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BenchLM
Data

Muse Spark Open vs Sage-A1.5

Updated October 20, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.

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Decision reading

Sage-A1.5 has the higher public point estimate, 62.95 versus 62.76. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Meta logo
Muse Spark Open

Meta

62.76/100

Estimated · Public rank #47

Conditional range 48.4–77.1

Model B
InternScience logo
Sage-A1.5

InternScience

62.95/100

Estimated · Public rank #44

Conditional range 48.6–77.3

Shared results
1
Muse Spark Open only
6
Sage-A1.5 only
11
Like-for-like categories
0 / 8
Estimated: Muse Spark Open and Sage-A1.5. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    Muse Spark Open

    Muse Spark Open has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Sage-A1.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Muse Spark Open and Sage-A1.5 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

56.0Muse Spark Open54.0Sage-A1.5

Directional only · BenchAlign v5.8

Muse Spark Open has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
Muse Spark Open
60.2
Estimated · #28/132
Sage-A1.5
57.7
Estimated · #36/132
Basis
BenchAlign v5.8 lane · 4 vs 6 public rows
Reading
Directional only

Coding

Directional only
Muse Spark Open
56.0
Supported · #30/153
Sage-A1.5
54.0
Estimated · #34/153
Basis
BenchAlign v5.8 lane · 0 vs 3 public rows
Reading
Directional only

Reasoning

Not comparable
Muse Spark Open
78.8
Unranked · 2 rankable rows
Sage-A1.5
80.6
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Muse Spark Open
83.6
Unranked · 4 rankable rows
Sage-A1.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Muse Spark Open
64.7
Supported · #40/187
Sage-A1.5
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
Muse Spark Open
Not ranked
Sage-A1.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Muse Spark Open
Not ranked
Sage-A1.5
91.9
#22/152
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Muse Spark Open
Not ranked
Sage-A1.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Muse Spark Open
API rate not published
Fits in one request
Sage-A1.5
Self-hosted; infrastructure cost varies
Fits in one request

Muse Spark Open has no comparable published API token rate. Sage-A1.5 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Muse Spark Open
API rate not published
Fits in one request
Sage-A1.5
Self-hosted; infrastructure cost varies
Fits in one request

Muse Spark Open has no comparable published API token rate. Sage-A1.5 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Muse Spark Open
API rate not published
Fits in one request
Cached-input rate unavailable
Sage-A1.5
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Muse Spark Open has no comparable published API token rate. Sage-A1.5 has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

API model ID

Muse Spark Open

Not sourced

Sage-A1.5

Not sourced

Provider availability

Muse Spark Open

Open Weights · Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, Microsoft Foundry Models, self-hosted inference

Meta AI Research: Muse Spark Open

Sage-A1.5

Not sourced

Reasoning profile

Muse Spark Open

Reasoning

Sage-A1.5

Reasoning

Weight access

Muse Spark Open

Open Weight

Sage-A1.5

Open Weight

License

Muse Spark Open

Open Weight

Sage-A1.5

Open Weight

Release date

Muse Spark Open

2026-10-15

Sage-A1.5

2026-10-15

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
Sage-A1.5 has the higher public point estimate, 62.95 versus 62.76. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Muse Spark Open has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Muse Spark Open or Sage-A1.5?

Sage-A1.5 has the higher public point estimate, 62.95 versus 62.76. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Muse Spark Open or Sage-A1.5?

Muse Spark Open scores higher for coding on the public lane, 56 to 54. Sage-A1.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Muse Spark Open or Sage-A1.5?

Muse Spark Open scores higher for agentic tasks on the public lane, 60.2 to 57.7. Muse Spark Open and Sage-A1.5 are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Muse Spark Open or Sage-A1.5?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Muse Spark Open or Sage-A1.5?

Muse Spark Open has the larger documented context window: 1M, compared with 262K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Muse Spark Open
API / mo$0
Self-host / mo$27,962
Break-even—
Sage-A1.5
API / mo$0
Self-host / mo$298
Break-even—
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence18 rows

Agentic

  • JobBench

    Muse Spark Open61.8%
    Source
    Sage-A1.5—

    Not directly comparable

  • MCP Atlas

    Muse Spark Open89.2%
    Source
    Sage-A1.5—

    Not directly comparable

  • HLE w/ tools

    Muse Spark Open61.0%
    Source
    Sage-A1.555.1%
    Source

    Muse Spark Open leads this result

  • Toolathlon-Verified

    Muse Spark Open72.8%
    Source
    Sage-A1.5—

    Not directly comparable

  • GAIA

    Muse Spark Open—
    Sage-A1.595.1%
    Source

    Not directly comparable

  • BrowseComp

    Muse Spark Open—
    Sage-A1.581.7%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Muse Spark Open—
    Sage-A1.565.2%
    Source

    Not directly comparable

  • VITA-Bench

    Muse Spark Open—
    Sage-A1.545.4%
    Source

    Not directly comparable

  • PinchBench

    Muse Spark Open—
    Sage-A1.590.0%
    Source

    Not directly comparable

Coding

  • SciCode

    Muse Spark Open—
    Sage-A1.544.3%
    Source

    Not directly comparable

  • SWE-bench Pro

    Muse Spark Open—
    Sage-A1.561.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Muse Spark Open—
    Sage-A1.572.7%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Muse Spark Open—
    Sage-A1.520.0%
    Source

    Not directly comparable

Multimodal

  • ZeroBench

    Muse Spark Open50.0%
    Source
    Sage-A1.5—

    Not directly comparable

  • BabyVision

    Muse Spark Open80.2%
    Source
    Sage-A1.5—

    Not directly comparable

  • SimpleVQA

    Muse Spark Open73.6%
    Source
    Sage-A1.5—

    Not directly comparable

Knowledge

  • FrontierScience Research

    Muse Spark Open—
    Sage-A1.548.3%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Muse Spark Open—
    Sage-A1.581.6%
    Source

    Not directly comparable

18 public results · 1 shared

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Last updated October 20, 2026