Head-to-head · multimodal vs multimodal

Claude Haiku 4.5 Pro vs GPT-4.1 Vision: which model should you choose?

We compared Claude Haiku 4.5 Pro and GPT-4.1 Vision on price, context window and 12 benchmarks. GPT-4.1 Vision has the stronger average benchmark score; Claude Haiku 4.5 Pro is cheaper on a blended 3:1 basis.

Anthropic

Claude Haiku 4.5 Pro

Claude Haiku 4.5 Pro is a multimodal model from Anthropic. It offers a 1,000,000-token context window, costs $15.07 per million input tokens and $73.96 per million output tokens, and averages 80.8 across the index's benchmark suite.

Input
$15.07 / 1M
Output
$73.96 / 1M
Context
1,000,000
Avg score
80.8
Open weights
No
Full Claude Haiku 4.5 Pro page
OpenAI

GPT-4.1 Vision

GPT-4.1 Vision is a multimodal model from OpenAI. It offers a 1,000,000-token context window, costs $14.88 per million input tokens and $81.24 per million output tokens, and averages 82.0 across the index's benchmark suite.

Input
$14.88 / 1M
Output
$81.24 / 1M
Context
1,000,000
Avg score
82.0
Open weights
No
Full GPT-4.1 Vision page

Specification comparison

SpecClaude Haiku 4.5 ProGPT-4.1 VisionEdge
Input price / 1M$15.07$14.88B
Output price / 1M$73.96$81.24A
Context window1,000,0001,000,000Tie
Max output8,1928,192Tie
Average score80.882.0B
Arena Elo1436.21451.8B
Open weightsNoNoTie

Benchmark comparison

Claude Haiku 4.5 Pro wins 2, GPT-4.1 Vision wins 10, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkClaude Haiku 4.5 ProGPT-4.1 VisionGapWinner
MMLU-Pro81.584.02.5GPT-4.1 Vision
GPQA Diamond75.779.33.6GPT-4.1 Vision
SWE-bench Verified70.472.42.0GPT-4.1 Vision
HumanEval86.086.80.8GPT-4.1 Vision
MATH-50083.787.53.8GPT-4.1 Vision
AIME 202580.177.22.9Claude Haiku 4.5 Pro
LiveCodeBench72.474.31.9GPT-4.1 Vision
IFEval90.188.71.4Claude Haiku 4.5 Pro
TruthfulQA84.184.40.3GPT-4.1 Vision
HellaSwag89.389.70.4GPT-4.1 Vision
MMMU75.377.52.2GPT-4.1 Vision
Arena Elo1436.21451.815.6GPT-4.1 Vision

Price analysis

On a blended 3:1 input-to-output basis, Claude Haiku 4.5 Pro costs about $29.79 per million tokens and GPT-4.1 Vision costs about $31.47. Claude Haiku 4.5 Pro is roughly 5% cheaper than GPT-4.1 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, Claude Haiku 4.5 Pro wins 2, GPT-4.1 Vision wins 10, and 0 are tied. On the index average, GPT-4.1 Vision leads Claude Haiku 4.5 Pro by 1.2 points (82.0 versus 80.8).

The price picture favours Claude Haiku 4.5 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-4.1 Vision for quality-sensitive work and Claude Haiku 4.5 Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose Claude Haiku 4.5 Pro if

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

Choose GPT-4.1 Vision if

  • You want the higher average benchmark score in this index.

FAQ

Is Claude Haiku 4.5 Pro better than GPT-4.1 Vision?

On the index average, GPT-4.1 Vision leads Claude Haiku 4.5 Pro by 1.2 points (82.0 versus 80.8). The better choice depends on price, context and your task. Claude Haiku 4.5 Pro wins 2 benchmarks, GPT-4.1 Vision wins 10.

Which is cheaper, Claude Haiku 4.5 Pro or GPT-4.1 Vision?

Claude Haiku 4.5 Pro is cheaper on a blended 3:1 basis at about $29.79 per million tokens, versus $31.47 for GPT-4.1 Vision.

Which has the larger context window?

Both models list a 1,000,000-token context window.

Are these models open weights?

Claude Haiku 4.5 Pro is proprietary; GPT-4.1 Vision is proprietary.

Which should I choose for production?

If you need a single default, start with GPT-4.1 Vision for quality-sensitive work and Claude Haiku 4.5 Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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