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BenchLM
Model A
Llama 4 Maverick

Meta

22.6/100

Supported · Public rank #216

90% interval 15.0–30.3

Llama 4 Maverick vs Muse Spark

Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Muse Spark

Meta

70.6/100

Supported · Public rank #17

90% interval 61.0–80.2

Decision reading

Muse Spark has the higher public score, 70.6 versus 22.63, and the 90% score intervals do not overlap.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    Llama 4 Maverick

    Llama 4 Maverick 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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

What is actually comparable

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

Shared results
1
Llama 4 Maverick only
0
Muse Spark only
23
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Math

Directional only
Llama 4 Maverick
0.7
Muse Spark
32.9
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
59.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
67.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
42.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
50.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
82.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Llama 4 Maverick
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

  • FrontierMath v2 (Tiers 1-3)

    Math

    Llama 4 Maverick: 0.690%Muse Spark: 39.000%Normalized gap 38.3Shared source

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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark
API rate not published
Fits in one request

Llama 4 Maverick has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark
API rate not published
Fits in one request

Llama 4 Maverick has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Muse Spark
API rate not published
Fits in one request
Cached-input rate unavailable

Llama 4 Maverick has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Specification differences

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

Context window

Maximum documented context; output-token limits may be lower.

Llama 4 Maverick

1M

Muse Spark

262K

API model ID

Llama 4 Maverick

Not sourced

Muse Spark

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Llama 4 Maverick

No comparable hosted API rate

Muse Spark

No comparable hosted API rate

Documented inputs

Llama 4 Maverick

Not sourced

Muse Spark

Not sourced

Documented outputs

Llama 4 Maverick

Not sourced

Muse Spark

Not sourced

Provider availability

Llama 4 Maverick

Not sourced

Muse Spark

Not sourced

Reasoning profile

Llama 4 Maverick

Non-Reasoning

Muse Spark

Reasoning

Weight access

Llama 4 Maverick

Open Weight

Muse Spark

Proprietary

License

Llama 4 Maverick

Open Weight

Muse Spark

Proprietary

Release date

Llama 4 Maverick

2026-02-28

Muse Spark

2026-04-08

If you already use one of these models
Deployment change
Both entries list Meta as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
Muse Spark has the higher public score, 70.6 versus 22.63, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Llama 4 Maverick has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

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

Llama 4 Maverick
API / mo$0
Self-host / mo$2,610
Break-even—
Muse Spark
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
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 evidence24 rows

Agentic

  • Terminal-Bench 2.0

    Llama 4 Maverick—
    Muse Spark59%
    Source

    Not directly comparable

  • τ²-bench results

    Llama 4 Maverick—
    Muse Spark91.5%
    Source

    Not directly comparable

  • DeepSearchQA

    Llama 4 Maverick—
    Muse Spark74.8%
    Source

    Not directly comparable

  • CyberGym

    Llama 4 Maverick—
    Muse Spark43.5%
    Source

    Not directly comparable

  • Claw-Eval

    Llama 4 Maverick—
    Muse Spark63.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Llama 4 Maverick—
    Muse Spark77.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Llama 4 Maverick—
    Muse Spark52.4%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    Llama 4 Maverick—
    Muse Spark80.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    Llama 4 Maverick—
    Muse Spark19.67%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Llama 4 Maverick—
    Muse Spark42.5%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Llama 4 Maverick—
    Muse Spark89.5%
    Source

    Not directly comparable

  • HLE

    Llama 4 Maverick—
    Muse Spark50.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Llama 4 Maverick—
    Muse Spark42.8%
    Source

    Not directly comparable

  • HealthBench Hard

    Llama 4 Maverick—
    Muse Spark42.8%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    Llama 4 Maverick—
    Muse Spark52.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Llama 4 Maverick0.690%
    Muse Spark39.000%

    Muse Spark leads this result

  • FrontierMath v2 (Tier 4)

    Llama 4 Maverick—
    Muse Spark14.600%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Llama 4 Maverick—
    Muse Spark86.4%
    Source

    Not directly comparable

  • MMMU-Pro

    Llama 4 Maverick—
    Muse Spark80.4%
    Source

    Not directly comparable

  • ERQA

    Llama 4 Maverick—
    Muse Spark64.7%
    Source

    Not directly comparable

  • SimpleVQA

    Llama 4 Maverick—
    Muse Spark71.3%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Llama 4 Maverick—
    Muse Spark84.1%
    Source

    Not directly comparable

  • ZeroBench

    Llama 4 Maverick—
    Muse Spark33.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    Llama 4 Maverick—
    Muse Spark78.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Llama 4 Maverick or Muse Spark?

Muse Spark has the higher public score, 70.6 versus 22.63, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Llama 4 Maverick or Muse Spark?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Llama 4 Maverick or Muse Spark?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Llama 4 Maverick or Muse Spark?

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, Llama 4 Maverick or Muse Spark?

Llama 4 Maverick has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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