Which is cheaper, GPT-4.1 or Phi-4?

Phi-4 is cheaper for both input ($0.07/M tokens) and output ($0.14/M tokens), compared to GPT-4.1 at $2.00/$8.00 per million tokens.

Which is faster, GPT-4.1 or Phi-4?

Phi-4 is faster at 160 tokens/sec with a TTFT of 100ms, compared to GPT-4.1 at 88 tokens/sec and 260ms TTFT.

Which has a bigger context window, GPT-4.1 or Phi-4?

GPT-4.1 has a 1048K token context window, which is larger Phi-4's 16K context window.

Which is better for coding, GPT-4.1 or Phi-4?

GPT-4.1 has a higher coding ELO of 1320 compared to Phi-4's 1130, making it the stronger choice for code generation and programming tasks.

GPT-4.1 vs Phi-4: Pricing, Benchmarks & Verdict (2026)

Verdict

Phi-4 is significantly cheaper at $0.07/$0.14 per million tokens vs $2.00/$8.00. GPT-4.1 is stronger for coding with a coding ELO of 1320 vs 1130. Phi-4 is faster at 160 tokens/sec vs 88 tokens/sec. GPT-4.1 ranks higher overall with an Arena ELO of 1290 vs 1150. GPT-4.1 offers a larger 1048K context window vs 16K.

Side-by-Side Comparison

Feature	GPT-4.1	Phi-4
Provider	OpenAI	Microsoft
Input Price / 1M tokens	$2.00	$0.070
Output Price / 1M tokens	$8.00	$0.140
Context Window	1.047576M	16.384K
Max Output Tokens	32,768	4,096
Arena ELO	1,290	1,150
Coding ELO	1,320	1,130
TTFT (ms)	260	100
Tokens/sec	88	160
Multimodal	Yes	No
JSON Mode	Yes	Yes
Function Calling	Yes	No
Vision	Yes	No

When to Use GPT-4.1

Choose GPT-4.1 when you need: 1m token context window, excellent coding performance, strong instruction following, competitive pricing for its capability. It excels at coding, long-context, agents, general-purpose tasks. Its 1048K context window is larger, making it better for long-document processing.

Strengths:

1M token context window
Excellent coding performance
Strong instruction following
Competitive pricing for its capability

Best for:

codinglong-contextagentsgeneral-purpose

When to Use Phi-4

Choose Phi-4 when you need: ultra-low cost for a capable model, strong math for its size (14b params), very fast inference, can run on consumer hardware. It excels at cost-sensitive, edge-deployment, math, lightweight-tasks tasks. It is also the more cost-effective option between the two.

Strengths:

Ultra-low cost for a capable model
Strong math for its size (14B params)
Very fast inference
Can run on consumer hardware

Best for:

cost-sensitiveedge-deploymentmathlightweight-tasks

GPT-4.1 vs Phi-4: Pricing, Benchmarks & Verdict (2026)

Side-by-Side Comparison

Frequently Asked Questions

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