R1 0528 vs Muse Spark Open
Updated October 21, 2026. 6 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 62.1 for Muse Spark Open, though on 1 benchmark against 2.
Pick Muse Spark Open when the context window is larger, 1.05M against 164K tokens, or when what matters is ALE-Bench or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 6
- R1 0528 only
- 53
- Muse Spark Open only
- 20
- Like-for-like categories
- 4 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
R1 0528
R1 0528 scores 71.4 against 62.1.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only R1 0528 has a AA Intelligence Index result, 13.1.
Directional only
Web design
Design Arena: websites
Muse Spark Open
Muse Spark Open leads, 1311 against 1149.
Same board, both measured
Long documents
Listed context window
Muse Spark Open
Muse Spark Open takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
R1 0528
R1 0528 $0.0016, Muse Spark Open $0.0034 per turn.
List prices
At a glance
- Coding index
- R1 0528 71.4 from 1 benchmark, Muse Spark Open 62.1 from 2 benchmarks.
- Widest category
- Coding, 26.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): R1 0528 15.8% against Muse Spark Open 43.9%, 28.1 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.15 against Muse Spark Open $4.25 per million output tokens, 2.0x apart.
- Context window
- R1 0528 takes 164K tokens, Muse Spark Open 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | R1 0528 | Bars | Muse Spark Open | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 6 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index01
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveBench coding | Not measured | 73.6% | Only Muse Spark Open measured |
| LMArena WebDev | Not measured | 1528 | Only Muse Spark Open measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 6.6% | Only Muse Spark Open measured |
| Vibe Code Bench (Vals AI) | Not measured | 76.2% | Only Muse Spark Open measured |
| Code migration (Vals AI) | Not measured | 21.2% | Only Muse Spark Open measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| SciCode | Not measured | 58.4% | Only Muse Spark Open measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | Not measured | Only R1 0528 measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | 1590.1 | Muse Spark Open |
| LMArena Text: coding | 1427 | Not measured | Only R1 0528 measured |
| LMArena Text: software and IT | 1435 | Not measured | Only R1 0528 measured |
| LMArena WebDev: React | Not measured | 1529 | Only Muse Spark Open measured |
| LMArena WebDev: HTML | Not measured | 1495 | Only Muse Spark Open measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | Not measured | Only R1 0528 measured |
| Epoch Capabilities Index | Not measured | 155.1 | Only Muse Spark Open measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 80.3% | Only Muse Spark Open measured |
| LiveBench browser | Not measured | 36.0% | Only Muse Spark Open measured |
| LiveBench long agentic | Not measured | 18.9% | Only Muse Spark Open measured |
| Finance Agent (Vals AI) | Not measured | 61.3% | Only Muse Spark Open measured |
| Tax Agent Bench (Vals AI) | Not measured | 26.3% | Only Muse Spark Open measured |
| Legal Research Bench (Vals AI) | Not measured | 38.5% | Only Muse Spark Open measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 53.7% | Only Muse Spark Open measured |
| τ²-Bench Telecom | 36.5% | Not measured | Only R1 0528 measured |
| APEX-Agents | Not measured | 33.4% | Only Muse Spark Open measured |
| METR time horizons | 53.8% | Not measured | Only R1 0528 measured |
| DeepResearch Bench | 35.1% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 73.7% | Only Muse Spark Open measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | 89.0% | Muse Spark Open |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 43.9% | Muse Spark Open |
| GPQA Diamond (Epoch AI run) | 76.3% | Not measured | Only R1 0528 measured |
| ARC-AGI | 21.2% | Not measured | Only R1 0528 measured |
| ARC-AGI-2 | 1.1% | Not measured | Only R1 0528 measured |
| SimpleBench | 40.8% | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only R1 0528 measured |
| LMArena Text: expert | 1394 | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts in English | 1422 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| AA Math Index | 76.0 | Not measured | Only R1 0528 measured |
| AIME 2025 (Artificial Analysis run) | 76.0% | Not measured | Only R1 0528 measured |
| OTIS Mock AIME | 66.4% | Not measured | Only R1 0528 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | Not measured | Only R1 0528 measured |
| LMArena Text: mathematical work | 1415 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| LMArena Text: science | 1430 | Not measured | Only R1 0528 measured |
| LMArena Text: medicine and healthcare | 1445 | Not measured | Only R1 0528 measured |
| LMArena Text: legal and government | 1419 | Not measured | Only R1 0528 measured |
| LMArena Text: business and finance | 1402 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | 1311 | Muse Spark Open |
| Design Arena: UI components | 1102 | Not measured | Only R1 0528 measured |
| Design Arena: game dev | 1098 | 1290 | Muse Spark Open |
| Design Arena: data viz | 1182 | Not measured | Only R1 0528 measured |
| Design Arena: 3D | 1111 | Not measured | Only R1 0528 measured |
| Design Arena: all code categories | 1139 | Not measured | Only R1 0528 measured |
| Design Arena: SVG | 1038 | Not measured | Only R1 0528 measured |
| LiveBench design | Not measured | 60.3% | Only Muse Spark Open measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 60.2% | Only Muse Spark Open measured |
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | 78.3% | Muse Spark Open |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| LMArena Text | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: instruction following | 1382 | Not measured | Only R1 0528 measured |
| LMArena Text: creative writing | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: English | 1437 | Not measured | Only R1 0528 measured |
| LMArena Text: non-English | 1412 | Not measured | Only R1 0528 measured |
| LMArena Text: Chinese | 1442 | Not measured | Only R1 0528 measured |
| LMArena Text: German | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: Japanese | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: Korean | 1360 | Not measured | Only R1 0528 measured |
| LMArena Text: French | 1417 | Not measured | Only R1 0528 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only R1 0528 measured |
| LMArena Text: Russian | 1423 | Not measured | Only R1 0528 measured |
| LMArena Text: Polish | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: multi-turn | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: longer queries | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: writing and language | 1402 | Not measured | Only R1 0528 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | Muse Spark Open | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
R1 0528 costs less
Repository review
50K input + 3K output tokens
R1 0528 costs less
Agent loop
200K input + 10K output tokens
R1 0528 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek R1 0528 | Meta Muse Spark Open |
|---|---|---|
| Maker | DeepSeek | Meta |
| Weights | Open weights | Open weights |
| Released | May 28, 2025 | October 15, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.50 | $1.25 |
| Output $/Mtok | $2.15 | $4.25 |
| Coding index | 71.4 | 62.1 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is R1 0528 better than Muse Spark Open for coding?
Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 62.1 for Muse Spark Open, though on 1 benchmark against 2. Pick Muse Spark Open when the context window is larger, 1.05M against 164K tokens, or when what matters is ALE-Bench or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, R1 0528 or Muse Spark Open?
R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; Muse Spark Open costs $1.25 and $4.25. Cost per solved task is known for neither of them.
Which has the bigger context window, R1 0528 or Muse Spark Open?
Muse Spark Open, with 1.05M tokens against 164K.
How many benchmarks is the R1 0528 vs Muse Spark Open comparison based on?
6 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and Muse Spark Open's on 2. Where only one model has a result, the page says so instead of naming a winner.