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Model Comparison

Muse Spark 1.2 vs GPT-5.5Which Is Better in 2026?

Meta: Muse Spark 1.2 logoMeta•OpenAI: GPT-5.5 logoOpenAI

Muse Spark 1.2 vs GPT-5.5: which should you choose in 2026?

Muse Spark 1.2 (by Meta) and GPT-5.5 (by OpenAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

Muse Spark 1.2 outperforms in 0 benchmarks, while GPT-5.5 is better at 2 benchmarks (DeepSWE 1.1, LiveBench).

GPT-5.5 leads every shared, protocol-compatible benchmark shown here.

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Choose Muse Spark 1.2 if…

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

Choose GPT-5.5 if…

  • • you want the model leading more of the 2 shared benchmarks

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.

Chat with both models
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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-5.5 benchmark results
BenchmarkMuse Spark 1.2GPT-5.5
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

—
42.1%
Artificial Analysis
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
67.0%±6.0
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

—
83.1%±1.1
Terminal-Bench
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
80.2%
LiveBench
Arena preference scores
Arena (Text)

Human preference score

—
1477 ±4

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 9, 2026.

Pricing, capabilities, and model facts

Muse Spark 1.2 and GPT-5.5 model facts
FeatureMuse Spark 1.2GPT-5.5
Context & model facts
DeveloperMetaOpenAI
API providerMetaOpenAI
Input context1,048,576 tokens1,050,000 tokens
Maximum output—128,000 tokens
Released——
Added to WritingmateAug 5, 2026Apr 24, 2026
LicenseNot availableNot available
Knowledge cutoff—2025-12-01
Capabilities
InputsText, Image, Video, File, AudioFile, Image, Text
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Base input (per 1M tokens)$1.25$5.00
Base output (per 1M tokens)$4.25$30.00
Higher-context pricing tiersBase rate only
  • ≥272,000 prompt tokens$10.00 input · $45.00 output per 1M
Price-comparison caveatAt least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like.
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-5.5 from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Muse Spark 1.2 and GPT-5.5
PlanPriceMuse Spark 1.2GPT-5.5AI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedUltimate requiredNano 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-5.5 FAQ

Which is better, Muse Spark 1.2 or GPT-5.5?

Muse Spark 1.2 outperforms in 0 benchmarks, while GPT-5.5 is better at 2 benchmarks (DeepSWE 1.1, LiveBench). GPT-5.5 leads every shared, protocol-compatible benchmark shown here. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Which model is cheaper to use through an API?

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Which model supports more context?

The published context windows are close enough that this page does not treat the difference as a material advantage.

Can I switch between Muse Spark 1.2 and GPT-5.5?

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.