Head-to-head · multimodal vs multimodal

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

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

Anthropic

Claude Haiku 4.5 Max

Claude Haiku 4.5 Max is a multimodal model from Anthropic. It offers a 200,000-token context window, costs $15.98 per million input tokens and $80.37 per million output tokens, and averages 83.8 across the index's benchmark suite.

Input
$15.98 / 1M
Output
$80.37 / 1M
Context
200,000
Avg score
83.8
Open weights
No
Full Claude Haiku 4.5 Max page
OpenAI

GPT-4.1

GPT-4.1 is a multimodal model from OpenAI. It offers a 1,000,000-token context window, costs $15.71 per million input tokens and $64.52 per million output tokens, and averages 82.4 across the index's benchmark suite.

Input
$15.71 / 1M
Output
$64.52 / 1M
Context
1,000,000
Avg score
82.4
Open weights
No
Full GPT-4.1 page

Specification comparison

SpecClaude Haiku 4.5 MaxGPT-4.1Edge
Input price / 1M$15.98$15.71B
Output price / 1M$80.37$64.52B
Context window200,0001,000,000B
Max output4,09616,384B
Average score83.882.4A
Arena Elo1452.01432.4A
Open weightsNoNoTie

Benchmark comparison

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

BenchmarkClaude Haiku 4.5 MaxGPT-4.1GapWinner
MMLU-Pro86.686.20.4Claude Haiku 4.5 Max
GPQA Diamond80.376.14.2Claude Haiku 4.5 Max
SWE-bench Verified74.072.21.8Claude Haiku 4.5 Max
HumanEval88.588.30.2Claude Haiku 4.5 Max
MATH-50088.786.81.9Claude Haiku 4.5 Max
AIME 202578.279.91.7GPT-4.1
LiveCodeBench79.277.81.4Claude Haiku 4.5 Max
IFEval89.990.40.5GPT-4.1
TruthfulQA83.781.81.9Claude Haiku 4.5 Max
HellaSwag91.190.30.8Claude Haiku 4.5 Max
MMMU81.476.35.1Claude Haiku 4.5 Max
Arena Elo1452.01432.419.6Claude Haiku 4.5 Max

Price analysis

On a blended 3:1 input-to-output basis, Claude Haiku 4.5 Max costs about $32.08 per million tokens and GPT-4.1 costs about $27.91. GPT-4.1 is roughly 13% cheaper than Claude Haiku 4.5 Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

GPT-4.1 has the larger context window at 1,000,000 tokens, compared with 200,000 for Claude Haiku 4.5 Max. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Claude Haiku 4.5 Max wins 10, GPT-4.1 wins 2, and 0 are tied. On the index average, Claude Haiku 4.5 Max leads GPT-4.1 by 1.4 points (83.8 versus 82.4).

The price picture favours GPT-4.1. The context picture favours GPT-4.1. 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 Claude Haiku 4.5 Max for quality-sensitive work and GPT-4.1 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 Max if

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

Choose GPT-4.1 if

  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 1,000,000-token context window.
  • You need the larger 16,384-token output limit.

FAQ

Is Claude Haiku 4.5 Max better than GPT-4.1?

On the index average, Claude Haiku 4.5 Max leads GPT-4.1 by 1.4 points (83.8 versus 82.4). The better choice depends on price, context and your task. Claude Haiku 4.5 Max wins 10 benchmarks, GPT-4.1 wins 2.

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

GPT-4.1 is cheaper on a blended 3:1 basis at about $27.91 per million tokens, versus $32.08 for Claude Haiku 4.5 Max.

Which has the larger context window?

GPT-4.1 has the larger context window at 1,000,000 tokens. Claude Haiku 4.5 Max has 200,000 tokens.

Are these models open weights?

Claude Haiku 4.5 Max is proprietary; GPT-4.1 is proprietary.

Which should I choose for production?

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

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