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
26.4
Evidence status
confirmed · 77% mass
General preference
27.9
Coding
15.3
Agents & tools
—
Reasoning
—
Evidence breadth
92%
Evidence coverage
63%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
—
Liquidity (providers × uptime)
—
Open reach (HF downloads)
—
Surface (context / tools / modalities)
30.1
Freshness
16.4
· Catalogue: context 32768, modalities Text/Image
· First seen 2025-06-03
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.
Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.
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%
50.0
Hugging Face 30-day downloads for open models; neutral for closed models.
Freshness5%
16.4
Time since first public availability, on a six-month half-life.
Specification
Context window
33K tokens
Max output
—
Inputs
Text, Image
Outputs
Text
Tokenizer
—
Cached input tokens
Not offered
First seen on OpenRouter
3 Jun 2025
Capabilities
Not supported: Reasoning
Not supported: Tool use
Not supported: Structured output
Not supported: Response format
Not supported: Moderated
Weights & access
Gated access. The weights are hosted behind an access request or acceptance step. Review the publisher's access requirements and license before use or redistribution.
gemma 3n E4B it holds ModelCap Index position #173 of 248 ranked language models as of 22 September 2026, with an Index score of 26.4 (interval 21.4–31.4); evidence: measured. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.
How much does gemma 3n E4B it cost per 1M tokens?
No live API price is listed for gemma 3n E4B it in the current snapshot as of 22 September 2026 because the latest endpoint refresh was unavailable. ModelCap shows a price only when a provider currently lists one.
What is the context window of gemma 3n E4B it?
gemma 3n E4B it has a published context window of 33K tokens (32,768). Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.
Is gemma 3n E4B it open-weight?
Gated access (gemma). The weights are hosted behind an access request or acceptance step. Review the publisher's access requirements and license before use or redistribution. The repository is google/gemma-3n-E4B-it on Hugging Face.
Which benchmarks has gemma 3n E4B it been evaluated on?
gemma 3n E4B it has published results on 1 public board tracked by ModelCap as of 22 September 2026: Arena coding 1307 (#291 of 397). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.
How does gemma 3n E4B it compare with Llama 3.3 Nemotron 70B Edit?
gemma 3n E4B it ranks #173 (Index 26.4) and Llama 3.3 Nemotron 70B Edit ranks #174 (Index 26.1) on the ModelCap Index as of 22 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.
When was gemma 3n E4B it released?
gemma 3n E4B it first appeared in the catalogue on 3 Jun 2025. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.