Muse Spark 1.3 is a Meta closed-API frontier model. CompareLLM Coding Estimated rating is 1620. Active context window extends to 1M tokens. Commercial API token pricing is listed at $1.25/1M tok input and $4.25/1M tok output per 1M tokens. Meta's multimodal reasoning model for long-running agentic, multi-agent and coding workflows. Numbers below are dated snapshots from empirical benchmark harnesses.
Independent evaluation answering: "Is Muse Spark 1.3 the right model for your workload & budget?"
Listed output price $4.25/1M tokens. Quality is the category rating above, not this price.
Dated snapshot metrics aggregated from official evaluators and API providers with visual relative score bars.
Our category lists (Coding, Reasoning, Chat, Agents). Named suites such as SWE-bench Pro are recipe inputs, not this rating.
LMArena, SWE-bench Pro, LiveBench and similar suites appear with a source and as-of date. They are not CompareLLM's rating.
How much of the recipe is present. Missing inputs stay missing; they are never filled with 0 or 50.
Compact streaming class. Exact tok/s stays in the fact sheet, not in compact cells.
Compact class for time-to-first-token. Exact milliseconds stay in the fact sheet.
Commercial API price per 1 Million prompt/output tokens (~750k words).
| Benchmark Metric & Meaning | Reported Score & Capability Fill | CompareLLM Review |
|---|---|---|
Maximum output (tokens) Maximum Response Length | 944k tokens | Source not recorded · Sep 15, 2026Maximum completion tokens for one request on the model author's own endpoint. |
Cached input token price Cost to Reuse Cached Input | $0.15/1M tok | Source not recorded · Sep 15, 2026Published cache-read price on the model author's own endpoint. |
Input token price Cost to Prompt (Input tokens) | $1.25/1M tok | Source not recorded · Sep 15, 2026Published list input price on the model author's own endpoint. |
Output token price Cost to Generate (Output tokens) | $4.25/1M tok | Source not recorded · Sep 15, 2026Published list output price on the model author's own endpoint. |
Context window (tokens) Context Window Capacity (tokens) | 1M tokens | Source not recorded · Sep 15, 2026Maximum input context on the model author's own endpoint. |
Generation speed (tok/s) Writing Speed (tok/s) | 50–100 tok/s | Source not recorded · Sep 15, 2026 |
Time to first token Waiting Time (before it replies) | 2s+ | Source not recorded · Sep 15, 2026 |
Peers in the same closed API frontier performance tier — not a jump to an unrelated frontier SKU.
Select any rival to launch a side-by-side empirical benchmark comparison with winner deltas.
Compare Muse Spark 1.3 against
Models may use different benchmarks and test settings. This is an indicative composite, not a controlled head-to-head comparison or community Elo. Admin-approved sentiment estimates fill categories without accepted benchmark results. Estimates are labelled and do not increase benchmark coverage. Coverage refers to the configured recipe, not confidence.
Release recent-models-2026-09-23-r1 · recipe reported-text-2026-09-11-r1 · method reported-with-estimates-v2 · research through 2026-09-23
Overall benchmark coverage includes missing applicable categories. Estimated categories contribute to the rating but add no benchmark coverage.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v4.3, on which Muse Spark 1.3 (max) scores 48, and placed within the 48-66 band this catalog's measured results occupy for this category. AA's index is a composite of ten evaluations and is not this category's recipe, so this is an ordering anchor rather than a measurement. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-09-23 · review on 2026-12-23 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Meta (vendor-reported) · reported 2026-09-02
Config: Meta's published Muse Spark 1.3 benchmarks, read through secondary reporting of that table. DeepSWE v1.1 solve rate. · Effort: reported_best
Admin-authored relevance policy, not empirical difficulty calibration. Repository-level work receives the largest share for coding relevance.
Missing benchmark families: terminal-work, frontiercode.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v4.3, on which Muse Spark 1.3 (max) scores 48, and placed mid-range, because this category has too few measured results to define a band. AA's index is a composite of ten evaluations and is not this category's recipe, so this is an ordering anchor rather than a measurement. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-09-23 · review on 2026-12-23 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: graduate-science, advanced-mathematics, broad-academic, interactive-abstraction.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v4.3, on which Muse Spark 1.3 (max) scores 48, and placed mid-range, because this category has too few measured results to define a band. AA's index is a composite of ten evaluations and is not this category's recipe, so this is an ordering anchor rather than a measurement. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-09-23 · review on 2026-12-23 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: constrained-story-writing, instruction-compliance.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v4.3, on which Muse Spark 1.3 (max) scores 48, and placed within the 59-66 band this catalog's measured results occupy for this category. AA's index is a composite of ten evaluations and is not this category's recipe, so this is an ordering anchor rather than a measurement. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-09-23 · review on 2026-12-23 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: professional-computer-tasks, desktop-use, workflow-automation, web-research.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v4.3, on which Muse Spark 1.3 (max) scores 48, and placed mid-range, because this category has too few measured results to define a band. AA's index is a composite of ten evaluations and is not this category's recipe, so this is an ordering anchor rather than a measurement. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-09-23 · review on 2026-12-23 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: conversation-quality, support-task-completion, instruction-compliance.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v4.3, on which Muse Spark 1.3 (max) scores 48, and placed mid-range, because this category has too few measured results to define a band. AA's index is a composite of ten evaluations and is not this category's recipe, so this is an ordering anchor rather than a measurement. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-09-23 · review on 2026-12-23 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: visual-grounding, spatial-reconstruction, visual-symbol-recognition.
What this model is available as, what it takes to run, and where its identity comes from.
Available only through the provider's hosted API.
muse-spark-1.3meta/muse-spark-1.3Concise empirical overview formatted for citations and prompt context
Seventeen models from thirteen vendors reached the market in September, and the top of the Intelligence Index moved from 51 to 53. No vendor publishes a roadmap, so here is what the release record actually supports.
Meta's Muse Spark 1.3 posts the highest DeepSWE v1.1 score in our catalog at 75.4%, with a 943,000-token output ceiling built for long agentic runs. Price, speed and the caveat.
Plain-English methodology and leaderboard answers
Elo is a crowd vote on which hidden answer people liked more — not a school test. What is preference Elo? · Methodology
Follow this model in your watchlist, set it as your global comparison baseline, or assign it to your custom production stack.
Track updates & rank changes
Compare all models against this
Assign to custom architecture
Compare side-by-side vs all
Plotted against all active catalog models (50th percentile = catalog median).
Pre-computed production rankings across developer workloads based on empirical benchmark capability, throughput, and operational economics.