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/gemma-3-4b-it
k2-fsa/OmniVoice
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$4.00
n/a
—
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$5.30
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)
$5.40
n/a
—
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$3.50
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)
$2.05
n/a
—
Capabilities side by side
Capability
google/gemma-3-4b-it
k2-fsa/OmniVoice
What it lets you do
Tool / function calling
—
—
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
—
return JSON that validates against your schema
Reasoning / thinking mode
—
—
spend extra tokens thinking before answering hard problems
Image input
✓ Yes
—
read screenshots, charts and scanned pages
Audio input
—
✓ Yes
take speech or audio directly
File / PDF input
—
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
—
—
answer from live web results
Seed (reproducible sampling)
✓ Yes
—
repeat a generation for tests and evals
Log-probabilities
✓ Yes
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
you need structured output (JSON schema) — to return JSON that validates against your schema; k2-fsa/OmniVoice does not advertise it
you need image input — to read screenshots, charts and scanned pages; k2-fsa/OmniVoice does not advertise it
you need seed (reproducible sampling) — to repeat a generation for tests and evals; k2-fsa/OmniVoice does not advertise it
you need log-probabilities — to read token confidence for classification and scoring; k2-fsa/OmniVoice does not advertise it
you want to prototype at zero cost — a free variant is listed
Choose k2-fsa/OmniVoice if…
you need audio input — to take speech or audio directly; google/gemma-3-4b-it does not advertise it
Frequently asked questions
Is google/gemma-3-4b-it better than k2-fsa/OmniVoice?
Neither wins outright: google/gemma-3-4b-it is the pick when you need structured output (JSON schema), k2-fsa/OmniVoice when you need audio input. In short: google/gemma-3-4b-it when you need structured output (JSON schema), you need image input, you need seed (reproducible sampling), you need log-probabilities, you want to prototype at zero cost; k2-fsa/OmniVoice when you need audio input.
Do google/gemma-3-4b-it and k2-fsa/OmniVoice support function calling and JSON output?
Tool calling — google/gemma-3-4b-it: not advertised, k2-fsa/OmniVoice: not advertised. JSON-schema structured output — google/gemma-3-4b-it: yes, k2-fsa/OmniVoice: not advertised.
Can google/gemma-3-4b-it or k2-fsa/OmniVoice read images?
Only google/gemma-3-4b-it accepts image input; k2-fsa/OmniVoice is text-only for input.
Is google/gemma-3-4b-it or k2-fsa/OmniVoice free to use?
google/gemma-3-4b-it has a free variant (google/gemma-3-4b-it:free). Free variants usually carry tighter rate limits.
Has the price of google/gemma-3-4b-it or k2-fsa/OmniVoice changed?
Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...