Gemma 4 E4B vs GPT-5.4

Updated October 17, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.4 has the higher public point estimate, 66.7 versus 31.02. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemma 4 E4B

Google

31.02/100

Estimated · Public rank #174

Conditional range 16.7–45.4

Model B

GPT-5.4

OpenAI

66.7/100

Supported · Public rank #30

90% interval 62.2–71.2

Shared results: 2
Gemma 4 E4B only: 3
GPT-5.4 only: 40
Like-for-like categories: 0 / 8

Estimated: Gemma 4 E4B · Supported: GPT-5.4. 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.

Long documents
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 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
Gemma 4 E4B is 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
Gemma 4 E4B is 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate.
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.

Pick the job you would use them for

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

12.4Gemma 4 E4B47.7GPT-5.4

Directional only · BenchAlign v5.8

GPT-5.4 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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

GPQAKnowledge
Normalized gap 34.2
Gemma 4 E4B:58.6%
GPT-5.4:92.8%
Gemma 4 E4B source GPT-5.4 source
MMMU-ProMultimodal
Normalized gap 28.6
Gemma 4 E4B:52.6%
GPT-5.4:81.2%
Gemma 4 E4B source GPT-5.4 source

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

Directional only

Gemma 4 E4B: 12.4; Estimated · #138/150
GPT-5.4: 47.7; Supported · #48/150
Basis: BenchAlign v5.8 lane · 1 vs 6 public rows
Reading: Directional only

Multimodal

Directional only

Gemma 4 E4B: 22.2; #52/54
GPT-5.4: 73.1; #30/54
Basis: Provisional lane · 1 vs 3 weighted rows
Reading: Directional only

Knowledge

Directional only

Gemma 4 E4B: 26.1; Estimated · #170/184
GPT-5.4: 69.1; Supported · #20/184
Basis: BenchAlign v5.8 lane · 2 vs 7 public rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 E4B: 51.9; #88/148
GPT-5.4: 90.7; #23/148
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 E4B: Not ranked
GPT-5.4: 52.1; Supported · #44/128
Basis: BenchAlign v5.8 lane · 0 vs 15 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 E4B: 32.4; Unranked · 3 rankable rows
GPT-5.4: 64.2; #23/28
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E4B: Not ranked
GPT-5.4: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E4B: Not ranked
GPT-5.4: 82.0; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 E4B and GPT-5.4
Category	Gemma 4 E4B	GPT-5.4	Basis	Reading
Coding	12.4Estimated · #138/150	47.7Supported · #48/150	Directional onlyBenchAlign v5.8 lane · 1 vs 6 public rows	Directional only
Multimodal	22.2#52/54	73.1#30/54	Directional onlyProvisional lane · 1 vs 3 weighted rows	Directional only
Knowledge	26.1Estimated · #170/184	69.1Supported · #20/184	Directional onlyBenchAlign v5.8 lane · 2 vs 7 public rows	Directional only
Instruction following	51.9#88/148	90.7#23/148	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	52.1Supported · #44/128	Not comparableBenchAlign v5.8 lane · 0 vs 15 public rows	Not comparable
Reasoning	32.4Unranked · 3 rankable rows	64.2#23/28	Not comparableProvisional lane · 1 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	82.0Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	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 E4B: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.4: $0.01; Fits in one request

Gemma 4 E4B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.4: $0.17; Fits in one request

Gemma 4 E4B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
GPT-5.4: $0.25; Fits in one request

Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B 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.

SpecificationGemma 4 E4BGPT-5.4

Context window

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

Gemma 4 E4B

128K

Google Gemma 4 model documentation

GPT-5.4

1.05M

OpenAI pricing

API model ID

Gemma 4 E4B

Not sourced

GPT-5.4

gpt-5.4

OpenAI pricing

Cached-input rate

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

Gemma 4 E4B

No comparable hosted API rate

GPT-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

Documented inputs

Gemma 4 E4B

text, image, audio, video

Google Gemma 4 model documentation

GPT-5.4

Not sourced

Documented outputs

Gemma 4 E4B

text

Google Gemma 4 model documentation

GPT-5.4

Not sourced

Provider availability

Gemma 4 E4B

Open Weights · open weights

Google gemma-4-E4B model card

GPT-5.4

Not sourced

Reasoning profile

Gemma 4 E4B

Reasoning

GPT-5.4

Reasoning

Weight access

Gemma 4 E4B

Open Weight

GPT-5.4

Proprietary

License

Gemma 4 E4B

Open Weight

GPT-5.4

Proprietary

Release date

Gemma 4 E4B

2026-04-02

GPT-5.4

2026-03-05

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 has the higher public point estimate, 66.7 versus 31.02. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.4 has the larger documented window (1.05M).

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

Questions

Which is better, Gemma 4 E4B or GPT-5.4?

GPT-5.4 has the higher public point estimate, 66.7 versus 31.02. Their conditional score ranges do not 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 E4B or GPT-5.4?

GPT-5.4 scores higher for coding on the public lane, 47.7 to 12.4. Gemma 4 E4B is 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, Gemma 4 E4B or GPT-5.4?

Gemma 4 E4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 E4B or GPT-5.4?

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 E4B or GPT-5.4?

GPT-5.4 has the larger documented context window: 1.05M, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence45 rows

Agentic

Terminal-Bench 2.0
Gemma 4 E4B—
GPT-5.475.1%
Source
Not directly comparable
CyberGym
Gemma 4 E4B—
GPT-5.479.0%
Source
Not directly comparable
BrowseComp
Gemma 4 E4B—
GPT-5.482.7%
Source
Not directly comparable
OSWorld-Verified
Gemma 4 E4B—
GPT-5.475%
Source
Not directly comparable
MCP Atlas
Gemma 4 E4B—
GPT-5.470.6%
Source
Not directly comparable
Toolathlon
Gemma 4 E4B—
GPT-5.454.6%
Source
Not directly comparable
τ²-bench results
Gemma 4 E4B—
GPT-5.498.9%
Source
Not directly comparable
Claw-Eval
Gemma 4 E4B—
GPT-5.460.3%
Source
Not directly comparable
DeepSearchQA
Gemma 4 E4B—
GPT-5.473.6%
Source
Not directly comparable
Gert Labs
Gemma 4 E4B—
GPT-5.464.89%
Source
Not directly comparable
ResearchClawBench
Gemma 4 E4B—
GPT-5.415.3%
Source
Not directly comparable
JobBench
Gemma 4 E4B—
GPT-5.438.9%
Source
Not directly comparable
ExploitGym
Gemma 4 E4B—
GPT-5.46.0%
Source
Not directly comparable
ApprenticeBench
Gemma 4 E4B—
GPT-5.411%
Source
Not directly comparable
MazeBench
Gemma 4 E4B—
GPT-5.40%
Source
Not directly comparable

Coding

LiveCodeBench v6
Gemma 4 E4B52.0%
Source
GPT-5.4—
Not directly comparable
LiveCodeBench Pro
Gemma 4 E4B—
GPT-5.487.5%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 E4B—
GPT-5.457.7%
Source
Not directly comparable
React Native Evals
Gemma 4 E4B—
GPT-5.485.3%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 E4B—
GPT-5.475.1%
Source
Not directly comparable
Vibe Code Bench
Gemma 4 E4B—
GPT-5.467.42%
Source
Not directly comparable
PostTrainBench v1.1
Gemma 4 E4B—
GPT-5.419.0%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 E4B25.4%
Source
GPT-5.4—
Not directly comparable
ARC-AGI-2
Gemma 4 E4B—
GPT-5.474.0%
Source
Not directly comparable
ARC-AGI-3
Gemma 4 E4B—
GPT-5.40.2%
Source
Not directly comparable
ARC-AGI-1
Gemma 4 E4B—
GPT-5.493.67%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 E4B52.6%
Source
GPT-5.481.2%
Source
GPT-5.4 leads this result
OfficeQA Pro
Gemma 4 E4B—
GPT-5.453.2%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemma 4 E4B—
GPT-5.482.1%
Source
Not directly comparable
CharXiv
Gemma 4 E4B—
GPT-5.482.8%
Source
Not directly comparable
ERQA
Gemma 4 E4B—
GPT-5.465.4%
Source
Not directly comparable
SimpleVQA
Gemma 4 E4B—
GPT-5.461.1%
Source
Not directly comparable
ScreenSpot Pro
Gemma 4 E4B—
GPT-5.485.4%
Source
Not directly comparable
ZeroBench
Gemma 4 E4B—
GPT-5.441.0%
Source
Not directly comparable
MedXpertQA (MM)
Gemma 4 E4B—
GPT-5.477.1%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E4B58.6%
Source
GPT-5.492.8%
Source
GPT-5.4 leads this result
MMLU-Pro
Gemma 4 E4B69.4%
Source
GPT-5.4—
Not directly comparable
HLE
Gemma 4 E4B—
GPT-5.452.1%
Source
Not directly comparable
HLE w/o tools
Gemma 4 E4B—
GPT-5.439.8%
Source
Not directly comparable
GPQA-D
Gemma 4 E4B—
GPT-5.492.8%
Source
Not directly comparable
HealthBench Hard
Gemma 4 E4B—
GPT-5.440.1%
Source
Not directly comparable
MedXpertQA (Text)
Gemma 4 E4B—
GPT-5.459.6%
Source
Not directly comparable
HealthBench Professional
Gemma 4 E4B—
GPT-5.448.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemma 4 E4B—
GPT-5.478.596%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 E4B—
GPT-5.449.000%
Source
Not directly comparable

45 public results · 2 shared

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Last updated October 17, 2026

Gemma 4 E4B vs GPT-5.4

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Multimodal

Knowledge

Instruction following

Agentic

Reasoning

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemma 4 E4B or GPT-5.4?

Which is better for coding, Gemma 4 E4B or GPT-5.4?

Which is better for agentic tasks, Gemma 4 E4B or GPT-5.4?

Which costs less, Gemma 4 E4B or GPT-5.4?

Which has the larger context window, Gemma 4 E4B or GPT-5.4?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemma 4 E4B vs GPT-5.4