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text model comparison

Muse Spark 1.3 vs Muse Spark Open

Muse Spark 1.3 and Muse Spark Open are both current text models, from Meta. On Arena WebDev, Muse Spark 1.3 scores 1656 Elo and Muse Spark Open 1526 Elo (Arena, measured October 28, 2026). Both accept about 1.05M tokens of context (1,048,576 and 1,000,000).

Higher Arena WebDev
Muse Spark 1.31656 Elo against 1526 Elo
Longer context window
Muse Spark 1.31.05M tokens against 1M tokens
Newer release
Muse Spark OpenOct 2026 against Sep 2026

Data verified November 3, 2026 · Dataset 2026-11-03

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Side by side

Muse Spark 1.3 and Muse Spark Open, line by line

Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.

FactMuse Spark 1.3Muse Spark Open
Price
Price per 1M input tokens$1.25Not listed
Price per 1M output tokens$4.25Not listed
Independent benchmarks
Arena WebDev1656 Elo (#13 of 45) (better)1526 Elo (#35 of 45)
FrontierMath Tiers 1-3 (v2)74.4% (#21 of 42) (better)69.8% (#24 of 42)
Arena Text1495 Elo (#7 of 51)Not measured
GPQA DiamondNot measured88.6% (#34 of 41)
SimpleQA VerifiedNot measured56.6% (#16 of 38)
Specs
ProviderMetaMeta
Release dateSeptember 2, 2026October 15, 2026
AccessAPI onlyopen weights
Context window1,048,576 tokens1,000,000 tokens
Max outputNot reportedNot reported
Inputtext, image, audio, videotext, image, video, audio
Outputtexttext

Scores:Arena · 2026-11-03Epoch AI · 2026-11-03

Open both in the interactive comparison

Charts

Against every current text model

Where Muse Spark 1.3 and Muse Spark Open land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

On Arena WebDev, Muse Spark 1.3 ranks #13 and Muse Spark Open #35 of 45 current models. Each bar is the share of current models it beats: on score, price and context.

Muse Spark 1.3 and Muse Spark Open among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

Muse Spark 1.3:#7of 51

1,495 Elo · ahead of 88%

Muse Spark Open: Not scored

Arena WebDev

Muse Spark 1.3:#13of 45

1,656 Elo · ahead of 70%

Muse Spark Open:#35of 45

1,526 Elo · ahead of 23%

FrontierMath Tiers 1-3 (v2)

Muse Spark 1.3:#21of 42

74.4% · ahead of 51%

Muse Spark Open:#24of 42

69.8% · ahead of 44%

GPQA Diamond

Muse Spark 1.3: Not scored

Muse Spark Open:#34of 41

88.6% · ahead of 18%

SimpleQA Verified

Muse Spark 1.3: Not scored

Muse Spark Open:#16of 38

56.6% · ahead of 59%

Price and context

Price per 1M input tokens

Muse Spark 1.3:#23of 52

$1.25 · cheaper than 53%

Muse Spark Open: Not listed

Price per 1M output tokens

Muse Spark 1.3:#25of 52

$4.25 · cheaper than 53%

Muse Spark Open: Not listed

Context window

Muse Spark 1.3:#11of 64

1.05M tokens · longer than 67%

Muse Spark Open:#26of 64

1M tokens · longer than 22%

Quality against price

Arena Text · measured October 28, 2026

Muse Spark 1.3 scores 1,495 at $2 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.

Quality against priceArena Text score against price for 43 text models, Muse Spark 1.3 and Muse Spark Open marked.1,2501,3001,3501,4001,4501,5001,550$0.30$1$3$10MOST ATTRACTIVE QUADRANTMedian price $3Median 1,475Blended price per 1M tokens (3 input : 1 output), log scaleArena Text (Elo)Muse Spark 1.3
Quality against price: the data
Arena Text score against price for 43 text models, Muse Spark 1.3 and Muse Spark Open marked.
ModelProviderBlended price per 1M tokensArena Text (Elo)
Gemini 4 ArgonGoogle$41,520
Claude Opus 5.5Anthropic$81,509
Muse WonderMeta$81,507
Claude Fable 5Anthropic$201,504
Claude Fable 5.1Anthropic$201,504
Gemini 3.8 FlashGoogle$1.501,496
Muse Spark 1.3Meta$21,495
Claude Opus 5Anthropic$101,492
Kimi K3Moonshot Kimi$61,488
GPT-5.6 SolOpenAI$81,485
Gemini 3.6 FlashGoogle$31,483
GPT-5.5OpenAI$111,482
Claude Opus 4.8Anthropic$101,482
Qwen3.8-MaxAlibaba Qwen$31,482
GPT-6.1 SolOpenAI$41,481
GLM-5.3Z.ai$2.151,481
MiMo-V2.6-ProXiaomi MiMo$0.541,480
Claude Sonnet 5.5Anthropic$41,477
GLM-5.3-FlashZ.ai$0.241,477
GPT-6 AstraOpenAI$201,476
DeepSeek-V4.1-FlashDeepSeek$0.521,476
GPT-5.4OpenAI$5.631,475
Gemini 3 Flash PreviewGoogle$1.131,473
Grok 4.20xAI$1.561,472
GPT-5.6 TerraOpenAI$4.501,467
Grok 4.5xAI$31,465
DeepSeek-V4-ProDeepSeek$1.981,465
Claude Sonnet 5Anthropic$41,462
GPT-6 SolOpenAI$41,458
Gemini 3.5 Flash-LiteGoogle$0.851,456
Grok 4.6xAI$31,455
GPT-5.6 LunaOpenAI$0.451,453
MiMo-V2.6-FlashXiaomi MiMo$0.181,448
Step 5 PreviewStepFun$1.431,448
Grok 4.3xAI$1.561,442
GPT-6 LunaOpenAI$0.201,441
Grok 4.7xAI$31,441
Mistral Large 4Mistral AI$1.051,432
Claude Haiku 5.5Anthropic$0.201,425
GPT-4.1OpenAI$3.501,415
Solar Pro 4Upstage$0.521,388
GPT-4.1 miniOpenAI$0.701,383
GPT-4.1 nanoOpenAI$0.181,322

Cost

What each costs

Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.

Workload or rateMuse Spark 1.3Muse Spark Open
Other listed rates
1M input tokens, cached$0.15Not listed

Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.

Launch price $100, regular price $150

Book the cost audit

Verdict

When to choose each

Muse Spark 1.3

Where it leads

  • Higher Arena WebDev score, 1656 Elo vs 1526 Elo
  • Higher FrontierMath Tiers 1-3 (v2) score, 74.4% vs 69.8%
  • A cached-input rate, $0.15 per 1M tokens

Best for

  • Tool-heavy agent loops where fewer calls per task matters as much as the per-token rate
  • Coding agents at volume, where its coding scores and low price line up

Watch out for

  • Meta’s own documentation states audio understanding is not fully supported and response quality may be degraded on requests containing audio
  • The much cheaper contributor endpoint (about $0.10 / $0.20 per million) is priced that way in exchange for Meta training on your traffic. The standard tier is the rate recorded here.
  • Meta’s scorecard has it trailing Claude Opus 5 on every broader agent-workflow benchmark it published, including GDPVal-AA v2, despite winning the coding rows

Muse Spark Open

Where it leads

  • Open weights you can run yourself

Best for

  • Self-hosted coding and agent work on owned infrastructure
  • Long-context multimodal analysis where data must stay in-house

Watch out for

  • The Muse Model License limits commercial use; it is not an open-source licence
  • Meta does not serve it on its own Model API, so hosted prices come from third parties
  • Self-hosting needs at least 700GB of GPU memory in NVFP4 or 2TB in BF16

From benchmark to production

Building on Muse Spark 1.3 or Muse Spark Open?

Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.

AI Model Cost AuditLaunch price $100, regular price $150What the audit includes

FAQ

Muse Spark 1.3 vs Muse Spark Open FAQ

Do Muse Spark 1.3 and Muse Spark Open discount cached input?

Muse Spark 1.3 lists a cached-input rate of $0.15 per 1M tokens, 88% below its standard input rate. No cached-input rate is listed for Muse Spark Open.

Which scores higher on independent benchmarks, Muse Spark 1.3 or Muse Spark Open?

Muse Spark 1.3 leads on Arena WebDev, 1656 Elo to 1526 Elo; Muse Spark 1.3 leads on FrontierMath Tiers 1-3 (v2), 74.4% to 69.8%. Scores are independent evaluations, not either vendor's launch figures.

Which has the larger context window, Muse Spark 1.3 or Muse Spark Open?

Both accept about 1.05M tokens of context (1,048,576 and 1,000,000).

Can I run Muse Spark 1.3 or Muse Spark Open myself?

Muse Spark Open is released as open weights you can run yourself; Muse Spark 1.3 is available through its provider's API only.

Production evaluation

Choosing between Muse Spark 1.3 and Muse Spark Open?

Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • A head-to-head evaluation on your real workload
  • Routing between models by cost and difficulty