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Model comparison

GPT-5.4 nano vs Hy3 Preview

Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.4 nano

OpenAI

66.1/100

Supported · Public rank #28

90% interval 55.7–76.5

Hy3 Preview

Tencent

42.8/100

Estimated · Public rank #167

90% interval 32.9–52.7

GPT-5.4 nano has the higher public score, 66.07 versus 42.8, 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-5.4 nano

    GPT-5.4 nano has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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-5.4 nano only
11
Hy3 Preview only
4
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.

Agentic

Directional only
GPT-5.4 nano
42.9
Hy3 Preview
54.4
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
GPT-5.4 nano
43.8
Hy3 Preview
87.2
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Not comparable
GPT-5.4 nano
Not measured
Hy3 Preview
74.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 nano
Not measured
Hy3 Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 nano
21.0
Hy3 Preview
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 nano
Not measured
Hy3 Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 nano
66.1
Hy3 Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 nano
Not measured
Hy3 Preview
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-5.4 nano
$0.00082
Fits in one request
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request

Hy3 Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 nano
$0.01375
Fits in one request
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request

Hy3 Preview has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.4 nano
$0.0205
Fits in one request
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Hy3 Preview has no comparable published API token 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-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Hy3 Preview

No comparable hosted API rate

Provider availability

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Hy3 Preview

Not sourced

Reasoning profile

GPT-5.4 nano

Reasoning

Hy3 Preview

Reasoning

Weight access

GPT-5.4 nano

Proprietary

Hy3 Preview

Open Weight

License

GPT-5.4 nano

Proprietary

Hy3 Preview

Open Weight

Release date

GPT-5.4 nano

2026-03-17

Hy3 Preview

2026-04-23

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
GPT-5.4 nano has the higher public score, 66.07 versus 42.8, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.4 nano has the larger documented window (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 evidence17 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.4 nano46.3%
    Source
    Hy3 Preview54.4%
    Source

    Hy3 Preview leads this result

  • OSWorld-Verified

    GPT-5.4 nano39%
    Source
    Hy3 Preview—

    Not directly comparable

  • MCP Atlas

    GPT-5.4 nano56.1%
    Source
    Hy3 Preview—

    Not directly comparable

  • Toolathlon

    GPT-5.4 nano35.5%
    Source
    Hy3 Preview—

    Not directly comparable

  • τ²-bench results

    GPT-5.4 nano92.5%
    Source
    Hy3 Preview—

    Not directly comparable

  • Gert Labs

    GPT-5.4 nano—
    Hy3 Preview36.91%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-5.4 nano26.10%
    Source
    Hy3 Preview—

    Not directly comparable

  • SWE-bench Verified

    GPT-5.4 nano—
    Hy3 Preview74.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.4 nano—
    Hy3 Preview54.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 nano82.8%
    Source
    Hy3 Preview87.2%
    Source

    Hy3 Preview leads this result

  • HLE

    GPT-5.4 nano37.7%
    Source
    Hy3 Preview—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 nano24.3%
    Source
    Hy3 Preview—

    Not directly comparable

  • GPQA-D

    GPT-5.4 nano—
    Hy3 Preview87.2%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 nano25.860%
    Source
    Hy3 Preview—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 nano6.250%
    Source
    Hy3 Preview—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 nano66.1%
    Source
    Hy3 Preview—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.4 nano69.5%
    Source
    Hy3 Preview—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 nano or Hy3 Preview?

GPT-5.4 nano has the higher public score, 66.07 versus 42.8, 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-5.4 nano or Hy3 Preview?

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-5.4 nano or Hy3 Preview?

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-5.4 nano or Hy3 Preview?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.4 nano or Hy3 Preview?

GPT-5.4 nano has the larger documented context window: 400K, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 7, 2026

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