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Model Comparison

Muse Glimmer 30B vs Kimi K3Which Is Better in 2026?

Meta: Muse Glimmer 30B logoMeta•MoonshotAI: Kimi K3 logoMoonshot AI

Muse Glimmer 30B vs Kimi K3: which should you choose in 2026?

Muse Glimmer 30B (by Meta) and Kimi K3 (by Moonshot AI) 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.

Muse Glimmer 30B is about 9.4× cheaper on a blended 3:1 input/output basis ($0.6375 vs $6.00 per 1M tokens).

Kimi K3 has the larger context window (1,048,576 tokens vs 131,072 tokens).

Choose Muse Glimmer 30B if…

  • • lower blended API cost matters for your workload

Choose Kimi K3 if…

  • • you work with longer documents, transcripts, or codebases

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 Glimmer 30B and Kimi K3 benchmark results
BenchmarkMuse Glimmer 30BKimi K3
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

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52.7%
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

—
69.0%±5.0
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

—
93.5%
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

—
46.9%
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

—
79.2%
LiveBench

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

Muse Glimmer 30B and Kimi K3 model facts
FeatureMuse Glimmer 30BKimi K3
Context & model facts
DeveloperMetaMoonshot AI
API providerMetaMoonshotAI
Input context131,072 tokens1,048,576 tokens
Maximum output—1,048,576 tokens
Released——
Added to WritingmateAug 9, 2026Jul 16, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsText, ImageText, Image, Video
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.35$3.00
Output (per 1M tokens)$1.50$15.00
Blended 3:1 input/output$0.6375$6.00
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 9, 2026.

Why Pay for Multiple Subscriptions?

Comparing Muse Glimmer 30B from Meta with Kimi K3 from Moonshot AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Muse Glimmer 30B and Kimi K3
PlanPriceMuse Glimmer 30BKimi K3AI 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 Glimmer 30B vs Kimi K3 FAQ

Which is better, Muse Glimmer 30B or Kimi K3?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Muse Glimmer 30B is about 9.4× cheaper on a blended 3:1 input/output basis ($0.6375 vs $6.00 per 1M tokens). Kimi K3 has the larger context window (1,048,576 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

Muse Glimmer 30B is about 9.4× cheaper on a blended 3:1 input/output basis ($0.6375 vs $6.00 per 1M tokens).

Which model supports more context?

Kimi K3 has the larger context window (1,048,576 tokens vs 131,072 tokens).

Can I switch between Muse Glimmer 30B and Kimi K3?

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.