Meta · AI model
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.
Pricing
Estimate your monthly cost →| Dimension | Tier | Price | Since | Verified | Source |
|---|---|---|---|---|---|
| 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
| Benchmark | Setting | Score | Reported by | Measured | Verified | Source |
|---|---|---|---|---|---|---|
| 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) |
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.
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.
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.
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.
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
- #9 of 42 in Best AI API as of 2026-10-26
- #12 of 39 in Best AI for business as of 2026-10-26
- #14 of 20 in Best AI for coding as of 2026-10-27
Derived from our published ranking snapshots; each ranking explains its criteria and weights.
Compare Muse Spark 1.2
Guides
- AI token pricing explained: input, output, cached and batch rates
- Reasoning models explained: thinking modes, effort and cost
- Multimodal AI explained: image, audio, video and file inputs, and what they cost
- Context windows explained: limits, long-context pricing and recall
- How to choose an AI model: a practical decision framework
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
- Models - Meta Model API
- Introducing Muse Code and Muse Spark 1.2
- Introducing Muse Spark 1.3
- Reasoning - Meta Model API
- Muse Spark 1.2 | Meta
- Search grounding - Meta Model API
- Pricing and rate limits - Meta Model API
- DeepSWE leaderboard (Datacurve)
- Muse Spark 1.3 model page
- GDPval-AA v2.1 Leaderboard (Artificial Analysis)
- Text Arena leaderboard (Arena)
- OSWorld 2.0 (project page and leaderboard)
Last verified Page updated 2026-10-27