Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 E2B and Gemma 4 E4B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Gemma 4 E2B vs Gemma 4 E4B
Updated November 13, 2026. Rank says Gemma 4 E4B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
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Decision reading
Gemma 4 E4B has the higher public point estimate, 24.59 versus 23.75. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- API price per 1M tokens
Gemma 4 E2B: No comparable API price
Gemma 4 E4B: No comparable API price
- Context window
Gemma 4 E2B: 128K
Gemma 4 E4B: 128K
- Released
Gemma 4 E2B: April 2, 2026
Gemma 4 E4B: April 2, 2026
- Shared results
- 6
- Gemma 4 E2B only
- 0
- Gemma 4 E4B only
- 1
- Like-for-like categories
- 1 / 8
Estimated: Gemma 4 E2B and Gemma 4 E4B. 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 E2B and Gemma 4 E4B are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesCache-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 E2B does not fit this workload in one request. Gemma 4 E4B does not fit this workload in one request. Gemma 4 E2B has no comparable published API token rate. Gemma 4 E4B has no comparable published API token rate.
Confidence: listed-ratesRepository 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.
13.6Gemma 4 E2B14.7Gemma 4 E4B
Directional only · BenchAlign v5.8
Gemma 4 E4B 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
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 Shared
1 Gemma 4 E4B only
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.
GPQAKnowledge
Normalized gap 15.2Gemma 4 E2B:43.4%Gemma 4 E4B:58.6%MMLU-ProKnowledge
Normalized gap 9.4Gemma 4 E2B:60%Gemma 4 E4B:69.4%MMMU-ProMultimodal
Normalized gap 8.4Gemma 4 E2B:44.2%Gemma 4 E4B:52.6%MRCRv2Reasoning
Normalized gap 6.3Gemma 4 E2B:19.1%Gemma 4 E4B:25.4%
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, reasoning, multimodal, 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.
Reasoning
Like-for-like- Gemma 4 E2B
- 18.6
- Supported · #150/160
- Gemma 4 E4B
- 21.8
- Supported · #140/160
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Gemma 4 E4B leads · intervals overlap
Coding
Directional only- Gemma 4 E2B
- 13.6
- Estimated · #152/162
- Gemma 4 E4B
- 14.7
- Estimated · #151/162
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemma 4 E2B
- 25.1
- Estimated · #168/180
- Gemma 4 E4B
- 27.5
- Estimated · #159/180
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 E2B
- 43.8
- #117/157
- Gemma 4 E4B
- 51.9
- #95/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemma 4 E2B
- Not ranked
- Gemma 4 E4B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 E2B
- Not ranked
- Gemma 4 E4B
- 28.6
- Supported · #67/67
- Basis
- BenchAlign v5.8 lane · 2 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 E2B
- Not ranked
- Gemma 4 E4B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 E2B
- Not ranked
- Gemma 4 E4B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 E2B | Gemma 4 E4B | Basis | Reading |
|---|---|---|---|---|
| Reasoning | 18.6Supported · #150/160 | 21.8Supported · #140/160 | Like-for-likeBenchAlign v5.8 lane · 1 vs 1 public rows | Gemma 4 E4B leads · intervals overlap |
| Coding | 13.6Estimated · #152/162 | 14.7Estimated · #151/162 | Directional onlyBenchAlign v5.8 lane · 1 vs 1 public rows | Directional only |
| Knowledge | 25.1Estimated · #168/180 | 27.5Estimated · #159/180 | Directional onlyBenchAlign v5.8 lane · 2 vs 2 public rows | Directional only |
| Instruction following | 43.8#117/157 | 51.9#95/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | 28.6Supported · #67/67 | Not comparableBenchAlign v5.8 lane · 2 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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 E2B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Fits in one request
Gemma 4 E2B has no comparable published API token rate. Gemma 4 E4B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 E2B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Fits in one request
Gemma 4 E2B has no comparable published API token rate. Gemma 4 E4B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 E2B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
Gemma 4 E2B does not fit this workload in one request. Gemma 4 E4B does not fit this workload in one request. Gemma 4 E2B has no comparable published API token rate. 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 E2BGemma 4 E4B
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 E2B
Gemma 4 E4B
API model ID
Gemma 4 E2B
Not sourced
Gemma 4 E4B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 E2B
No comparable hosted API rate
Gemma 4 E4B
No comparable hosted API rate
Documented inputs
Gemma 4 E2B
text, image, audio, video
Google Gemma 4 model documentationGemma 4 E4B
text, image, audio, video
Google Gemma 4 model documentationDocumented outputs
Gemma 4 E2B
Gemma 4 E4B
Provider availability
Gemma 4 E2B
Open Weights · open weights
Google gemma-4-E2B model cardGemma 4 E4B
Open Weights · open weights
Google gemma-4-E4B model cardReasoning profile
Gemma 4 E2B
Reasoning
Gemma 4 E4B
Reasoning
Weight access
Gemma 4 E2B
Open Weight
Gemma 4 E4B
Open Weight
License
Gemma 4 E2B
Open Weight
Gemma 4 E4B
Open Weight
Release date
Gemma 4 E2B
2026-04-02
Gemma 4 E4B
2026-04-02
If you are choosing between sibling variants
- Deployment change
- Both entries list Google as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- Gemma 4 E4B has the higher public point estimate, 24.59 versus 23.75. 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
- Both models list 128K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemma 4 E2B or Gemma 4 E4B?
Gemma 4 E4B has the higher public point estimate, 24.59 versus 23.75. 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 E2B or Gemma 4 E4B?
Gemma 4 E4B scores higher for coding on the public lane, 14.7 to 13.6. Gemma 4 E2B and Gemma 4 E4B 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, Gemma 4 E2B or Gemma 4 E4B?
Gemma 4 E2B and Gemma 4 E4B are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemma 4 E2B or Gemma 4 E4B?
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 E2B or Gemma 4 E4B?
Both models list the same context window, 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 evidence7 rows
Coding
Reasoning
Multimodal
ExtractBench (LlamaIndex)
Shared sourceGemma 4 E2B51.85%Gemma 4 E4B69.64%Gemma 4 E4B leads this result
MMMU-Pro
Gemma 4 E2B44.2%SourceGemma 4 E4B52.6%SourceGemma 4 E4B leads this result
ParseBench-1
Gemma 4 E2B—Gemma 4 E4B41.13%SourceNot directly comparable
Knowledge
GPQA
Shared sourceGemma 4 E2B43.4%Gemma 4 E4B58.6%Gemma 4 E4B leads this result
MMLU-Pro
Shared sourceGemma 4 E2B60%Gemma 4 E4B69.4%Gemma 4 E4B leads this result
7 public results · 6 shared
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Last updated November 13, 2026