Muse Spark 1.1 vs DeepSeek V4 Flash
Head-to-head specifications
| Metric | Muse Spark 1.1 | DeepSeek V4 Flash | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 40.0 | +27.5% |
| Coding Index | 71.3 | 56.2 | +26.9% |
| Agentic Index | 37.5 | 31.1 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.62 | $0.06 | +933.3% |
| Output speed (tokens/s) | 118 | 102 | +15.7% |
| Access | Proprietary API | Open weights | — |
- Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 40.0).
- DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 10.3× cheaper.
Verdict: Muse Spark 1.1 or DeepSeek V4 Flash?
Muse Spark 1.1 advantages
- General intelligence (+22%)
- Coding ability (+21%)
- Agentic task performance (+17%)
- Output speed (+14%)
DeepSeek V4 Flash advantages
- Affordability (+90%)
Which should you choose?
- Choose the Muse Spark 1.1 if you need the strongest overall reasoning and accuracy.
- Choose the DeepSeek V4 Flash if you want the lowest cost per token at scale.
- Choose the Muse Spark 1.1 if coding and software development are your main workload.
Value for money
DeepSeek V4 Flash offers more intelligence per dollar (8.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Muse Spark 1.1 vs DeepSeek V4 Flash: which should you choose?
Muse Spark 1.1 — Muse multimodal model with an Intelligence Index of 51, a 1M-token context window and a blended price of $0.62/1M tokens.
DeepSeek V4 Flash — DeepSeek text model with an Intelligence Index of 40, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
Muse Spark 1.1 vs DeepSeek V4 Flash: Muse Spark 1.1 scores higher on the Intelligence Index. Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 40.0). DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 10.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Muse Spark 1.1 scores 51.0 versus 40.0. For software development, the Coding Index puts Muse Spark 1.1 ahead (71.3 vs 56.2). On agentic, multi-step tool-use tasks, Muse Spark 1.1 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Muse Spark 1.1 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Muse Spark 1.1 generates faster (118 vs 102 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, DeepSeek V4 Flash is the cheaper model to run ($0.06 vs $0.62 per 1M tokens). Muse Spark 1.1 is proprietary api and DeepSeek V4 Flash is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Muse Spark 1.1 better than the DeepSeek V4 Flash?
Muse Spark 1.1 takes the overall edge, though DeepSeek V4 Flash wins in specific areas worth weighing. Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 40.0).
What is the main difference between the Muse Spark 1.1 and the DeepSeek V4 Flash?
Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 40.0). DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 10.3× cheaper.
Which is better value?
DeepSeek V4 Flash offers more intelligence per dollar (8.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Muse Spark 1.1 if you need the strongest overall reasoning and accuracy. Choose the DeepSeek V4 Flash if you want the lowest cost per token at scale.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.