google/electra-base-discriminator vs timm/efficientnet_b3.ra2_in1k

timm/efficientnet_b3.ra2_in1k comes out ahead on the data we track: 1 advantage and none for google/electra-base-discriminator.

Key numbers

Spec	google/electra-base-discriminator	timm/efficientnet_b3.ra2_in1k
Provider	Google	Hugging Face
Input price (per 1M tokens)	n/a	n/a
Output price (per 1M tokens)	n/a	n/a
Blended price (3:1 input/output)	n/a	n/a
Cheaper than … of all models we track	n/a	n/a
Context window	n/a	n/a
Larger context than … of all models	n/a	n/a
Max output per response	n/a	n/a
Input types	text	text
Output types	text	image
Free variant	No	No
Listed since	n/a	n/a

What it costs for real workloads

Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.

Workload (per month)	google/electra-base-discriminator	timm/efficientnet_b3.ra2_in1k	Difference
Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)	n/a	n/a	—
RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)	n/a	n/a	—
Coding / tool-using agent 2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)	n/a	n/a	—
Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)	n/a	n/a	—
Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)	n/a	n/a	—

Capabilities side by side

Capability	google/electra-base-discriminator	timm/efficientnet_b3.ra2_in1k	What it lets you do
Tool / function calling	—	—	call your APIs and run agent loops
Structured output (JSON schema)	—	—	return JSON that validates against your schema
Reasoning / thinking mode	—	—	spend extra tokens thinking before answering hard problems
Image input	—	—	read screenshots, charts and scanned pages
Audio input	—	—	take speech or audio directly
File / PDF input	—	—	accept documents without your own parsing step
Image output	—	✓ Yes	generate images, not just text
Built-in web search	—	—	answer from live web results
Seed (reproducible sampling)	—	—	repeat a generation for tests and evals
Log-probabilities	—	—	read token confidence for classification and scoring

From each model's published parameters and input/output types. “—” means not advertised, not proven absent.

Which should you choose?

Choose google/electra-base-discriminator if…

No advantage on the data we track — pick it for provider preference or output quality on your own prompts.

Choose timm/efficientnet_b3.ra2_in1k if…

you need image output — to generate images, not just text; google/electra-base-discriminator does not advertise it

Frequently asked questions

Is google/electra-base-discriminator better than timm/efficientnet_b3.ra2_in1k?

timm/efficientnet_b3.ra2_in1k comes out ahead on the data we track: 1 advantage and none for google/electra-base-discriminator.

Do google/electra-base-discriminator and timm/efficientnet_b3.ra2_in1k support function calling and JSON output?

Tool calling — google/electra-base-discriminator: not advertised, timm/efficientnet_b3.ra2_in1k: not advertised. JSON-schema structured output — google/electra-base-discriminator: not advertised, timm/efficientnet_b3.ra2_in1k: not advertised.