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: Gemini 3.7 Flash
Meta: Muse Wonder
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$131
$700
Google: Gemini 3.7 Flash saves $569
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$127
$680
Google: Gemini 3.7 Flash saves $553
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)
$135
$720
Google: Gemini 3.7 Flash saves $585
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$82.50
$440
Google: Gemini 3.7 Flash saves $358
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$41.25
$220
Google: Gemini 3.7 Flash saves $179
Capabilities side by side
Capability
Google: Gemini 3.7 Flash
Meta: Muse Wonder
What it lets you do
Tool / function calling
✓ Yes
✓ Yes
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
✓ Yes
return JSON that validates against your schema
Reasoning / thinking mode
✓ Yes
✓ Yes
spend extra tokens thinking before answering hard problems
Image input
✓ Yes
✓ Yes
read screenshots, charts and scanned pages
Audio input
✓ Yes
✓ Yes
take speech or audio directly
File / PDF input
✓ Yes
✓ Yes
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
✓ Yes
✓ Yes
answer from live web results
Seed (reproducible sampling)
✓ Yes
—
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.
Meta: Muse Wonder: price unchanged since we started tracking it on 2026-10-13.
Date
Model
Input / 1M
Output / 1M
2026-08-29
Google: Gemini 3.7 Flash
$0.75
$3.75
Which should you choose?
Choose Google: Gemini 3.7 Flash if…
cost matters at scale — it is 81% cheaper on a typical 3:1 input/output mix ($131 vs $700 a month for 50,000 support chats)
you need seed (reproducible sampling) — to repeat a generation for tests and evals; Meta: Muse Wonder does not advertise it
Choose Meta: Muse Wonder if…
you generate long outputs in one go — up to 944K tokens (about 707,800 words) per response
Frequently asked questions
Is Google: Gemini 3.7 Flash better than Meta: Muse Wonder?
Neither wins outright: Google: Gemini 3.7 Flash is the pick when cost matters at scale, Meta: Muse Wonder when you generate long outputs in one go. In short: Google: Gemini 3.7 Flash when cost matters at scale, you need seed (reproducible sampling); Meta: Muse Wonder when you generate long outputs in one go.
Which is cheaper, Google: Gemini 3.7 Flash or Meta: Muse Wonder?
Google: Gemini 3.7 Flash is cheaper: $1.50 vs $8.00 per 1M tokens at a typical 3:1 input/output mix, 81% less. For 50,000 support conversations a month that is $131 against $700.
How much do Google: Gemini 3.7 Flash and Meta: Muse Wonder cost per million tokens?
Google: Gemini 3.7 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens; Meta: Muse Wonder costs $4.00 input and $20.00 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Google: Gemini 3.7 Flash or Meta: Muse Wonder?
Both take 1.0M tokens per request — roughly 1,573 pages of text.
Do Google: Gemini 3.7 Flash and Meta: Muse Wonder support function calling and JSON output?
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step...
Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with...