GPT-5.2 Vision vs Grok 4 Pro: which model should you choose?
We compared GPT-5.2 Vision and Grok 4 Pro on price, context window and 12 benchmarks. GPT-5.2 Vision has the stronger average benchmark score; Grok 4 Pro is cheaper on a blended 3:1 basis.
GPT-5.2 Vision
GPT-5.2 Vision is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $19.03 per million input tokens and $85.72 per million output tokens, and averages 87.4 across the index's benchmark suite.
- Input
- $19.03 / 1M
- Output
- $85.72 / 1M
- Context
- 1,000,000
- Avg score
- 87.4
- Open weights
- No
Grok 4 Pro
Grok 4 Pro is a frontier model from xAI. It offers a 1,000,000-token context window, costs $19.00 per million input tokens and $77.72 per million output tokens, and averages 85.4 across the index's benchmark suite.
- Input
- $19.00 / 1M
- Output
- $77.72 / 1M
- Context
- 1,000,000
- Avg score
- 85.4
- Open weights
- No
Specification comparison
| Spec | GPT-5.2 Vision | Grok 4 Pro | Edge |
|---|---|---|---|
| Input price / 1M | $19.03 | $19.00 | B |
| Output price / 1M | $85.72 | $77.72 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 16,384 | 128,000 | B |
| Average score | 87.4 | 85.4 | A |
| Arena Elo | 1484.3 | 1487.5 | B |
| Open weights | No | No | Tie |
Benchmark comparison
GPT-5.2 Vision wins 9, Grok 4 Pro wins 3, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-5.2 Vision | Grok 4 Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 89.5 | 84.9 | 4.6 | GPT-5.2 Vision |
| GPQA Diamond | 82.1 | 79.2 | 2.9 | GPT-5.2 Vision |
| SWE-bench Verified | 78.8 | 74.7 | 4.1 | GPT-5.2 Vision |
| HumanEval | 91.2 | 89.7 | 1.5 | GPT-5.2 Vision |
| MATH-500 | 92.9 | 92.3 | 0.6 | GPT-5.2 Vision |
| AIME 2025 | 85.7 | 85.4 | 0.3 | GPT-5.2 Vision |
| LiveCodeBench | 83.4 | 77.8 | 5.6 | GPT-5.2 Vision |
| IFEval | 93.9 | 91.3 | 2.6 | GPT-5.2 Vision |
| TruthfulQA | 85.3 | 86.0 | 0.7 | Grok 4 Pro |
| HellaSwag | 94.6 | 92.8 | 1.8 | GPT-5.2 Vision |
| MMMU | 84.4 | 84.9 | 0.5 | Grok 4 Pro |
| Arena Elo | 1484.3 | 1487.5 | 3.2 | Grok 4 Pro |
Price analysis
On a blended 3:1 input-to-output basis, GPT-5.2 Vision costs about $35.70 per million tokens and Grok 4 Pro costs about $33.68. Grok 4 Pro is roughly 6% cheaper than GPT-5.2 Vision. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-5.2 Vision wins 9, Grok 4 Pro wins 3, and 0 are tied. On the index average, GPT-5.2 Vision leads Grok 4 Pro by 2.0 points (87.4 versus 85.4).
The price picture favours Grok 4 Pro. 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-5.2 Vision for quality-sensitive work and Grok 4 Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GPT-5.2 Vision if
- You want the higher average benchmark score in this index.
Choose Grok 4 Pro if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 128,000-token output limit.
FAQ
Is GPT-5.2 Vision better than Grok 4 Pro?
On the index average, GPT-5.2 Vision leads Grok 4 Pro by 2.0 points (87.4 versus 85.4). The better choice depends on price, context and your task. GPT-5.2 Vision wins 9 benchmarks, Grok 4 Pro wins 3.
Which is cheaper, GPT-5.2 Vision or Grok 4 Pro?
Grok 4 Pro is cheaper on a blended 3:1 basis at about $33.68 per million tokens, versus $35.70 for GPT-5.2 Vision.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
GPT-5.2 Vision is proprietary; Grok 4 Pro is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-5.2 Vision for quality-sensitive work and Grok 4 Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.