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Model Comparison

Muse Spark 1.2 vs GPT AudioWhich Is Better in 2026?

Meta: Muse Spark 1.2 logoMeta•OpenAI: GPT Audio logoOpenAI

Muse Spark 1.2 vs GPT Audio: which should you choose in 2026?

Muse Spark 1.2 (by Meta) and GPT Audio (by OpenAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Choose Muse Spark 1.2 if…

  • • you need a model with explicit reasoning support
  • • you need image inputs

Choose GPT Audio if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Muse Spark 1.2 and GPT Audio benchmark results
BenchmarkMuse Spark 1.2GPT Audio
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

——
BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

——
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

——
DeepSWE 1.1

A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests.

Pass@1

55.0%±2.0
DataCurve
—
SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

——
Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

——
AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

——
GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

90.4%
Artificial Analysis
—
Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

45.5%
Artificial Analysis
—
LiveBench

A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol.

Mean of category averages

78.0%
LiveBench
—

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

Muse Spark 1.2 and GPT Audio model facts
FeatureMuse Spark 1.2GPT Audio
Context & model facts
DeveloperMetaOpenAI
API providerMetaOpenAI
Input context1,048,576 tokens128,000 tokens
Maximum output—16,384 tokens
Released——
Added to WritingmateAug 5, 2026Jan 19, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsText, Image, Video, File, AudioText, Audio
OutputsTextText, Audio
Provider endpoint accepts tool parametersYesYes
ReasoningYesNo
VisionYesNo
Image GenerationNoNo
Video GenerationNoNo
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated Aug 5, 2026.

Why Pay for Multiple Subscriptions?

Comparing Muse Spark 1.2 from Meta with GPT Audio from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Muse Spark 1.2 and GPT Audio
PlanPriceMuse Spark 1.2GPT AudioAI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

Muse Spark 1.2 vs GPT Audio FAQ

Which is better, Muse Spark 1.2 or GPT Audio?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Which model is cheaper to use through an API?

A comparable blended token price is not available for both models. Check the API pricing rows for the values that are currently published.

Which model supports more context?

Comparable context-window data is not available for both models.

Can I switch between Muse Spark 1.2 and GPT Audio?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.