Models, measured side by side
DeepSeek V4.1 Flash vs Muse Spark 1.3
Compare DeepSeek V4.1 Flash, Muse Spark 1.3 by token pricing, context length, benchmark results, speed and tool support.
Monthly workload
Monthly cost breakdown
InputOutput
DeepSeek V4.1 Flash
$7.00 / mo
Muse Spark 1.3
$46 / mo
Performance comparison
DeepSeek V4.1 Flash
39.5
Muse Spark 1.3
48.1
Overview
Pricing & capacity
| Pricing & capacity | DeepSeek V4.1 Flash | Muse Spark 1.3 |
|---|---|---|
| Input / 1M tokens | $0.05 | $1.25 |
| Output / 1M tokens | $1.20 | $4.25 |
| Cached input / 1M | $0.02 | $0.15 |
| Monthly cost | $7.00 | $46 |
| Context window | 1M | 1M |
| Maximum output | 944K | 944K |
Input / 1M tokens
- DeepSeek V4.1 Flash
- $0.05
- Muse Spark 1.3
- $1.25
Output / 1M tokens
- DeepSeek V4.1 Flash
- $1.20
- Muse Spark 1.3
- $4.25
Cached input / 1M
- DeepSeek V4.1 Flash
- $0.02
- Muse Spark 1.3
- $0.15
Monthly cost
- DeepSeek V4.1 Flash
- $7.00
- Muse Spark 1.3
- $46
Context window
- DeepSeek V4.1 Flash
- 1M
- Muse Spark 1.3
- 1M
Maximum output
- DeepSeek V4.1 Flash
- 944K
- Muse Spark 1.3
- 944K
Benchmarks & performance
| Benchmarks & performance | DeepSeek V4.1 Flash | Muse Spark 1.3 |
|---|---|---|
| Intelligence Index | 39.5 | 48.1 |
| GPQA | Not listed | 93.5% |
| Humanity’s Last Exam | 39.2% | 48.7% |
| SciCode | 51.9% | 58.8% |
| Output speed | 213 tokens/s | 146 tokens/s |
| Time to first token | 1.05 s | 47.86 s |
| DesignArena rating | 1,325 | 1,358 |
| DesignArena win rate | 52.1% | 57.3% |
Intelligence Index
- DeepSeek V4.1 Flash
- 39.5
- Muse Spark 1.3
- 48.1
GPQA
- DeepSeek V4.1 Flash
- Not listed
- Muse Spark 1.3
- 93.5%
Humanity’s Last Exam
- DeepSeek V4.1 Flash
- 39.2%
- Muse Spark 1.3
- 48.7%
SciCode
- DeepSeek V4.1 Flash
- 51.9%
- Muse Spark 1.3
- 58.8%
Output speed
- DeepSeek V4.1 Flash
- 213 tokens/s
- Muse Spark 1.3
- 146 tokens/s
Time to first token
- DeepSeek V4.1 Flash
- 1.05 s
- Muse Spark 1.3
- 47.86 s
DesignArena rating
- DeepSeek V4.1 Flash
- 1,325
- Muse Spark 1.3
- 1,358
DesignArena win rate
- DeepSeek V4.1 Flash
- 52.1%
- Muse Spark 1.3
- 57.3%
Tools & features
| Tools & features | DeepSeek V4.1 Flash | Muse Spark 1.3 |
|---|---|---|
| Function calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Log probabilities | Supported | Not listed |
| Deterministic seed | Supported | Not listed |
| Prompt caching | Supported | Supported |
Function calling
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Supported
Structured outputs
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Supported
JSON mode
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Supported
Reasoning
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Supported
Log probabilities
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Not listed
Deterministic seed
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Not listed
Prompt caching
- DeepSeek V4.1 Flash
- Supported
- Muse Spark 1.3
- Supported
API & availability
| API & availability | DeepSeek V4.1 Flash | Muse Spark 1.3 |
|---|---|---|
| API identifier | deepseek-v4.1-flash | muse-spark-1.3 |
| Prices checked | Oct 8, 2026 | Oct 8, 2026 |
API identifier
- DeepSeek V4.1 Flash
deepseek-v4.1-flash- Muse Spark 1.3
muse-spark-1.3
Prices checked
- DeepSeek V4.1 Flash
- Oct 8, 2026
- Muse Spark 1.3
- Oct 8, 2026
About the models
DeepSeek V4.1 Flash
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...
Full pricing & detailsMuse Spark 1.3
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through...
Full pricing & detailsComparison FAQ
DeepSeek V4.1 Flash: $0.05 input and $1.20 output per million tokens. Muse Spark 1.3: $1.25 input and $4.25 output per million tokens. The cheaper choice depends on your input-to-output ratio; a model can have a lower input rate but a higher output rate.
