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Model comparison

GPT-5.2 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-5.2

OpenAI

57.6/100

Estimated · Public rank #71

90% interval 49.4–65.9

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 estimate, 65.98 versus 57.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

6 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.4 nano

    GPT-5.4 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-5.2 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-5.4 nano

    GPT-5.4 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
6
GPT-5.2 only
9
GPT-5.4 nano only
7
Like-for-like categories
1 / 8

3 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.

Math

Like-for-like
GPT-5.2
35.2
GPT-5.4 nano
21.0
Weighted basis
2 vs 2 rows
Reading
GPT-5.2 leads

Agentic

Directional only
GPT-5.2
55.7
GPT-5.4 nano
42.9
Weighted basis
2 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GPT-5.2
92.4
GPT-5.4 nano
43.8
Weighted basis
1 vs 2 rows
Reading
Directional only

Multimodal

Directional only
GPT-5.2
80.4
GPT-5.4 nano
66.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Not comparable
GPT-5.2
70.6
GPT-5.4 nano
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.2
52.9
GPT-5.4 nano
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.2
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.2
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 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-5.2
$0.00875
Fits in one request
GPT-5.4 nano
$0.00082
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.2
$0.1295
Fits in one request
GPT-5.4 nano
$0.01375
Fits in one request

GPT-5.4 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-5.2
$0.525
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-5.2 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-5.2

Not published

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

GPT-5.2

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GPT-5.2

Reasoning

GPT-5.4 nano

Reasoning

Weight access

GPT-5.2

Proprietary

GPT-5.4 nano

Proprietary

License

GPT-5.2

Proprietary

GPT-5.4 nano

Proprietary

Release date

GPT-5.2

2025-12-11

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 estimate, 65.98 versus 57.62, but the 90% score intervals overlap.
Workload cost
Repository review: $0.1295 vs $0.01375. Cache-heavy agent loop: $0.525 vs $0.0205.
Context tradeoff
Both models list 400K.

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 evidence22 rows

Agentic

  • BrowseComp

    GPT-5.265.8%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.247.3%
    Source
    GPT-5.4 nano39%
    Source

    GPT-5.2 leads this result

  • Gert Labs

    GPT-5.246.54%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • JobBench

    GPT-5.234.3%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.2—
    GPT-5.4 nano46.3%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-5.2—
    GPT-5.4 nano56.1%
    Source

    Not directly comparable

  • Toolathlon

    GPT-5.2—
    GPT-5.4 nano35.5%
    Source

    Not directly comparable

  • τ²-bench results

    GPT-5.2—
    GPT-5.4 nano92.5%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-5.280%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • SWE-bench Pro

    GPT-5.255.6%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    GPT-5.253.50%
    GPT-5.4 nano26.10%

    GPT-5.2 leads this result

Reasoning

  • ARC-AGI-2

    GPT-5.252.9%
    Source
    GPT-5.4 nano—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.292.4%
    Source
    GPT-5.4 nano82.8%
    Source

    GPT-5.2 leads this result

  • HLE

    GPT-5.2—
    GPT-5.4 nano37.7%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-5.2—
    GPT-5.4 nano24.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-5.240.700%
    GPT-5.4 nano25.860%

    GPT-5.2 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GPT-5.218.800%
    GPT-5.4 nano6.250%

    GPT-5.2 leads this result

Multimodal

  • MMMU-Pro

    GPT-5.279.5%
    Source
    GPT-5.4 nano66.1%
    Source

    GPT-5.2 leads this result

  • MathVision

    GPT-5.283.0%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • CharXiv

    GPT-5.282.1%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • V*

    GPT-5.275.9%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.2—
    GPT-5.4 nano69.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.2 or GPT-5.4 nano?

GPT-5.4 nano has the higher public score estimate, 65.98 versus 57.62, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.2 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-5.2 or GPT-5.4 nano?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.2 or GPT-5.4 nano?

For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.00082 on GPT-5.4 nano; repository review costs $0.1295 and $0.01375; the cache-heavy agent loop costs $0.525 and $0.0205. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.2 or GPT-5.4 nano?

Both models list the same context window, 400K.

Related comparisons

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Last updated July 30, 2026

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