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ModelCap

gemma 4 E2B it

Current board #106Official publisher releaseOpen weights

Google·google/gemma-4-E2B-it

gemma 4 E2B it is ModelCap current-board rank #106 with Index 39.3 (Modeled · peer benchmarks), from the ModelCap Index as of 9 Sept 2026, 20:46 UTC.

gemma 4 E2B it weight access is open weights, from ModelCap classification of published repository metadata as of 9 Sept 2026, 20:46 UTC.

Snapshot facts · download the public dataset · methodology

ModelCap Index · Current board
39.3#106
Estimated · 29% support · score interval 30.5–48.1
No admitted evaluation publication timestamp yet
Market Gravity
17.2
Secondary market signal

OpenRouter endpoint status could not be refreshed. This model is not treated as currently served until a successful observation arrives.

Input
Self-hosted
weights only · no listed API price
Output
Self-hosted
weights only · no listed API price
Context
tokens
Providers
0
endpoints
Gateway spend
latest · Vercel share

Providers

Uptime measured over the last 30 minutes

Current provider status is unavailable.

Reported evaluation leads (10)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.

Dataset / taskMetricValueRevisionProvenance
ARTPARK-IISc/Vaani-Benchmark-V1.0
Hindi_WER
19.4
Unpinned
not published
Reported
Not used in launch estimate
Idavidrein/gpqa
diamond
43.4
Unpinned
not published
Reported
Not used in launch estimate
LiquidAI/ifstruct-v1.0
ifstruct_v1
64.85
Unpinned
not published
Reported
Not used in launch estimate
llamaindex/ExtractBench
mean
51.85
Unpinned
not published
Reported
Not used in launch estimate
llamaindex/ExtractBench
short
63.71
Unpinned
not published
Reported
Not used in launch estimate
llamaindex/ExtractBench
medium
28.96
Unpinned
not published
Reported
Not used in launch estimate
llamaindex/ExtractBench
long
14.58
Unpinned
not published
Reported
Not used in launch estimate
MathArena/aime_2026
MathArena/aime_2026
37.5
Unpinned
not published
Reported
Not used in launch estimate
MMMU/MMMU_Pro
mmmu_pro_vision
44.2
Unpinned
not published
Reported
Used in launch estimate
TIGER-Lab/MMLU-Pro
mmlu_pro
60
Unpinned
not published
Reported
Used in launch estimate

ModelCap Index · Current board

Methodology
39.3
Modeled · peer benchmarks · calibrated architecture-and-release-era fallback from static public metadata
Current board
#106
Public rank basis
Modeled · peer benchmarks (architecture)
Index support
29.0% · limited
Index interval
30.5–48.1
Rank posterior
Not available
Top 5 / Top 10 probability
Not available / Not available
Posterior as of
Snapshot timestamp unavailable
Evidence as of
No admitted evaluation publication timestamp
Identity binding
Exact catalogue product · google/google/gemma-4-E2B-it · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata google/gemma-4-E2B-it@3e22461f65e89153144f8adb70e3b8c2cc9845a7 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
39.3
How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate
39.3 ± 6.9
Independent share
0%
Shrink toward corpus
+2.7
Peer ceiling share
0%
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Publisher corpus prior · not independent
publisher-corpus-prior over 182 held-out anchors
65.0 ± 22.39%
Launch card against resolved peers · not independent
launch card against 3 resolved peers on 5 rows
36.6 ± 7.291%
  • · Launch card row aime-2026-without-tools: inside the resolved peer range
  • · Launch card row gpqa-diamond: inside the resolved peer range
  • · Launch card row livecodebench-v-6: inside the resolved peer range
  • · Launch card row mmlu-pro: claims below every resolved peer
  • · Launch card row mmmu-pro: claims below every resolved peer
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
Evidence status
No admitted measured evidence
General preference
Coding
Agents & tools
Reasoning
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
Liquidity (providers × uptime)
Open reach (HF downloads)
98.6
Surface (context / tools / modalities)
15.0
Freshness
48.3
  • · HF 30d downloads 3222423
  • · Catalogue: modalities Text/Image/Audio
  • · First seen 2026-03-02

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
Estimator abstained

uncertainty-above-gate

Feature coverage
71%
Training support
187 models · 107 lineages

uncertainty-above-gate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
52.4
partial · 56% metadata coverage
Not capability
Artifact reproducibility
100.0
Access & legal clarity
100.0
Deployability
8.0
Evaluation provenance
0.0

Missing: deployability.independent-provider-records, deployability.measured-provider-uptime, deployability.published-provider-pricing, deployability.declared-capacity, evaluationProvenance.admitted-evaluation-observations, evaluationProvenance.capability-family-breadth, evaluationProvenance.evaluation-source-diversity, evaluationProvenance.identity-and-source-confidence, evaluationProvenance.evaluation-dataset-revisions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
17.2
OpenRouter weekly popularity
Not listed

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
0.0

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
0.0

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
98.6

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
48.3

Time since first public availability, on a six-month half-life.

Specification

Context window
tokens
Max output
Inputs
Text, Image, Audio
Outputs
Text, Image, Audio
Tokenizer
Cached input tokens
Not offered
First seen on OpenRouter
2 Mar 2026

Capabilities

  • Not supported: Reasoning
  • Not supported: Tool use
  • Not supported: Structured output
  • Not supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
apache-2.0
Pinned revision
3e22461f65e8
Parameters
5.1B
Architecture
Gemma4ForConditionalGeneration
Model type
gemma4
Hugging Face downloads (30d)
3.2M
Downloads (all time)
17.3M
Likes
950
Repository updated
20 Jul 2026, 16:41 UTC
Base lineage
google/gemma-4-E2B

Compare gemma 4 E2B it with

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Ranked neighbours

Models within 3 places of #106 on the ModelCap Index.

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gemma 4 E2B it: common questions

How does gemma 4 E2B it rank among AI models?

gemma 4 E2B it holds ModelCap Index position #106 of 160 ranked language models as of 9 September 2026, with an Index score of 39.3 (interval 30.5–48.1); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does gemma 4 E2B it cost per 1M tokens?

No live API price is listed for gemma 4 E2B it in the current snapshot as of 9 September 2026 because the latest endpoint refresh was unavailable. ModelCap shows a price only when a provider currently lists one.

Is gemma 4 E2B it open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is google/gemma-4-E2B-it on Hugging Face.

Which benchmarks has gemma 4 E2B it been evaluated on?

gemma 4 E2B it's Index position rests on the evidence listed on this page; it has no result on the public boards ModelCap tracks as separate leaderboards as of 9 September 2026. The evidence panel names each source and its publication date.

How does gemma 4 E2B it compare with Nemotron 3 Super?

gemma 4 E2B it ranks #106 (Index 39.3) and Nemotron 3 Super ranks #107 (Index 38.9) on the ModelCap Index as of 9 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.

When was gemma 4 E2B it released?

gemma 4 E2B it first appeared in the catalogue on 2 Mar 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.