DeepSeek V4 Pro vs Muse Spark 1.3
DeepSeek V4 Pro 
Muse Spark 1.3 Verdict
- Muse Spark 1.3 leads on quality: 68.3 vs 59.6.
- DeepSeek V4 Pro (high) is stronger in agents & tools, instruction following.
- Muse Spark 1.3 is stronger in composite, knowledge, long context.
- DeepSeek V4 Pro (high) is 3.7× cheaper ($0.544 vs $2.00 per 1M blended).
- Muse Spark 1.3 streams 3.2× faster (241 vs 75 tokens per second).
| Metric | DeepSeek V4 Pro (high) | Muse Spark 1.3 |
|---|---|---|
| BenchLeader Index | 59.6 | 68.3 |
| Agents & tools score | 63.5 | – |
| Composite score | 67.3 | 90.3 |
| Instruction following score | 70.1 | – |
| Knowledge score | 59.4 | 79.1 |
| Long context score | 61.7 | 68.2 |
| Blended price $/M | $0.544 | $2.00 |
| Output speed | 75 tok/s | 241 tok/s |
| Time to first answer | 28.2 s | 30.5 s |
| Context window | 1M | 1.0M |
| LMArena Agent | 4.1 | – |
| AA Intelligence Index | 30.1 | 48.2 |
| IFBench | 71.3% | – |
| AA-LCR | 70.3% | 83.0% |
| AA-Omniscience | -10.6 | 25 |
| Terminal-Bench Hard | 41.7% | – |
| GPQA Diamond (AA) | 90.5% | 93.5% |
| Humanity's Last Exam (AA) | 35.2% | 48.7% |
| SciCode (AA) | – | 58.8% |
| τ²-Bench Telecom (AA) | 94.2% | – |
| PRBench Finance | – | 59.5% |
| PRBench Legal | – | 61.6% |
Data as of 2026-09-14. Best configuration of each model; every score links to its source on the model pages.
DeepSeek V4 Pro vs Muse Spark 1.3: questions
- Is DeepSeek V4 Pro better than Muse Spark 1.3?
- Muse Spark 1.3 leads on quality: 68.3 vs 59.6. The BenchLeader Index combines every independent quality benchmark; Muse Spark 1.3 is ahead overall as of 2026-09-14, but check the category scores for your use.
- Which is cheaper, DeepSeek V4 Pro or Muse Spark 1.3?
- DeepSeek V4 Pro is cheaper: $0.544 against $2.00 per million tokens, blended at three input tokens per output token.
- Which is faster, DeepSeek V4 Pro or Muse Spark 1.3?
- Muse Spark 1.3 streams faster: 241 against 75 output tokens per second.
- Which has the larger context window?
- Muse Spark 1.3 accepts more context: 1.0M against 1M tokens.