Llama 4 Maverick vs Muse Spark 1.2

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Llama 4 Maverick

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
Llama 4 Maverick 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
Llama 4 Maverick is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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

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.

Pick the job you would use them for

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

25.6Llama 4 Maverick60.0Muse Spark 1.2

Directional only · BenchAlign

Muse Spark 1.2 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

Open the coding leaderboard Shortlist models for this job

What is actually comparable

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

1 Llama 4 Maverick only

8 Muse Spark 1.2 only

Shared results: 0
Llama 4 Maverick only: 1
Muse Spark 1.2 only: 8
Like-for-like categories: 0 / 8

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Llama 4 Maverick: 24.2; Estimated · #151/154
Muse Spark 1.2: 61.0; Supported · #16/154
Basis: BenchAlign lane · 0 vs 2 public rows
Reading: Directional only

Coding

Directional only

Llama 4 Maverick: 25.6; Estimated · #150/154
Muse Spark 1.2: 60.0; Supported · #21/154
Basis: BenchAlign lane · 0 vs 5 public rows
Reading: Directional only

Knowledge

Directional only

Llama 4 Maverick: 30.8; Estimated · #178/184
Muse Spark 1.2: 70.7; Estimated · #10/184
Basis: BenchAlign lane · 0 vs 1 public rows
Reading: Directional only

Reasoning

Not comparable

Llama 4 Maverick: 55.4; Unranked · 2 rankable rows
Muse Spark 1.2: 75.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Llama 4 Maverick: 25.1; Unranked · 1 rankable row
Muse Spark 1.2: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Llama 4 Maverick: Not ranked
Muse Spark 1.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Llama 4 Maverick: 51.6; Unranked · 1 rankable row
Muse Spark 1.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Llama 4 Maverick: 48.9; #82/124
Muse Spark 1.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Llama 4 Maverick and Muse Spark 1.2
Category	Llama 4 Maverick	Muse Spark 1.2	Basis	Reading
Agentic	24.2Estimated · #151/154	61.0Supported · #16/154	Directional onlyBenchAlign lane · 0 vs 2 public rows	Directional only
Coding	25.6Estimated · #150/154	60.0Supported · #21/154	Directional onlyBenchAlign lane · 0 vs 5 public rows	Directional only
Knowledge	30.8Estimated · #178/184	70.7Estimated · #10/184	Directional onlyBenchAlign lane · 0 vs 1 public rows	Directional only
Reasoning	55.4Unranked · 2 rankable rows	75.3Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	25.1Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	51.6Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	48.9#82/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

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

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 1.2: $0.00338; Fits in one request

Llama 4 Maverick 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 1.2: $0.07525; Fits in one request

Llama 4 Maverick 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 1.2: $0.0975; Fits in one request

Llama 4 Maverick 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.

SpecificationLlama 4 MaverickMuse Spark 1.2

Context window

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

Llama 4 Maverick

Muse Spark 1.2

Meta: Muse Spark 1.2 model page

API model ID

Llama 4 Maverick

Not sourced

Muse Spark 1.2

muse-spark-1.2

Meta: Muse Spark 1.2 model page

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 1.2

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.2 model page

Documented inputs

Llama 4 Maverick

Not sourced

Muse Spark 1.2

Not sourced

Documented outputs

Llama 4 Maverick

Not sourced

Muse Spark 1.2

Not sourced

Provider availability

Llama 4 Maverick

Not sourced

Muse Spark 1.2

Not sourced

Reasoning profile

Llama 4 Maverick

Non-Reasoning

Muse Spark 1.2

Reasoning

Weight access

Llama 4 Maverick

Open Weight

Muse Spark 1.2

Proprietary

License

Llama 4 Maverick

Open Weight

Muse Spark 1.2

Proprietary

Release date

Llama 4 Maverick

2026-02-28

Muse Spark 1.2

2026-08-05

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 1.2

API / mo$4,125

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 evidence9 rows

Agentic

Terminal-Bench 2.1
Llama 4 Maverick—
Muse Spark 1.282.9%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Llama 4 Maverick—
Muse Spark 1.269.7%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Llama 4 Maverick—
Muse Spark 1.282.9%
Source
Not directly comparable
DeepSWE
Llama 4 Maverick—
Muse Spark 1.259.3%
Source
Not directly comparable
VulcanBench v3
Llama 4 Maverick—
Muse Spark 1.287.0%
Source
Not directly comparable
FrontierSWE v2
Llama 4 Maverick—
Muse Spark 1.212.0%
Source
Not directly comparable
SWE-bench (Vals)
Llama 4 Maverick—
Muse Spark 1.286.6%
Source
Not directly comparable

Knowledge

MMLU-Pro (Vals)
Llama 4 Maverick—
Muse Spark 1.288.3%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Llama 4 Maverick0.690%
Source
Muse Spark 1.2—
Not directly comparable

Questions

Which is better, Llama 4 Maverick or Muse Spark 1.2?

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 1.2?

Muse Spark 1.2 scores higher for coding on the public lane, 60 to 25.6. Llama 4 Maverick 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, Llama 4 Maverick or Muse Spark 1.2?

Muse Spark 1.2 scores higher for agentic tasks on the public lane, 61 to 24.2. Llama 4 Maverick is 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, Llama 4 Maverick or Muse Spark 1.2?

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 1.2?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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Llama 4 Maverick vs Muse Spark 1.2

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Questions

Which is better, Llama 4 Maverick or Muse Spark 1.2?

Which is better for coding, Llama 4 Maverick or Muse Spark 1.2?

Which is better for agentic tasks, Llama 4 Maverick or Muse Spark 1.2?

Which costs less, Llama 4 Maverick or Muse Spark 1.2?

Which has the larger context window, Llama 4 Maverick or Muse Spark 1.2?

Related comparisons

Watch Llama 4 Maverick vs Muse Spark 1.2