DeepSeek: DeepSeek V4.1 Flash vs Meta: Muse Spark 1.3 Contributor
Neither wins outright: DeepSeek: DeepSeek V4.1 Flash is the pick when cost matters at scale, Meta: Muse Spark 1.3 Contributor when you need file / PDF input.
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)
DeepSeek: DeepSeek V4.1 Flash
Meta: Muse Spark 1.3 Contributor
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$8.28
$11.50
DeepSeek: DeepSeek V4.1 Flash saves $3.22
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$10.08
$14.00
DeepSeek: DeepSeek V4.1 Flash saves $3.92
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)
$10.37
$14.40
DeepSeek: DeepSeek V4.1 Flash saves $4.03
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$6.62
$9.20
DeepSeek: DeepSeek V4.1 Flash saves $2.58
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$3.74
$5.20
DeepSeek: DeepSeek V4.1 Flash saves $1.46
Capabilities side by side
Capability
DeepSeek: DeepSeek V4.1 Flash
Meta: Muse Spark 1.3 Contributor
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
—
—
take speech or audio directly
File / PDF input
—
✓ 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
—
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.
Price history
DeepSeek: DeepSeek V4.1 Flash: input price raised 56% ($0.03 → $0.07/1M) on 2026-10-18 (21 price changes since 2026-09-11).
Meta: Muse Spark 1.3 Contributor: price unchanged since we started tracking it on 2026-09-03.
Date
Model
Input / 1M
Output / 1M
2026-10-18
DeepSeek: DeepSeek V4.1 Flash
$0.07
$0.14
2026-10-17
DeepSeek: DeepSeek V4.1 Flash
$0.03
$0.14
2026-10-16
DeepSeek: DeepSeek V4.1 Flash
$0.01
$0.68
2026-10-15
DeepSeek: DeepSeek V4.1 Flash
$0.02
$0.89
2026-10-14
DeepSeek: DeepSeek V4.1 Flash
$0.04
$0.60
2026-10-06
DeepSeek: DeepSeek V4.1 Flash
$0.30
$1.20
2026-10-04
DeepSeek: DeepSeek V4.1 Flash
$0.0030
$2.40
2026-10-03
DeepSeek: DeepSeek V4.1 Flash
$0.30
$1.20
Which should you choose?
Choose DeepSeek: DeepSeek V4.1 Flash if…
cost matters at scale — it is 28% cheaper on a typical 3:1 input/output mix ($8.28 vs $11.50 a month for 50,000 support chats)
you need seed (reproducible sampling) — to repeat a generation for tests and evals; Meta: Muse Spark 1.3 Contributor does not advertise it
you need log-probabilities — to read token confidence for classification and scoring; Meta: Muse Spark 1.3 Contributor does not advertise it
Choose Meta: Muse Spark 1.3 Contributor if…
you need file / PDF input — to accept documents without your own parsing step; DeepSeek: DeepSeek V4.1 Flash does not advertise it
you need built-in web search — to answer from live web results; DeepSeek: DeepSeek V4.1 Flash does not advertise it
Frequently asked questions
Is DeepSeek: DeepSeek V4.1 Flash better than Meta: Muse Spark 1.3 Contributor?
Neither wins outright: DeepSeek: DeepSeek V4.1 Flash is the pick when cost matters at scale, Meta: Muse Spark 1.3 Contributor when you need file / PDF input. In short: DeepSeek: DeepSeek V4.1 Flash when cost matters at scale, you need seed (reproducible sampling), you need log-probabilities; Meta: Muse Spark 1.3 Contributor when you need file / PDF input, you need built-in web search.
Which is cheaper, DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 Contributor?
DeepSeek: DeepSeek V4.1 Flash is cheaper: $0.09 vs $0.13 per 1M tokens at a typical 3:1 input/output mix, 28% less. For 50,000 support conversations a month that is $8.28 against $11.50.
How much do DeepSeek: DeepSeek V4.1 Flash and Meta: Muse Spark 1.3 Contributor cost per million tokens?
DeepSeek: DeepSeek V4.1 Flash costs $0.07 per 1M input tokens and $0.14 per 1M output tokens; Meta: Muse Spark 1.3 Contributor costs $0.10 input and $0.20 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 Contributor?
Both take 1.0M tokens per request — roughly 1,573 pages of text.
Do DeepSeek: DeepSeek V4.1 Flash and Meta: Muse Spark 1.3 Contributor support function calling and JSON output?
Can DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 Contributor read images?
Yes, both accept image input.
Is DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 Contributor free to use?
No free variant is listed for either; both are pay-per-token.
What is the maximum output length of DeepSeek: DeepSeek V4.1 Flash and Meta: Muse Spark 1.3 Contributor?
DeepSeek: DeepSeek V4.1 Flash can write up to 944K tokens (about 707,800 words) per response; Meta: Muse Spark 1.3 Contributor up to 944K (about 707,800 words).
When were DeepSeek: DeepSeek V4.1 Flash and Meta: Muse Spark 1.3 Contributor released?
DeepSeek: DeepSeek V4.1 Flash was listed on 2026-09-10; Meta: Muse Spark 1.3 Contributor on 2026-09-02.
Has the price of DeepSeek: DeepSeek V4.1 Flash or Meta: Muse Spark 1.3 Contributor changed?
DeepSeek: DeepSeek V4.1 Flash: input price raised 56% ($0.03 → $0.07/1M) on 2026-10-18 (21 price changes since 2026-09-11). Meta: Muse Spark 1.3 Contributor: price unchanged since we started tracking it on 2026-09-03.
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information...