Skip to main content
BenchLM

GPT-4.1 nano vs Hy3 Preview

Updated September 23, 2026. Rank says Hy3 Preview is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInSocial cardCSVJSON

Decision reading

Hy3 Preview has the higher public score, 45.32 versus 24.33, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo
GPT-4.1 nano

OpenAI

24.33/100

Estimated · Public rank #179

90% interval 18.6–30.1

Model B
Tencent logo
Hy3 Preview

Tencent

45.32/100

Estimated · Public rank #95

90% interval 33.8–56.8

Shared results
1
GPT-4.1 nano only
3
Hy3 Preview only
5
Like-for-like categories
0 / 8
Estimated: GPT-4.1 nano and Hy3 PreviewHow the comparison works

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
Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GPT-4.1 nano and Hy3 Preview are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GPT-4.1 nano and Hy3 Preview are not ranked on the public lane for agentic, so no winner is named for agentic.

    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: rate-fallback

  • 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

20.5GPT-4.1 nano33.3Hy3 Preview

Directional only · BenchAlign v5.6

Hy3 Preview scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.6 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Directional only
GPT-4.1 nano
20.5
Estimated · #125/135
Hy3 Preview
33.3
Estimated · #85/135
Basis
BenchAlign v5.6 lane · 0 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
GPT-4.1 nano
23.8
Supported · #152/160
Hy3 Preview
39.1
Estimated · #93/160
Basis
BenchAlign v5.6 lane · 2 vs 2 public rows
Reading
Directional only

Instruction following

Directional only
GPT-4.1 nano
34.6
#109/124
Hy3 Preview
46.9
#86/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
GPT-4.1 nano
Not ranked
Hy3 Preview
Not ranked
Basis
BenchAlign v5.6 lane · 0 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4.1 nano
35.0
Unranked · 2 rankable rows
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4.1 nano
25.4
Unranked · 1 rankable row
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4.1 nano
Not ranked
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-4.1 nano
25.4
Unranked · 1 rankable row
Hy3 Preview
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
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-4.1 nano
$0.0062
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-4.1 nano
$0.026
Fits in one request
Cached input priced at the published list-input rate
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Hy3 Preview has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

Context window

Maximum documented context; output-token limits may be lower.

GPT-4.1 nano

1M

Hy3 Preview

256K

API model ID

GPT-4.1 nano

Not sourced

Hy3 Preview

Not sourced

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

Hy3 Preview

No comparable hosted API rate

Documented inputs

GPT-4.1 nano

Not sourced

Hy3 Preview

Not sourced

Documented outputs

GPT-4.1 nano

Not sourced

Hy3 Preview

Not sourced

Provider availability

GPT-4.1 nano

Not sourced

Hy3 Preview

Not sourced

Reasoning profile

GPT-4.1 nano

Non-Reasoning

Hy3 Preview

Reasoning

Weight access

GPT-4.1 nano

Proprietary

Hy3 Preview

Open Weight

License

GPT-4.1 nano

Proprietary

Hy3 Preview

Open Weight

Release date

GPT-4.1 nano

2025-04-14

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
Hy3 Preview has the higher public score, 45.32 versus 24.33, 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-4.1 nano has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-4.1 nano or Hy3 Preview?

Hy3 Preview has the higher public score, 45.32 versus 24.33, 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 Hy3 Preview?

Hy3 Preview scores higher for coding on the public lane, 33.3 to 20.5. GPT-4.1 nano and Hy3 Preview are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-4.1 nano or Hy3 Preview?

GPT-4.1 nano and Hy3 Preview are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-4.1 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-4.1 nano or Hy3 Preview?

GPT-4.1 nano has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence9 rows

Agentic

  • Terminal-Bench 2.0

    GPT-4.1 nano—
    Hy3 Preview54.4%
    Source

    Not directly comparable

  • Gert Labs

    GPT-4.1 nano—
    Hy3 Preview36.91%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-4.1 nano—
    Hy3 Preview74.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-4.1 nano—
    Hy3 Preview54.4%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-4.1 nano80.1%
    Source
    Hy3 Preview—

    Not directly comparable

  • GPQA

    GPT-4.1 nano50.3%
    Source
    Hy3 Preview87.2%
    Source

    Hy3 Preview leads this result

  • GPQA-D

    GPT-4.1 nano—
    Hy3 Preview87.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-4.1 nano83.2%
    Source
    Hy3 Preview—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-4.1 nano1.034%
    Source
    Hy3 Preview—

    Not directly comparable

9 public results · 1 shared

Related comparisons

Compare API pricingRead the methodologyOpen the model selector

Watch GPT-4.1 nano vs Hy3 Preview

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Last updated September 23, 2026