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Model comparison

GPT-4.1 nano vs GPT-5.4 nano

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-4.1 nano

OpenAI

41.1/100

Estimated · Public rank #170

90% interval 29.6–52.6

GPT-5.4 nano

OpenAI

66.0/100

Supported · Public rank #28

90% interval 55.5–76.4

GPT-5.4 nano has the higher public score, 65.98 versus 41.1, and the 90% score intervals do not overlap.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    GPT-4.1 nano

    GPT-4.1 nano has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-4.1 nano

    GPT-4.1 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    GPT-5.4 nano

    GPT-5.4 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-4.1 nano

    GPT-4.1 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
2
GPT-4.1 nano only
2
GPT-5.4 nano only
11
Like-for-like categories
0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Directional only
GPT-4.1 nano
50.3
GPT-5.4 nano
43.8
Weighted basis
1 vs 2 rows
Reading
Directional only

Math

Directional only
GPT-4.1 nano
1.0
GPT-5.4 nano
21.0
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
GPT-4.1 nano
Not measured
GPT-5.4 nano
42.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
GPT-4.1 nano
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4.1 nano
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4.1 nano
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4.1 nano
Not measured
GPT-5.4 nano
66.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-4.1 nano
83.2
GPT-5.4 nano
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-4.1 nano
$0.0003
Fits in one request
GPT-5.4 nano
$0.00082
Fits in one request

GPT-4.1 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-4.1 nano
$0.0062
Fits in one request
GPT-5.4 nano
$0.01375
Fits in one request

GPT-4.1 nano has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-4.1 nano
$0.026
Fits in one request
Cached input priced at the published list-input rate
GPT-5.4 nano
$0.0205
Fits in one request

GPT-5.4 nano has the lower modeled cost

GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-4.1 nano

Not published

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

GPT-4.1 nano

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GPT-4.1 nano

Non-Reasoning

GPT-5.4 nano

Reasoning

Weight access

GPT-4.1 nano

Proprietary

GPT-5.4 nano

Proprietary

License

GPT-4.1 nano

Proprietary

GPT-5.4 nano

Proprietary

Release date

GPT-4.1 nano

2025-04-14

GPT-5.4 nano

2026-03-17

If you already use one of these models
Deployment change
Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
GPT-5.4 nano has the higher public score, 65.98 versus 41.1, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.0062 vs $0.01375. Cache-heavy agent loop: $0.026 vs $0.0205.
Context tradeoff
GPT-4.1 nano has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence15 rows

Agentic

  • Terminal-Bench 2.0

    GPT-4.1 nano—
    GPT-5.4 nano46.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    GPT-4.1 nano—
    GPT-5.4 nano39%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-4.1 nano—
    GPT-5.4 nano56.1%
    Source

    Not directly comparable

  • Toolathlon

    GPT-4.1 nano—
    GPT-5.4 nano35.5%
    Source

    Not directly comparable

  • τ²-bench results

    GPT-4.1 nano—
    GPT-5.4 nano92.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-4.1 nano—
    GPT-5.4 nano26.10%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-4.1 nano80.1%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • GPQA

    GPT-4.1 nano50.3%
    Source
    GPT-5.4 nano82.8%
    Source

    GPT-5.4 nano leads this result

  • HLE

    GPT-4.1 nano—
    GPT-5.4 nano37.7%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-4.1 nano—
    GPT-5.4 nano24.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-4.1 nano1.034%
    GPT-5.4 nano25.860%

    GPT-5.4 nano leads this result

  • FrontierMath v2 (Tier 4)

    GPT-4.1 nano—
    GPT-5.4 nano6.250%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-4.1 nano—
    GPT-5.4 nano66.1%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-4.1 nano—
    GPT-5.4 nano69.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-4.1 nano83.2%
    Source
    GPT-5.4 nano—

    Not directly comparable

Frequently asked questions

Which is better, GPT-4.1 nano or GPT-5.4 nano?

GPT-5.4 nano has the higher public score, 65.98 versus 41.1, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-4.1 nano or GPT-5.4 nano?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-4.1 nano or GPT-5.4 nano?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-4.1 nano or GPT-5.4 nano?

For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.00082 on GPT-5.4 nano; repository review costs $0.0062 and $0.01375; the cache-heavy agent loop costs $0.026 and $0.0205. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-4.1 nano or GPT-5.4 nano?

GPT-4.1 nano has the larger documented context window: 1M, compared with 400K.

Related comparisons

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Last updated July 30, 2026

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