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BenchLM
Data

Gemma 4 31B vs Hy4 preview

Updated October 13, 2026. Rank says Hy4 preview is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Hy4 preview has the higher public point estimate, 60.86 versus 39.81. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo

Google

39.81/100

Estimated · Public rank #134

Conditional range 30.1–49.5

Model B
Tencent logo

Tencent

60.86/100

Estimated · Public rank #48

Conditional range 46.5–75.2

Shared results
3
Gemma 4 31B only
7
Hy4 preview only
27
Like-for-like categories
1 / 8
Estimated: Gemma 4 31B and Hy4 preview. Conditional ranges do not establish rank confidence.How 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Hy4 preview

    Hy4 preview has the higher public coding point estimate, 57.1 to 32.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    Hy4 preview

    Hy4 preview has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls
  • Agentic work

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

    Not enough matched evidence

    Gemma 4 31B and Hy4 preview are scored on Estimated evidence for agentic, so the reading is 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

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.

32.2Gemma 4 31B57.1Hy4 preview

Like-for-like · BenchAlign v5.8

Hy4 preview has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

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.

2 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 v5.8 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

Like-for-like
Gemma 4 31B
32.2
Supported · #88/148
Hy4 preview
57.1
Supported · #26/148
Basis
BenchAlign v5.8 lane · 3 vs 8 public rows
Reading
Hy4 preview leads

Agentic

Directional only
Gemma 4 31B
19.4
Estimated · #103/123
Hy4 preview
54.5
Estimated · #37/123
Basis
BenchAlign v5.8 lane · 1 vs 15 public rows
Reading
Directional only

Knowledge

Directional only
Gemma 4 31B
40.8
Supported · #111/180
Hy4 preview
63.6
Estimated · #39/180
Basis
BenchAlign v5.8 lane · 4 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
Gemma 4 31B
66.6
Unranked · 3 rankable rows
Hy4 preview
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 31B
74.3
#26/54
Hy4 preview
88.3
Unranked · 1 rankable row
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 31B
Not ranked
Hy4 preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 31B
92.9
#15/127
Hy4 preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemma 4 31B
Not ranked
Hy4 preview
Not ranked
Basis
Provisional lane · 0 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 v5.8) 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

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Hy4 preview
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate. Hy4 preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Hy4 preview
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate. Hy4 preview has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Hy4 preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Gemma 4 31B has no comparable published API token rate. Hy4 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.

Cached-input rate

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

Gemma 4 31B

No comparable hosted API rate

Hy4 preview

No comparable hosted API rate

Tencent Hy4 preview model card

Provider availability

Gemma 4 31B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Hy4 preview

Not sourced

Reasoning profile

Gemma 4 31B

Reasoning

Hy4 preview

Reasoning

Weight access

Gemma 4 31B

Open Weight

Hy4 preview

Open Weight

License

Gemma 4 31B

Open Weight

Hy4 preview

Open Weight

Release date

Gemma 4 31B

2026-04-02

Hy4 preview

2026-08-28

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
Hy4 preview has the higher public point estimate, 60.86 versus 39.81. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Hy4 preview has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 31B or Hy4 preview?

Hy4 preview has the higher public point estimate, 60.86 versus 39.81. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 31B or Hy4 preview?

Hy4 preview has the higher public coding point estimate, 57.1 to 32.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemma 4 31B or Hy4 preview?

Hy4 preview scores higher for agentic tasks on the public lane, 54.5 to 19.4. Gemma 4 31B and Hy4 preview are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemma 4 31B or Hy4 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, Gemma 4 31B or Hy4 preview?

Hy4 preview has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
Hy4 preview
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence37 rows

Agentic

  • Gert Labs

    Gemma 4 31B35.26%
    Source
    Hy4 preview—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemma 4 31B—
    Hy4 preview85.4%
    Source

    Not directly comparable

  • CyberGym

    Gemma 4 31B—
    Hy4 preview78.4%
    Source

    Not directly comparable

  • WideResearch

    Gemma 4 31B—
    Hy4 preview83.9%
    Source

    Not directly comparable

  • DRACO

    Gemma 4 31B—
    Hy4 preview77.2%
    Source

    Not directly comparable

  • MCP Atlas

    Gemma 4 31B—
    Hy4 preview83.7%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemma 4 31B—
    Hy4 preview74.1%
    Source

    Not directly comparable

  • APEX-Agents

    Gemma 4 31B—
    Hy4 preview37.1%
    Source

    Not directly comparable

  • skillsBench

    Gemma 4 31B—
    Hy4 preview62.9%
    Source

    Not directly comparable

  • JobBench

    Gemma 4 31B—
    Hy4 preview61.7%
    Source

    Not directly comparable

  • Agents' Last Exam

    Gemma 4 31B—
    Hy4 preview22.8%
    Source

    Not directly comparable

  • AutomationBench

    Gemma 4 31B—
    Hy4 preview32.1%
    Source

    Not directly comparable

  • BankerToolBench

    Gemma 4 31B—
    Hy4 preview78.6%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemma 4 31B—
    Hy4 preview55.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemma 4 31B—
    Hy4 preview55.1%
    Source

    Not directly comparable

  • CWE-bench v1

    Gemma 4 31B—
    Hy4 preview53.0%
    Source

    Not directly comparable

Coding

  • LiveCodeBench v6

    Gemma 4 31B80.0%
    Source
    Hy4 preview—

    Not directly comparable

  • SWE-Rebench

    Gemma 4 31B41.6%
    Source
    Hy4 preview—

    Not directly comparable

  • React Native Evals

    Gemma 4 31B75.2%
    Source
    Hy4 preview—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemma 4 31B—
    Hy4 preview85.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemma 4 31B—
    Hy4 preview65.7%
    Source

    Not directly comparable

  • SWE Multilingual

    Gemma 4 31B—
    Hy4 preview82.9%
    Source

    Not directly comparable

  • DeepSWE

    Gemma 4 31B—
    Hy4 preview64.3%
    Source

    Not directly comparable

  • NL2Repo

    Gemma 4 31B—
    Hy4 preview58.9%
    Source

    Not directly comparable

  • ProgramBench

    Gemma 4 31B—
    Hy4 preview17.5%
    Source

    Not directly comparable

  • PostTrain Bench

    Gemma 4 31B—
    Hy4 preview35.6%
    Source

    Not directly comparable

  • sweMarathon

    Gemma 4 31B—
    Hy4 preview31.9%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemma 4 31B66.4%
    Source
    Hy4 preview—

    Not directly comparable

  • CritPt

    Gemma 4 31B—
    Hy4 preview16.9%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 31B76.9%
    Source
    Hy4 preview—

    Not directly comparable

  • OfficeQA Pro

    Gemma 4 31B—
    Hy4 preview66.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 31B84.3%
    Source
    Hy4 preview92.3%
    Source

    Hy4 preview leads this result

  • MMLU-Pro

    Gemma 4 31B85.2%
    Source
    Hy4 preview—

    Not directly comparable

  • HLE

    Gemma 4 31B26.5%
    Source
    Hy4 preview55.4%
    Source

    Hy4 preview leads this result

  • HLE w/o tools

    Gemma 4 31B19.5%
    Source
    Hy4 preview43.4%
    Source

    Hy4 preview leads this result

  • GPQA-D

    Gemma 4 31B—
    Hy4 preview92.3%
    Source

    Not directly comparable

Math

  • Apex

    Gemma 4 31B—
    Hy4 preview74.2%
    Source

    Not directly comparable

37 public results · 3 shared

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Last updated October 13, 2026