

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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C

Choose ChatGPT (GPT-5.2) if

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

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Choose Gemini (3.0 Pro) if

Best pick

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

10 numeric metrics compared

MetricChatGPT (GPT-5.2)Gemini (3.0 Pro)Ratio
Output Token Limit(tokens)32,000 tokens65,000 tokens
Input Token Capacity(tokens)1,000,000 tokens1,000,000 tokens
Context Memory Window(tokens (with compaction))256,000 tokens1,000,000 tokens
Model Parameters(billion)175 billionEstimated 340 billion
Context Window(tokens)128,000 tokens1,000,000 tokens
Cost per 1M Input Tokens(USD)$2.50$0.075
Average Response Latency(seconds)1.2 seconds0.8 seconds
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

C(
1ChatGPT (GPT-5.2)
Gemini (3.0 Pro) leads
G(
6Gemini (3.0 Pro)
  • 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

CChatGPT (GPT-5.2)
GGemini (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
Context Window(tokens)
128,000 tokens
1,000,000 tokens
Coding Performance(benchmark ranking)
Winner
Second
Analytical Reasoning(benchmark ranking)
Slightly superior
Strong
Average Response Latency(seconds)
1.2 seconds
0.8 seconds
Code Generation Accuracy (HumanEval)(% pass rate)
89.4%
92.1%
Real-Time Search Integration(null)
Limited
Native
Multimodal Capabilities
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
Mathematical Reasoning Accuracy (MATH)(percent)
94.2%
91.8%
Training Data Recency(months old)
April 2024
December 2024
Native Multimodal Input Types(count)
2 (text, image)
4 (text, image, audio, video)

Pros & Cons

11 pros·6 cons across both

C(
G(
C(

ChatGPT (GPT-5.2)

+5-3

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)
G(

Gemini (3.0 Pro)

+6-3

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

9 questions

  1. 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.

  2. Gemini 3.0 Pro's 1,000,000 token context window dwarfs ChatGPT's 128,000 tokens, enabling it to process 300+ page documents, entire codebases, or extended conversations without losing prior context. ChatGPT requires chunking large inputs, while Gemini handles them natively.

  3. Gemini 3.0 Pro processes video, audio, images, and text natively without preprocessing, making it purpose-built for multimedia analysis. ChatGPT requires you to extract frames, transcribe audio, or use intermediate tools, adding latency and cost. For video-heavy workflows, Gemini is decisively superior.

  4. Gemini 3.0 Pro costs $0.075 per 1M input tokens versus ChatGPT's $2.50—a 33x cost advantage. For organizations processing millions of tokens monthly, Gemini saves thousands monthly. ChatGPT's higher cost reflects its specialized reasoning capabilities, but Gemini offers better value for most enterprise workloads.

  5. Gemini 3.0 Pro's December 2024 training cutoff is 8 months newer than ChatGPT's April 2024 cutoff, providing better knowledge of recent geopolitical events, market movements, and technology releases. For time-sensitive queries, Gemini is more current.

  1. Gemini 3.0 Pro wins on most measurable specs but not all. It leads on context window (1,000,000 vs 128,000 tokens), native audio and video input, a December 2024 training cutoff versus April 2024, and average latency of 0.8 seconds versus 1.2. ChatGPT (GPT-5.2) still edges ahead on reasoning, scoring 94.2% on the MATH benchmark against Gemini's 91.8%, and on nuanced creative writing.

  2. Gemini 3.0 Pro covers the same core ground—text generation, coding, analysis, image understanding—and adds native audio and video input that ChatGPT (GPT-5.2) lacks. The gap runs the other way on reasoning depth: ChatGPT scores 94.2% on the MATH benchmark to Gemini's 91.8% and is generally stronger on conversational coherence and creative writing where nuance matters.

  3. Gemini 3.0 Pro beats ChatGPT (GPT-5.2) on several concrete measures: a 1,000,000-token context window against 128,000, native video and audio input, 0.8-second average latency versus 1.2, and a far lower token price at $0.075 per 1M input tokens against $2.50. "Better" depends on the workload, since ChatGPT retains the reasoning edge at 94.2% on MATH versus 91.8%.

  4. Gemini 3.0 Pro is priced at $0.075 per 1 million input tokens, compared with $2.50 for ChatGPT (GPT-5.2)—roughly a 33x difference on input. That gap compounds quickly for teams processing millions of tokens a month, which is why Gemini tends to win on cost for high-volume document, video, and audio workloads.

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