Gemma 4 released
Google · Model release
Source confirmed
Data as of September 30, 2026 · How the score is built
EstablishedOpen WeightReasoning
Gemma 4 E2B
Decision readingGemma 4 E2B scores 32.5 out of 100 and ranks #157 of 210. This profile shows 2 source-displayable benchmark rows; its strongest eligible category is Knowledge at #154. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Apr 2, 2026 — see all recent releases
Gemma 4 E2B will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
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Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
32.5/100
field median 50.6#157 of 210 ranked models
Public
#157of 210
Verified —
Price
Self-hosted; infrastructure cost varies
input median $1No comparable first-party hosted token rate
Speed
Not measured
field median 92 tok/sTime to first token not measured
Context
128Ktokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Knowledge ranks #154. Particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Validate before choosing
2 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 2 displayable benchmark rows
Follow model changesCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| CodingWeight 20%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank #154 of 169Percentile 9thWeight 12%2 benchmarksReported | 27.9 | 2 benchmarks | Reported | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
2 of 491 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- AgenticNot measured
- CodingNot measured
- ReasoningNot measured
- MultimodalNot measured
- Knowledge0/2 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
Verified sourceProvisionalNot measured
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Gemma 4 E2B category percentile values
- AgenticNot eligible
- CodingNot eligible
- ReasoningNot eligible
- MultimodalNot eligible
- Knowledge9th percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- AgenticNot ranked
- CodingNot ranked
- ReasoningNot ranked
- MultimodalNot ranked
- Knowledge#154/169
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
Top decileTop quartileMid-fieldNot eligible
Benchmark ledger
Knowledge opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-ProMassive Multitask Language Understanding Professional | Score60% | Versus best verified row Best verified: Qwen3.7 Max · 89.6% | Gap29.6 behind | Weight6% ref. weight | Reported |
| GPQAGraduate-Level Google-Proof Q&A | Score43.4% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap52.6 behind | Weight3% ref. weight | Reported |
Bars run 0–100; the dark tick marks the best source-verified value
All 2 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Radar
Full release historyGemma 4 E2B release history
Radar confirmed these at the source. Use Gemma 4 E2B in your work? Explore Radar to follow supported changes and choose your alerts.
Radar
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- google/gemma-4-E2BGoogle gemma-4-E2B model card
- Context window
- 128KGoogle Gemma 4 model documentation
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- text, image, audio, videoGoogle Gemma 4 model documentation
- Output modalities
- textGoogle Gemma 4 model documentation
- Parameters
- Not sourced yet
- Availability
- open weightsGoogle gemma-4-E2B model card
- Cloud regions
- Not tracked yet
- Lifecycle
- activeGoogle gemma-4-E2B model card
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Gemma 4 E2B ranks #157 of 210 on the public leaderboard with a score of 32.45/100. It does not yet have enough sourced coverage for a verified position.
Gemma 4 E2B is a open weight model with a 128K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official exact-value snapshot from Google's April 2, 2026 Gemma 4 model card and launch post. BenchLM maps only schema-equivalent results from the published table: MMLU Pro, GPQA Diamond, LiveCodeBench v6, BigBench Extra Hard, MMMU Pro, and MRCR v2. AIME 2026, Codeforces, MMMLU, OmniDocBench 1.5, MATH-Vision, MedXPertQA MM, audio benchmarks, HLE, and Tau2 (average over 3) remain out-of-schema or unavailable for this model in the current weighted keys.
Gemma 4 E2B sits in the Gemma 4 family with Gemma 4 31B, Gemma 4 26B A4B, Gemma 4 12B, Gemma 4 E4B. 2 of 491 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Knowledge at #154. particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Last updated September 30, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Gemma 4 E2B perform overall in AI benchmarks?
Gemma 4 E2B ranks #157 out of 210 models on the public BenchAlign leaderboard, with a score of 32.45/100. Its evidence status is Estimated, and this profile shows 2 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Gemma 4 E2B good for knowledge and understanding?
Gemma 4 E2B ranks #154 out of 169 eligible models for knowledge and understanding, with a public category score of 27.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Gemma 4 E2B open source?
Gemma 4 E2B is an open-weight model from Google. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Which sibling models are related to Gemma 4 E2B?
Gemma 4 E2B belongs to the Gemma 4 family. Related tracked variants include Gemma 4 31B, Gemma 4 26B A4B, Gemma 4 12B, plus 1 more. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Gemma 4 E2B have full benchmark coverage on BenchLM?
No. Gemma 4 E2B currently has 16 source-displayable rows across 491 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Gemma 4 E2B?
Gemma 4 E2B has a documented context window of 128K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.