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 3.8 Flash
Meta: Muse Spark Open
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$144
$179
Google: Gemini 3.8 Flash saves $34.38
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$140
$193
Google: Gemini 3.8 Flash saves $52.25
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)
$149
$201
Google: Gemini 3.8 Flash saves $52.50
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$90.75
$126
Google: Gemini 3.8 Flash saves $34.75
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$45.38
$66.75
Google: Gemini 3.8 Flash saves $21.38
Capabilities side by side
Capability
Google: Gemini 3.8 Flash
Meta: Muse Spark Open
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
—
answer from live web results
Seed (reproducible sampling)
✓ Yes
✓ 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.
Meta: Muse Spark Open: price unchanged since we started tracking it on 2026-10-16.
Date
Model
Input / 1M
Output / 1M
2026-10-16
Google: Gemini 3.8 Flash
$0.82
$4.13
Which should you choose?
Choose Google: Gemini 3.8 Flash if…
cost matters at scale — it is 18% cheaper on a typical 3:1 input/output mix ($144 vs $179 a month for 50,000 support chats)
you need built-in web search — to answer from live web results; Meta: Muse Spark Open does not advertise it
Choose Meta: Muse Spark Open if…
you need log-probabilities — to read token confidence for classification and scoring; Google: Gemini 3.8 Flash does not advertise it
you generate long outputs in one go — up to 944K tokens (about 707,800 words) per response
Frequently asked questions
Is Google: Gemini 3.8 Flash better than Meta: Muse Spark Open?
Neither wins outright: Google: Gemini 3.8 Flash is the pick when cost matters at scale, Meta: Muse Spark Open when you need log-probabilities. In short: Google: Gemini 3.8 Flash when cost matters at scale, you need built-in web search; Meta: Muse Spark Open when you need log-probabilities, you generate long outputs in one go.
Which is cheaper, Google: Gemini 3.8 Flash or Meta: Muse Spark Open?
Google: Gemini 3.8 Flash is cheaper: $1.65 vs $2.00 per 1M tokens at a typical 3:1 input/output mix, 18% less. For 50,000 support conversations a month that is $144 against $179.
How much do Google: Gemini 3.8 Flash and Meta: Muse Spark Open cost per million tokens?
Google: Gemini 3.8 Flash costs $0.82 per 1M input tokens and $4.13 per 1M output tokens; Meta: Muse Spark Open costs $1.25 input and $4.25 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Google: Gemini 3.8 Flash or Meta: Muse Spark Open?
Both take 1.0M tokens per request — roughly 1,573 pages of text.
Do Google: Gemini 3.8 Flash and Meta: Muse Spark Open support function calling and JSON output?
Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.
Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers...