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Model Comparison

Muse Spark 1.2 vs Qwen3 32BWhich Is Better in 2026?

Meta: Muse Spark 1.2 logoMeta•Qwen: Qwen3 32B logoQwen

Muse Spark 1.2 vs Qwen3 32B: which should you choose in 2026?

Muse Spark 1.2 (by Meta) and Qwen3 32B (by Qwen) 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.

Qwen3 32B is about 15.4× cheaper on a blended 3:1 input/output basis ($0.13 vs $2.00 per 1M tokens).

Muse Spark 1.2 has the larger context window (1,048,576 tokens vs 131,072 tokens).

Choose Muse Spark 1.2 if…

  • • you work with longer documents, transcripts, or codebases
  • • you need image inputs

Choose Qwen3 32B if…

  • • lower blended API cost matters for your workload

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 Qwen3 32B benchmark results
BenchmarkMuse Spark 1.2Qwen3 32B
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 Qwen3 32B model facts
FeatureMuse Spark 1.2Qwen3 32B
Context & model facts
DeveloperMetaQwen
API providerMetaQwen
Input context1,048,576 tokens131,072 tokens
Maximum output—16,384 tokens
Released——
Added to WritingmateAug 5, 2026Apr 28, 2025
LicenseNot availableNot available
Knowledge cutoff—2025-03-31
Capabilities
InputsText, Image, Video, File, AudioText
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$1.25$0.08
Output (per 1M tokens)$4.25$0.28
Blended 3:1 input/output$2.00$0.13
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 Qwen3 32B from Qwen? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Muse Spark 1.2 and Qwen3 32B
PlanPriceMuse Spark 1.2Qwen3 32BAI 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 Qwen3 32B FAQ

Which is better, Muse Spark 1.2 or Qwen3 32B?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Qwen3 32B is about 15.4× cheaper on a blended 3:1 input/output basis ($0.13 vs $2.00 per 1M tokens). Muse Spark 1.2 has the larger context window (1,048,576 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

Qwen3 32B is about 15.4× cheaper on a blended 3:1 input/output basis ($0.13 vs $2.00 per 1M tokens).

Which model supports more context?

Muse Spark 1.2 has the larger context window (1,048,576 tokens vs 131,072 tokens).

Can I switch between Muse Spark 1.2 and Qwen3 32B?

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.