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Muse Spark 1.2

Coding-focused Muse Spark version (announced 5 Aug 2026), listed as the previous version after 1.3. Takes text, image, video, audio and PDF input and returns text, with a 1,048,576-token context. Offered on the Meta Model API (public preview).

Last verified Preview 12 sources

Key specs

Provider
Meta
Family
Muse Spark
API identifier
muse-spark-1.2
Availability
Preview
Release date
2026-08-05
License
Proprietary
Context window
1,048,576 tokens
API access
Yes
Consumer app
Unknown

Capabilities

Inputs

  • Text input Yes
  • Image input Yes
  • Audio input Yes MP3 and WAV; Meta recommends 1.2 for audio understanding
  • Video input Yes MP4 (video understanding docs)
  • File input Yes PDF input

Outputs

  • Text output Yes
  • Image output No Muse Spark is text output only
  • Audio output No Muse Spark is text output only

Features

  • Reasoning Native Muse Spark always reasons; effort 'none' returns HTTP 400. Effort is adjustable, but among Muse Spark versions 'max' is limited to Standard-tier muse-spark-1.3
  • Tool calling Yes
  • Web search Yes Documented for Muse Spark text models in general (examples use muse-spark-1.3). Not available on Chat Completions

"Unknown" means we have not verified this capability yet; it does not mean the model lacks it.

DimensionTierPriceSinceVerifiedSource
Input tokens base $1.25 per 1M tokens 2026-10-12 2026-10-25 Pricing and rate limits - Meta Model API Standard tier; no long-context premium. Contributor variant muse-spark-1.2-contributor: $0.10 input / $0.002 cached input / $0.20 output per 1M tokens, in exchange for permission to train on prompts and completions
Cached input tokens base $0.15 per 1M tokens 2026-10-12 2026-10-25 Pricing and rate limits - Meta Model API Standard tier; no cache-write price is listed
Output tokens base $4.25 per 1M tokens 2026-10-12 2026-10-25 Pricing and rate limits - Meta Model API Standard tier; reasoning tokens are billed at the output rate
Tool and search requests base $2.50 per 1,000 requests 2026-10-12 2026-10-25 Pricing and rate limits - Meta Model API Web search grounding (web_search tool): per 1,000 search queries, on top of token costs

Prices are shown in the currency the provider publishes; we never convert currencies.

Benchmarks

BenchmarkSettingScoreReported byMeasuredVerifiedSource
AutomationBench % tasks completed correctly, higher is better public v3 task set (600 workflows), pass@1; xhigh effort; benchmark's automation tools 38.2 % Self-reported — 2026-10-25 Muse Spark 1.3 model page
DeepSWE v1.1 % resolved (pass@1), higher is better v1.1 (113 tasks), pass@1 averaged over 5 attempts; xhigh effort; Muse Code harness (Meta internal framework, Daytona sandboxes) 59.3 % Self-reported — 2026-10-25 Introducing Muse Code and Muse Spark 1.2
v1.1 (113 tasks), pass@1; xhigh effort; mini-swe-agent (official leaderboard harness) 55 % Third party — 2026-10-25 DeepSWE leaderboard (Datacurve)
GDPval-AA v2.1 Elo, higher is better v2.1; xhigh effort; Artificial Analysis Stirrup harness 1,475 Elo Third party 2026-10-25 2026-10-25 GDPval-AA v2.1 Leaderboard (Artificial Analysis)
OSWorld 2.0 % tasks completed, higher is better v2.1, full set, binary; batch tool approach, 500-step budget; xhigh effort 15.84 % Third party 2026-10-15 2026-10-25 OSWorld 2.0 (project page and leaderboard)
v2.1, full set, partial; batch tool approach, 500-step budget; xhigh effort 44.08 % Third party 2026-10-15 2026-10-25 OSWorld 2.0 (project page and leaderboard)
version 06.24, full 108-task set, binary; xhigh effort; GUI computer-control tool in Meta's internal evaluation framework 17.9 % Self-reported — 2026-10-25 Muse Spark 1.3 model page
version 06.24, full 108-task set, partial; xhigh effort; GUI computer-control tool in Meta's internal evaluation framework 47.6 % Self-reported — 2026-10-25 Muse Spark 1.3 model page
Text Arena Arena score, higher is better Overall, style control on (default); xhigh effort 1,491 Third party 2026-10-23 2026-10-25 Text Arena leaderboard (Arena)
AutomationBench caveats:

Leaderboard versions (for example 1.0.5 and 1.0.6) are not comparable. Some results use fallback routing for refused steps: for "Claude Fable 5.1 (with Opus 5 Fallback)", Opus 5 handled 260 of 657 tasks. Artificial Analysis runs a separate leaderboard, AutomationBench-AA, which uses a guardrail-adjusted metric; its scores are not comparable with Zapier's. The partial_credit metric is for diagnostics only. The leaderboard shows no per-row evaluation dates.

DeepSWE v1.1 caveats:

The official leaderboard uses mini-swe-agent for all models and shows whole-number percentages with margins of about ±1 to ±6 points. Providers often report scores from their own scaffolds or effort settings; DeepSeek reports 65.5% to 74.2% for the same model depending on the scaffold. DeepSWE v1 scores are not comparable with v1.1. With only 113 tasks, small differences are within noise.

GDPval-AA v2.1 caveats:

Elo is a relative rating: values change as models are added and the scale is re-anchored, so always store the date a rating was read. For example, Anthropic's launch page gives 1846 for Claude Opus 5.5, while the leaderboard on 2026-10-12 shows 1866 (Max, Default Fallback). Earlier versions (Google cites "GDPVal-AA v2") are not comparable. Judging is done by an LLM, not by human experts. Anthropic models are listed with "Default Fallback", meaning refused steps are routed to a fallback model. AA-Briefcase and GDP.pdf are separate evaluations.

OSWorld 2.0 caveats:

Numbers are only comparable when release, task set (full or offline), metric (binary or partial), step budget (150, 300 or 500) and agent approach (standard or batch tool calls) all match. Providers usually quote partial scores, which are much higher than binary completion. Claude Opus 5 at max effort on v2.1 scores 44.33% binary and 77.67% partial. The verified leaderboard is run by the maintainers and covers few current models, so most numbers are self-reported. The original OSWorld and OSWorld-Verified (369 tasks) are a different benchmark and are not recorded here.

Text Arena caveats:

Scores measure voter preference, which can be influenced by answer style and length. The leaderboard offers a style-control adjustment, so record which view was used. Scores shift with every update, so a value is only meaningful together with its leaderboard date. Entries marked "Preliminary" or with few votes have wide confidence intervals. The same model can appear several times with different reasoning-effort suffixes (for example -high or -max). Category views (Coding, Math, Creative Writing and others) are different rankings from Overall.

One row per result, without rank numbers: each result was measured under its own setting (effort level, harness, tools, test version), as stated by the source, and scores compare only within the same setting. Self-reported results come from the provider and have not been reproduced independently.

Rankings

Derived from our published ranking snapshots; each ranking explains its criteria and weights.

Compare Muse Spark 1.2

Guides

About Muse Spark 1.2

Muse Spark 1.2 was announced on 5 August 2026 together with Muse Code, Meta's terminal coding agent, and was released in Muse Code and the Meta Model API. Meta describes it as a coding-focused update to Muse Spark 1.1, optimized for real coding workflows with more reliable tool calling. The Meta Model API lists it as the previous version, after Muse Spark 1.3. Its context window is 1,048,576 tokens, and a lower-priced Contributor variant (muse-spark-1.2-contributor) is available.

Strengths and weaknesses

Strengths

  • 1,048,576-token context window, with no long-context premium
  • Text, image, video, audio and PDF input; Meta recommends 1.2 over 1.3 for audio understanding
  • Meta positions it for coding workflows, with higher first-attempt accuracy and more reliable tool calling
  • Lower-priced Contributor variant available

Weaknesses

  • The 'max' reasoning effort level is not available (among Muse Spark versions, Standard-tier muse-spark-1.3 only)
  • Listed as the previous version, after Muse Spark 1.3
  • Reasoning cannot be turned off

Sources

  1. Models - Meta Model API
  2. Introducing Muse Code and Muse Spark 1.2
  3. Introducing Muse Spark 1.3
  4. Reasoning - Meta Model API
  5. Muse Spark 1.2 | Meta
  6. Search grounding - Meta Model API
  7. Pricing and rate limits - Meta Model API
  8. DeepSWE leaderboard (Datacurve)
  9. Muse Spark 1.3 model page
  10. GDPval-AA v2.1 Leaderboard (Artificial Analysis)
  11. Text Arena leaderboard (Arena)
  12. OSWorld 2.0 (project page and leaderboard)

Last verified Page updated 2026-10-27