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Data

Data as of October 17, 2026 · How the score is built

Muse Spark 1.3

Decision readingMuse Spark 1.3 is tracked, but not publicly ranked yet. The profile exposes 20 sourced benchmark rows and leaves unsupported fields blank until a published record exists.

Released Sep 2, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

Unranked

field median 49.6Not eligible for a public rank

Price

$1.25input / $4.25 output

input median $0.75cached $0.15 · blended $2.75

Speed

128tok/s

field median 92 tok/sFirst token 71.21 s

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Reasoning ranks #6. Particularly strong for complex reasoning, multi-step problem solving, and analytical tasks.

Validate before choosing

20 published rows leave some tracked benchmark slots empty.

Source-linked · 20 displayable benchmark rows

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank Not rankedWeight 22%9 benchmarksVerified
75.3
CodingRank Not rankedWeight 20%6 benchmarksVerified
68.7
ReasoningRank #6 of 28Percentile 81stWeight 17%2 benchmarksVerified
82.9
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeWeight 12%0 benchmarksNot measured
Not measured
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%1 benchmarkVerified
Score pending
MathRank Not rankedWeight 5%2 benchmarksVerified
78.5

20 of 681 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic9/9 verified
  2. Coding6/6 verified
  3. Reasoning2/2 verified
  4. MultimodalNot measured
  5. KnowledgeNot measured
  6. MultilingualNot measured
  7. Inst. Following1/1 verified
  8. Math2/2 verified
Verified sourceProvisionalNot measured

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding6 rows
Coding benchmark values, best verified comparison, weight, and source status
FrontierSWE v2Score25.8%Versus best verified row

Best verified: GPT-6 Astra · 65.5%

Gap39.7 behindWeight8% ref. weight
DeepSWEScore75.4%Versus best verified row

Best verified: Muse Wonder Full · 79.0%

Gap3.6 behindWeightDisplay only
Terminal-Bench 2.1Score88.8%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap4 behindWeightDisplay only
SWE-Atlas Codebase QnAScore59.4%Versus best verified row

Best verified: Ling 3.1 Flash · 64.5%

Gap5.1 behindWeightDisplay only
CursorBench 4.0Score41.6%Versus best verified row

Best verified: Claude Opus 5.5 · 57.8%

Gap16.2 behindWeightDisplay only
Bug Hunt BenchScore32.2 fixesVersus best verified row

Best verified: Claude Sonnet 5.5 · 51.3 fixes

Gap19.1 behindWeightDisplay only
Agentic9 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore72.3%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap15 behindWeight4% ref. weight
Terminal-Bench 2.1Score88.8%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap4 behindWeightDisplay only
JobBenchScore64.9%Versus best verified row

Best verified: Muse Wonder Full · 75.3%

Gap10.4 behindWeightDisplay only
AutomationBenchScore49.4%Versus best verified row

Best verified: DeepSeek V4.1 Flash 1017 · 60.5%

Gap11.1 behindWeightDisplay only
DeepSearchQAScore89.4%Versus best verified row

Best verified: Atria Dawn Preview · 96.0%

Gap6.6 behindWeightDisplay only
OSWorld 2.0Score66.9%Versus best verified row

Best verified: GPT-6 Astra · 72.6%

Gap5.7 behindWeightDisplay only
ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable jobScore19%Versus best verified row

Best verified: Claude Fable 5.1 · 72%

Gap53 behindWeightDisplay only
CWE-bench v1Score55.0%Versus best verified row

Best verified: GPT-6 Astra · 68.0%

Gap13 behindWeightDisplay only
MazeBenchMazeBench: visual spatial reasoning in a 3D open worldScore0%Versus best verified row

Best verified: GPT-6 Astra · 23%

Gap23 behindWeightDisplay only
Reasoning2 rows
Reasoning benchmark values, best verified comparison, weight, and source status
MRCR v2 256K-512KOpenAI MRCR v2 8-needle 256K-512KScore98.5%Versus best verified row

Best verified: GPT-6 Astra · 100.0%

Gap1.5 behindWeightDisplay only
MRCR v2 512K-1MOpenAI MRCR v2 8-needle 512K-1MScore98.1%Versus best verified row

Best verified: Muse Spark 1.3 · 98.1%

GapBest verifiedWeightDisplay only
Inst. Following1 row
Inst. Following benchmark values, best verified comparison, weight, and source status
Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attemptsScore15.9%Versus best verified row

Best verified: Gemini 4 Argon · 0.7%

Gap15.2 behindWeightDisplay only
Math2 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score74.035%Versus best verified row

Best verified: GPT-6 Astra · 93.684%

Gap19.6 behindWeightWeighted 30%
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4Score46.341%Versus best verified row

Best verified: GPT-6.1 Sol · 100.000%

Gap53.7 behindWeightWeighted 10%

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Apr 8, 2026

    Muse Spark

    Score 60.5 · Price not listed

  2. Jul 9, 2026

    Muse Spark 1.1

    Score 66.0 · Price not listed

  3. Aug 5, 2026

    Muse Spark 1.2

    Score 65.8 · $1.25 / $4.25

  4. Sep 2, 2026 · you are here

    Muse Spark 1.3

    Not publicly ranked · $1.25 / $4.25

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
muse-spark-1.3Meta Muse Spark 1.3 model page
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
text, image, video, documentMeta Muse Spark 1.3 model page
Output modalities
textMeta Muse Spark 1.3 model page
Parameters
Not disclosed by the provider
Availability
Meta Model API · Muse CodeMeta AI Research Muse Spark 1.3 launch
Cloud regions
Not tracked yet
API capabilities
OpenAI-compatible requests · tool calling · native image, video, and document perceptionMeta Muse Spark 1.3 model page
Prompt caching
Published at $0.15 per million cached input tokensMeta: Muse Spark 1.3 model page
Self-host
Weights are not published
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

We track Muse Spark 1.3, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.

Muse Spark 1.3 is a proprietary model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Available through Muse Code and Meta Model API as `muse-spark-1.3`. Meta also offers a lower-priced `muse-spark-1.3-contributor` SKU whose data may be used to improve Meta products. Previously available reasoning modes are live; Meta says max reasoning will follow additional safety testing.

Muse Spark 1.3 sits in the Muse Spark family with Muse Spark 1.1, Muse Spark, Muse Spark 1.2. Its explicit predecessor is Muse Spark 1.2. 20 of 681 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Reasoning at #6. particularly strong for complex reasoning, multi-step problem solving, and analytical tasks.

Last updated October 17, 2026. Runtime fields remain blank until a sourced snapshot exists.

Questions

How does Muse Spark 1.3 perform overall in AI benchmarks?

Muse Spark 1.3 has 20 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.

Is Muse Spark 1.3 good for coding and programming?

Muse Spark 1.3 has source-displayable benchmark coverage for coding and programming, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Muse Spark 1.3 good for mathematics?

Muse Spark 1.3 has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Muse Spark 1.3 good for reasoning and logic?

Muse Spark 1.3 ranks #6 out of 28 eligible models for reasoning and logic, with a public category score of 82.9/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Muse Spark 1.3 good for agentic tool use and computer tasks?

Muse Spark 1.3 has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Muse Spark 1.3 good for instruction following?

Muse Spark 1.3 has source-displayable benchmark coverage for instruction following, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Which sibling models are related to Muse Spark 1.3?

Muse Spark 1.3 belongs to the Muse Spark family. Related tracked variants include Muse Spark 1.1, Muse Spark, Muse Spark 1.2. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does Muse Spark 1.3 have full benchmark coverage on BenchLM?

No. Muse Spark 1.3 currently has 43 source-displayable rows across 681 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of Muse Spark 1.3?

Muse Spark 1.3 has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

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Compare Muse Spark 1.3 with every tracked model953 comparisons