Muse Spark Open vs Saba
Updated October 21, 2026. 2 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; Saba has none, so the two cannot be ranked on coding.
Saba has the edge where output tokens cost 7.1x less.
- Shared results
- 2
- Muse Spark Open only
- 24
- Saba only
- 2
- Like-for-like categories
- 1 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, six public coding benchmarks
No call
Only Muse Spark Open has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Saba has a AA Intelligence Index result, 6.5.
Directional only
Web design
Design Arena: websites
No call
Only Muse Spark Open has a Design Arena: websites result, 1313.
Directional only
Long documents
Listed context window
Muse Spark Open
Muse Spark Open takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
Muse Spark Open $0.0034, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- Muse Spark Open 47.4 from 4 benchmarks, Saba not benchmarked.
- Widest category
- Reasoning, 43.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Muse Spark Open 89.0% against Saba 42.4%, 46.6 points apart on a 0 to 100 scale.
- Output price
- Muse Spark Open $4.25 against Saba $0.60 per million output tokens, 7.1x apart.
- Context window
- Muse Spark Open takes 1.05M tokens, Saba 33K.
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 | Muse Spark Open | Bars | Saba | Ties |
|---|---|---|---|---|
| Reasoning | 2 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | Not measured | Only Muse Spark Open measured |
| LMArena WebDev | 1528 | Not measured | Only Muse Spark Open measured |
| Terminal-Bench 4 (Vals AI) | 6.6% | Not measured | Only Muse Spark Open measured |
| Vibe Code Bench (Vals AI) | 76.2% | Not measured | Only Muse Spark Open measured |
| Code migration (Vals AI) | 21.2% | Not measured | Only Muse Spark Open measured |
| SciCode | 58.4% | Not measured | Only Muse Spark Open measured |
| ALE-Bench | 1590.1 | Not measured | Only Muse Spark Open measured |
| LMArena WebDev: React | 1529 | Not measured | Only Muse Spark Open measured |
| LMArena WebDev: HTML | 1495 | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.5 | Only Saba measured |
| Epoch Capabilities Index | 155.1 | Not measured | Only Muse Spark Open measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| LiveBench research | 80.3% | Not measured | Only Muse Spark Open measured |
| LiveBench browser | 36.0% | Not measured | Only Muse Spark Open measured |
| LiveBench long agentic | 18.9% | Not measured | Only Muse Spark Open measured |
| Finance Agent (Vals AI) | 61.3% | Not measured | Only Muse Spark Open measured |
| Tax Agent Bench (Vals AI) | 26.3% | Not measured | Only Muse Spark Open measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only Muse Spark Open measured |
| Excel Modeling Benchmark (Vals AI) | 53.7% | Not measured | Only Muse Spark Open measured |
| APEX-Agents | 33.4% | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| LiveBench reasoning | 73.7% | Not measured | Only Muse Spark Open measured |
| GPQA Diamond (Artificial Analysis run) | 89.0% | 42.4% | Muse Spark Open |
| Humanity's Last Exam (Artificial Analysis run) | 43.9% | 4.3% | Muse Spark Open |
| Neither model has a result on the other 18 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 61.1% | Only Saba measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| Design Arena: websites | 1313 | Not measured | Only Muse Spark Open measured |
| Design Arena: game dev | 1303 | Not measured | Only Muse Spark Open measured |
| LiveBench design | 60.3% | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 6 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Muse Spark Open | Saba | Leader |
|---|---|---|---|
| LiveBench data analysis | 60.2% | Not measured | Only Muse Spark Open measured |
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Spark Open | Saba | 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
Saba costs less
Repository review
50K input + 3K output tokens
Saba costs less
Agent loop
200K input + 10K output tokens
Saba 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 | Meta Muse Spark Open | Mistral Saba |
|---|---|---|
| Maker | Meta | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | October 15, 2026 | February 17, 2025 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $1.25 | $0.20 |
| Output $/Mtok | $4.25 | $0.60 |
| Coding index | 47.4 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Muse Spark Open better than Saba for coding?
Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; Saba has none, so the two cannot be ranked on coding. Saba has the edge where output tokens cost 7.1x less.
Which is cheaper, Muse Spark Open or Saba?
Muse Spark Open costs $1.25 per million input tokens and $4.25 per million output tokens; Saba costs $0.20 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Muse Spark Open or Saba?
Muse Spark Open, with 1.05M tokens against 33K.
How many benchmarks is the Muse Spark Open vs Saba comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Muse Spark Open's coding index rests on 4 independent benchmarks and Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.