Muse Spark 1.3

meta/muse-spark-1.3-20260902

Meta: Muse Spark 1.3 is a multimodal model from Meta that accepts text, image, video, file, and audio inputs and generates text outputs. It has a context length of 1,048,576 tokens, supports tool use and reasoning, and is not open weights.

  • tool use
  • structured outputs
  • reasoning

At a glance

providermeta
releasedSep 2, 2026
context1.0M
input / M$1.25

Specifications

What the API gives you

Context window
1.0M tokens
Max output
944K tokens
Input price
$1.25 / 1M tokens
Output price
$4.25 / 1M tokens
Cache read
$0.15 / 1M tokens
Modalities
text, image, video, file → text
Knowledge cutoff
—
Released
Sep 2, 2026

Measured evidence

Public benchmark record

Design Arena Coding
1352 Elo2026-10-20
Design Arena UI
1347 Elo2026-10-20
Design Arena Web-app Agent
1295 Elo2026-10-20
Design Arena Website
1346 Elo2026-10-20
FrontierMath Tiers 1-3 (Epoch AI)‡
74% ± 2.62026-09-18max effort, evaluated 2026-09-18
FrontierMath Tiers 1-3 (Epoch AI)‡
74.4% ± 2.62026-09-16xhigh effort, evaluated 2026-09-16
LiveBench Coding
81.1%2026-10-18
LiveBench Language
82.8%2026-10-18
LiveBench Math
96.0%2026-10-18
LiveBench Reasoning
89.7%2026-10-18
LMArena Agent‡
3.32026-10-19max effort
LMArena Agent Bash Recovery‡
6.12026-10-08max effort
LMArena Agent Steerability‡
52026-10-19max effort
LMArena Agent Task Outcome‡
5.22026-10-19max effort
LMArena Agent Tool Hallucination‡
0.62026-10-19max effort
LMArena Creative Writing‡
1460 Elo2026-10-15max effort
LMArena Instruction Following‡
1478 Elo2026-10-15max effort
LMArena Long Query‡
1488 Elo2026-10-15max effort
LMArena Math‡
1496 Elo2026-10-15max effort
LMArena Text‡
1494 Elo2026-10-15max effort
LMArena Text Factuality‡
1479 Elo2026-10-15max effort
LMArena Vision‡
1290 Elo2026-10-19max effort
LMArena Vision Diagrams‡
1322 Elo2026-10-19max effort
LMArena Vision OCR‡
1307 Elo2026-10-19max effort
LMArena WebDev‡
1659 Elo2026-10-19max effort
LMArena WebDev Image-to-Web‡
1640 Elo2026-10-19max effort
OTIS Mock AIME (Epoch AI)‡
99.2% ± 0.62026-09-17xhigh effort, evaluated 2026-09-17
Terminal-Bench (4.0)‡
14.6%2026-10-20Muse Code, xhigh effort, evaluated 2026-10-06

LMArena data licensed CC-BY-4.0. Aider (Apache-2.0) and SWE-bench (MIT) are open. Design Arena uses blind human preference. ‡ Submitted scaffold plus model result. Compare the harness and effort settings, not only the model name.

Task evidence

How broad is the support?

Each status is exact to this model version and the selected task. Official release claims remain provisional.

  • Best LLM for Competition MathEstablished evidence

    100% of intended weighted task evidence across 4 independent sources. 6 of 6 configured evaluation feeds currently have results.

  • Best LLM for Business WritingEstablished evidence

    100% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for Marketing CopyEstablished evidence

    100% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for Chat & RoleplayEstablished evidence

    100% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for Creative WritingEstablished evidence

    100% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for Frontend & UIEstablished evidence

    100% of intended weighted task evidence across 3 independent sources. 6 of 6 configured evaluation feeds currently have results.

Show all 45 tasks
  • Best LLM for WritingEstablished evidence

    100% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • 100% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • 91% of intended weighted task evidence across 2 independent sources. 6 of 6 configured evaluation feeds currently have results.

  • Best LLM for Math & ReasoningEstablished evidence

    88% of intended weighted task evidence across 4 independent sources. 7 of 7 configured evaluation feeds currently have results.

  • 82% of intended weighted task evidence across 2 independent sources. 5 of 5 configured evaluation feeds currently have results.

  • Best LLM for Agentic CodingEstablished evidence

    80% of intended weighted task evidence across 4 independent sources. 7 of 7 configured evaluation feeds currently have results.

  • Best LLM for Code CompletionEstablished evidence

    79% of intended weighted task evidence across 2 independent sources. 3 of 3 configured evaluation feeds currently have results.

  • Best LLM for Autonomous AgentsEstablished evidence

    67% of intended weighted task evidence across 3 independent sources. 6 of 6 configured evaluation feeds currently have results.

  • Best LLM for CodingEstablished evidence

    64% of intended weighted task evidence across 4 independent sources. 7 of 7 configured evaluation feeds currently have results.

  • 64% of intended weighted task evidence across 2 independent sources. 2 of 3 configured evaluation feeds currently have results.

  • 61% of intended weighted task evidence across 2 independent sources. 2 of 3 configured evaluation feeds currently have results.

  • 60% of intended weighted task evidence across 2 independent sources. 5 of 6 configured evaluation feeds currently have results.

  • Best LLM for SummarizationEstablished evidence

    60% of intended weighted task evidence across 2 independent sources. 5 of 6 configured evaluation feeds currently have results.

  • Best LLM for Fast SummariesEstablished evidence

    58% of intended weighted task evidence across 2 independent sources. 2 of 3 configured evaluation feeds currently have results.

  • Best Value LLMEstablished evidence

    58% of intended weighted task evidence across 2 independent sources. 2 of 3 configured evaluation feeds currently have results.

  • Best LLM for AI AgentsEstablished evidence

    50% of intended weighted task evidence across 3 independent sources. 9 of 9 configured evaluation feeds currently have results.

  • 50% of intended weighted task evidence across 2 independent sources. 3 of 4 configured evaluation feeds currently have results.

  • 50% of intended weighted task evidence across 2 independent sources. 3 of 4 configured evaluation feeds currently have results.

  • Best LLM for TranslationEstablished evidence

    50% of intended weighted task evidence across 2 independent sources. 3 of 4 configured evaluation feeds currently have results.

  • Best LLM for Hermes AgentEstablished evidence

    48% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for OpenHandsEstablished evidence

    48% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for GooseEstablished evidence

    48% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best LLM for OpenClawEstablished evidence

    48% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best Local LLM for CodingEstablished evidence

    47% of intended weighted task evidence across 2 independent sources. 4 of 4 configured evaluation feeds currently have results.

  • Best Cheap LLM for High VolumeEstablished evidence

    40% of intended weighted task evidence across 2 independent sources. 2 of 3 configured evaluation feeds currently have results.

  • Cheapest Capable LLMEstablished evidence

    40% of intended weighted task evidence across 2 independent sources. 2 of 3 configured evaluation feeds currently have results.

  • 35% of intended weighted task evidence across 2 independent sources. 3 of 4 configured evaluation feeds currently have results.

  • 35% of intended weighted task evidence across 2 independent sources. 3 of 4 configured evaluation feeds currently have results.

  • 33% of intended weighted task evidence across 2 independent sources. 5 of 5 configured evaluation feeds currently have results.

  • Best LLM for OCR & DocumentsProvisional evidence

    65% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Structured-output eval or VLMEvalKit tasks. 3 of 5 configured evaluation feeds currently have results.

  • Best LLM for Charts & DiagramsProvisional evidence

    65% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Structured-output eval or VLMEvalKit tasks. 3 of 5 configured evaluation feeds currently have results.

  • 60% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Structured-output eval or VLMEvalKit tasks. 4 of 6 configured evaluation feeds currently have results.

  • Best LLM for Document ParsingProvisional evidence

    58% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Structured-output eval or VLMEvalKit tasks. 3 of 5 configured evaluation feeds currently have results.

  • Best LLM for Massive ContextProvisional evidence

    47% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from LongBench v2. 2 of 3 configured evaluation feeds currently have results.

  • 39% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from FACTS Grounding or LMArena Search. 5 of 6 configured evaluation feeds currently have results.

  • 38% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Berkeley Function Calling or LMArena Search. 6 of 7 configured evaluation feeds currently have results.

  • 21% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Route reliability or Structured-output eval. 1 of 3 configured evaluation feeds currently have results.

  • 17% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from Berkeley Function Calling or OSWorld Verified. 3 of 3 configured evaluation feeds currently have results.

  • Best LLM for Grounded RAGProvisional evidence

    14% of intended weighted task evidence across 1 independent source; broader independent confirmation is still missing from CRAG or SimpleQA Verified (Epoch AI). 5 of 7 configured evaluation feeds currently have results.

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