Head-to-head · frontier vs frontier

Gemini 3 Pro Reasoning vs GPT-5.2: which model should you choose?

We compared Gemini 3 Pro Reasoning and GPT-5.2 on price, context window and 12 benchmarks. GPT-5.2 has the stronger average benchmark score; GPT-5.2 is cheaper on a blended 3:1 basis.

Google DeepMind

Gemini 3 Pro Reasoning

Gemini 3 Pro Reasoning is a frontier model from Google DeepMind. It offers a 200,000-token context window, costs $19.69 per million input tokens and $98.36 per million output tokens, and averages 87.5 across the index's benchmark suite.

Input
$19.69 / 1M
Output
$98.36 / 1M
Context
200,000
Avg score
87.5
Open weights
No
Full Gemini 3 Pro Reasoning page
OpenAI

GPT-5.2

GPT-5.2 is a frontier model from OpenAI. It offers a 2,000,000-token context window, costs $19.71 per million input tokens and $85.71 per million output tokens, and averages 88.0 across the index's benchmark suite.

Input
$19.71 / 1M
Output
$85.71 / 1M
Context
2,000,000
Avg score
88.0
Open weights
No
Full GPT-5.2 page

Specification comparison

SpecGemini 3 Pro ReasoningGPT-5.2Edge
Input price / 1M$19.69$19.71A
Output price / 1M$98.36$85.71B
Context window200,0002,000,000B
Max output8,19216,384B
Average score87.588.0B
Arena Elo1485.01478.0A
Open weightsNoNoTie

Benchmark comparison

Gemini 3 Pro Reasoning wins 7, GPT-5.2 wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkGemini 3 Pro ReasoningGPT-5.2GapWinner
MMLU-Pro90.189.90.2Gemini 3 Pro Reasoning
GPQA Diamond83.984.91.0GPT-5.2
SWE-bench Verified76.380.23.9GPT-5.2
HumanEval90.291.00.8GPT-5.2
MATH-50093.390.62.7Gemini 3 Pro Reasoning
AIME 202588.890.61.8GPT-5.2
LiveCodeBench82.079.72.3Gemini 3 Pro Reasoning
IFEval93.893.60.2Gemini 3 Pro Reasoning
TruthfulQA90.389.70.6Gemini 3 Pro Reasoning
HellaSwag93.592.60.9Gemini 3 Pro Reasoning
MMMU80.885.34.5GPT-5.2
Arena Elo1485.01478.07.0Gemini 3 Pro Reasoning

Price analysis

On a blended 3:1 input-to-output basis, Gemini 3 Pro Reasoning costs about $39.36 per million tokens and GPT-5.2 costs about $36.21. GPT-5.2 is roughly 8% cheaper than Gemini 3 Pro Reasoning. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

GPT-5.2 has the larger context window at 2,000,000 tokens, compared with 200,000 for Gemini 3 Pro Reasoning. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Gemini 3 Pro Reasoning wins 7, GPT-5.2 wins 5, and 0 are tied. On the index average, GPT-5.2 leads Gemini 3 Pro Reasoning by 0.5 points (88.0 versus 87.5).

The price picture favours GPT-5.2. The context picture favours GPT-5.2. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.

If you need a single default, start with GPT-5.2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.

Choose Gemini 3 Pro Reasoning if

  • You prefer Google DeepMind's tooling or already use it.

Choose GPT-5.2 if

  • You want the higher average benchmark score in this index.
  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 2,000,000-token context window.
  • You need the larger 16,384-token output limit.

FAQ

Is Gemini 3 Pro Reasoning better than GPT-5.2?

On the index average, GPT-5.2 leads Gemini 3 Pro Reasoning by 0.5 points (88.0 versus 87.5). The better choice depends on price, context and your task. Gemini 3 Pro Reasoning wins 7 benchmarks, GPT-5.2 wins 5.

Which is cheaper, Gemini 3 Pro Reasoning or GPT-5.2?

GPT-5.2 is cheaper on a blended 3:1 basis at about $36.21 per million tokens, versus $39.36 for Gemini 3 Pro Reasoning.

Which has the larger context window?

GPT-5.2 has the larger context window at 2,000,000 tokens. Gemini 3 Pro Reasoning has 200,000 tokens.

Are these models open weights?

Gemini 3 Pro Reasoning is proprietary; GPT-5.2 is proprietary.

Which should I choose for production?

If you need a single default, start with GPT-5.2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.

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