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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 1.1

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Muse Spark 1.1

Meta

76.7/100

Supported · Public rank #7

90% interval 72.6–80.9

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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
0
Llama 4 Maverick only
1
Muse Spark 1.1 only
21
Like-for-like categories
0 / 8

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.

Agentic

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
80.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
61.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
62.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Llama 4 Maverick
0.7
Muse Spark 1.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
88.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Llama 4 Maverick
Not measured
Muse Spark 1.1
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.

A shared-evidence shape is not available.

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

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 1.1
API rate not published
Fits in one request

Llama 4 Maverick has no comparable published API token rate. Muse Spark 1.1 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 1.1
API rate not published
Fits in one request

Llama 4 Maverick has no comparable published API token rate. Muse Spark 1.1 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 1.1
API rate not published
Fits in one request
Cached-input rate unavailable

Llama 4 Maverick has no comparable published API token rate. Muse Spark 1.1 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 1.1

1M

API model ID

Llama 4 Maverick

Not sourced

Muse Spark 1.1

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 1.1

No comparable hosted API rate

Documented inputs

Llama 4 Maverick

Not sourced

Muse Spark 1.1

Not sourced

Documented outputs

Llama 4 Maverick

Not sourced

Muse Spark 1.1

Not sourced

Provider availability

Llama 4 Maverick

Not sourced

Muse Spark 1.1

Not sourced

Reasoning profile

Llama 4 Maverick

Non-Reasoning

Muse Spark 1.1

Reasoning

Weight access

Llama 4 Maverick

Open Weight

Muse Spark 1.1

Proprietary

License

Llama 4 Maverick

Open Weight

Muse Spark 1.1

Proprietary

Release date

Llama 4 Maverick

2026-02-28

Muse Spark 1.1

2026-07-09

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 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 1.1
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 evidence22 rows

Agentic

  • Terminal-Bench 2.0

    Llama 4 Maverick—
    Muse Spark 1.180%
    Source

    Not directly comparable

  • MCP Atlas

    Llama 4 Maverick—
    Muse Spark 1.188.1%
    Source

    Not directly comparable

  • Toolathlon

    Llama 4 Maverick—
    Muse Spark 1.175.6%
    Source

    Not directly comparable

  • OSWorld-Verified

    Llama 4 Maverick—
    Muse Spark 1.180.8%
    Source

    Not directly comparable

  • WebArena-Verified

    Llama 4 Maverick—
    Muse Spark 1.169%
    Source

    Not directly comparable

  • DeepSearchQA

    Llama 4 Maverick—
    Muse Spark 1.184.9%
    Source

    Not directly comparable

  • CyberGym

    Llama 4 Maverick—
    Muse Spark 1.159.0%
    Source

    Not directly comparable

  • Finance Agent v2

    Llama 4 Maverick—
    Muse Spark 1.157.2%
    Source

    Not directly comparable

  • deepSwe

    Llama 4 Maverick—
    Muse Spark 1.153.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    Llama 4 Maverick—
    Muse Spark 1.114.2%
    Source

    Not directly comparable

  • JobBench

    Llama 4 Maverick—
    Muse Spark 1.154.7%
    Source

    Not directly comparable

  • Cybench

    Llama 4 Maverick—
    Muse Spark 1.192.9%
    Source

    Not directly comparable

  • ExploitGym

    Llama 4 Maverick—
    Muse Spark 1.10.8%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Llama 4 Maverick—
    Muse Spark 1.180.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    Llama 4 Maverick—
    Muse Spark 1.161.5%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    Llama 4 Maverick—
    Muse Spark 1.154.1%
    Source

    Not directly comparable

Knowledge

  • HLE

    Llama 4 Maverick—
    Muse Spark 1.162.1%
    Source

    Not directly comparable

  • HLE w/o tools

    Llama 4 Maverick—
    Muse Spark 1.152.2%
    Source

    Not directly comparable

  • HealthBench Professional

    Llama 4 Maverick—
    Muse Spark 1.159.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Llama 4 Maverick0.690%
    Source
    Muse Spark 1.1—

    Not directly comparable

Multimodal

  • CharXiv

    Llama 4 Maverick—
    Muse Spark 1.188.4%
    Source

    Not directly comparable

  • BabyVision

    Llama 4 Maverick—
    Muse Spark 1.176.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Llama 4 Maverick or Muse Spark 1.1?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Llama 4 Maverick or Muse Spark 1.1?

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 1.1?

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 1.1?

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 1.1?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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