Head-to-head · frontier vs frontier

Gemini 3 Pro Reasoning vs GPT-5.1 Pro: which model should you choose?

We compared Gemini 3 Pro Reasoning and GPT-5.1 Pro on price, context window and 12 benchmarks. GPT-5.1 Pro has the stronger average benchmark score; Gemini 3 Pro Reasoning 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-5.1 Pro

GPT-5.1 Pro is a frontier model from OpenAI. It offers a 200,000-token context window, costs $19.77 per million input tokens and $100.32 per million output tokens, and averages 87.8 across the index's benchmark suite.

Input
$19.77 / 1M
Output
$100.32 / 1M
Context
200,000
Avg score
87.8
Open weights
No
Full GPT-5.1 Pro page

Specification comparison

SpecGemini 3 Pro ReasoningGPT-5.1 ProEdge
Input price / 1M$19.69$19.77A
Output price / 1M$98.36$100.32A
Context window200,000200,000Tie
Max output8,19232,768B
Average score87.587.8B
Arena Elo1485.01490.0B
Open weightsNoNoTie

Benchmark comparison

Gemini 3 Pro Reasoning wins 3, GPT-5.1 Pro wins 9, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkGemini 3 Pro ReasoningGPT-5.1 ProGapWinner
MMLU-Pro90.186.23.9Gemini 3 Pro Reasoning
GPQA Diamond83.983.40.5Gemini 3 Pro Reasoning
SWE-bench Verified76.380.13.8GPT-5.1 Pro
HumanEval90.291.10.9GPT-5.1 Pro
MATH-50093.395.11.8GPT-5.1 Pro
AIME 202588.890.61.8GPT-5.1 Pro
LiveCodeBench82.082.20.2GPT-5.1 Pro
IFEval93.894.81.0GPT-5.1 Pro
TruthfulQA90.386.93.4Gemini 3 Pro Reasoning
HellaSwag93.593.80.3GPT-5.1 Pro
MMMU80.881.60.8GPT-5.1 Pro
Arena Elo1485.01490.05.0GPT-5.1 Pro

Price analysis

On a blended 3:1 input-to-output basis, Gemini 3 Pro Reasoning costs about $39.36 per million tokens and GPT-5.1 Pro costs about $39.91. Gemini 3 Pro Reasoning is roughly 1% cheaper than GPT-5.1 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Both models list a 200,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Gemini 3 Pro Reasoning wins 3, GPT-5.1 Pro wins 9, and 0 are tied. On the index average, GPT-5.1 Pro leads Gemini 3 Pro Reasoning by 0.3 points (87.8 versus 87.5).

The price picture favours Gemini 3 Pro Reasoning. The context picture is effectively a tie. 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-5.1 Pro for quality-sensitive work and Gemini 3 Pro Reasoning for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose Gemini 3 Pro Reasoning if

  • Unit cost matters and you want the cheaper blended price.

Choose GPT-5.1 Pro if

  • You want the higher average benchmark score in this index.
  • You need the larger 32,768-token output limit.

FAQ

Is Gemini 3 Pro Reasoning better than GPT-5.1 Pro?

On the index average, GPT-5.1 Pro leads Gemini 3 Pro Reasoning by 0.3 points (87.8 versus 87.5). The better choice depends on price, context and your task. Gemini 3 Pro Reasoning wins 3 benchmarks, GPT-5.1 Pro wins 9.

Which is cheaper, Gemini 3 Pro Reasoning or GPT-5.1 Pro?

Gemini 3 Pro Reasoning is cheaper on a blended 3:1 basis at about $39.36 per million tokens, versus $39.91 for GPT-5.1 Pro.

Which has the larger context window?

Both models list a 200,000-token context window.

Are these models open weights?

Gemini 3 Pro Reasoning is proprietary; GPT-5.1 Pro is proprietary.

Which should I choose for production?

If you need a single default, start with GPT-5.1 Pro for quality-sensitive work and Gemini 3 Pro Reasoning for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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