Head-to-head · frontier vs frontier

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

We compared Gemini 3 Pro and GPT-5.1 Vision on price, context window and 12 benchmarks. Gemini 3 Pro has the stronger average benchmark score; GPT-5.1 Vision is cheaper on a blended 3:1 basis.

Google DeepMind

Gemini 3 Pro

Gemini 3 Pro is a frontier model from Google DeepMind. It offers a 2,000,000-token context window, costs $18.24 per million input tokens and $82.93 per million output tokens, and averages 86.1 across the index's benchmark suite.

Input
$18.24 / 1M
Output
$82.93 / 1M
Context
2,000,000
Avg score
86.1
Open weights
No
Full Gemini 3 Pro page
OpenAI

GPT-5.1 Vision

GPT-5.1 Vision is a frontier model from OpenAI. It offers a 400,000-token context window, costs $17.76 per million input tokens and $80.59 per million output tokens, and averages 84.8 across the index's benchmark suite.

Input
$17.76 / 1M
Output
$80.59 / 1M
Context
400,000
Avg score
84.8
Open weights
No
Full GPT-5.1 Vision page

Specification comparison

SpecGemini 3 ProGPT-5.1 VisionEdge
Input price / 1M$18.24$17.76B
Output price / 1M$82.93$80.59B
Context window2,000,000400,000A
Max output8,19216,384B
Average score86.184.8A
Arena Elo1460.71462.4B
Open weightsNoNoTie

Benchmark comparison

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

BenchmarkGemini 3 ProGPT-5.1 VisionGapWinner
MMLU-Pro88.285.52.7Gemini 3 Pro
GPQA Diamond79.078.70.3Gemini 3 Pro
SWE-bench Verified78.573.64.9Gemini 3 Pro
HumanEval91.889.62.2Gemini 3 Pro
MATH-50089.989.10.8Gemini 3 Pro
AIME 202584.382.41.9Gemini 3 Pro
LiveCodeBench83.377.85.5Gemini 3 Pro
IFEval93.792.71.0Gemini 3 Pro
TruthfulQA88.886.91.9Gemini 3 Pro
HellaSwag91.392.10.8GPT-5.1 Vision
MMMU78.683.95.3GPT-5.1 Vision
Arena Elo1460.71462.41.7GPT-5.1 Vision

Price analysis

On a blended 3:1 input-to-output basis, Gemini 3 Pro costs about $34.41 per million tokens and GPT-5.1 Vision costs about $33.47. GPT-5.1 Vision is roughly 3% cheaper than Gemini 3 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Gemini 3 Pro has the larger context window at 2,000,000 tokens, compared with 400,000 for GPT-5.1 Vision. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Gemini 3 Pro wins 9, GPT-5.1 Vision wins 3, and 0 are tied. On the index average, Gemini 3 Pro leads GPT-5.1 Vision by 1.3 points (86.1 versus 84.8).

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

Choose Gemini 3 Pro if

  • You want the higher average benchmark score in this index.
  • You need the larger 2,000,000-token context window.

Choose GPT-5.1 Vision if

  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 16,384-token output limit.

FAQ

Is Gemini 3 Pro better than GPT-5.1 Vision?

On the index average, Gemini 3 Pro leads GPT-5.1 Vision by 1.3 points (86.1 versus 84.8). The better choice depends on price, context and your task. Gemini 3 Pro wins 9 benchmarks, GPT-5.1 Vision wins 3.

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

GPT-5.1 Vision is cheaper on a blended 3:1 basis at about $33.47 per million tokens, versus $34.41 for Gemini 3 Pro.

Which has the larger context window?

Gemini 3 Pro has the larger context window at 2,000,000 tokens. GPT-5.1 Vision has 400,000 tokens.

Are these models open weights?

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

Which should I choose for production?

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

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