# Muse Spark 1.3

Meta, released 2026-09-02

**Verdict: conditional.** Muse Spark 1.3 is Meta's strongest coding and long-context model yet, and an easy drop-in for anyone already on 1.2: the standard token price is unchanged while software-engineering and million-token retrieval results move sharply up. The deciding caveat is that the headline agent numbers come from a max reasoning configuration Meta has not released, while the shipping tier runs slower and costs more per task. Right for cost-sensitive coding teams inside Muse Code; wrong for anyone who needs independently reproduced benchmarks. Last verified 2026-09-04.

## Pricing

- Input: $1.25 / 1M tokens
- Output: $4.25 / 1M tokens
- Context: 1M ctx; contributor tier $0.10/$0.20

## Strengths

- Million-token retrieval: MRCR v2 98.5 (256K-512K) and 98.1 (512K-1M) on Meta's launch scorecard, up from 66.3 and 55.5 for Muse Spark 1.2
- Codebase-scale software engineering: DeepSWE v1.1 75.4, up from 55.0 for Muse Spark 1.2 per Kingy's tally, and SWE-Atlas Codebase Q&A 59.4 (+13.2pp). Peer figures for GPT-5.6 Sol and Claude Opus 5 come from their owners' leaderboards under different harnesses, so the cross-model ranking is not like-for-like
- Efficiency-sensitive agent runs in Muse Code: Meta reports ~20% fewer tool calls and ~25% fewer tokens than 1.2 on its engineering comparisons
- Hardest agent runs on Meta's own API: Meta's docs now list the max reasoning level for Standard-tier muse-spark-1.3, billed at the same per-token price as the other effort levels
- Teams that can trade data rights for price: the muse-spark-1.3-contributor tier prices at $0.10 input / $0.20 output per 1M in exchange for training rights on prompts and completions
- Multimodal repository context: text, image, video and PDF inputs at a 1,048,576-token context window per Meta's model docs
- Audio workloads: Meta's own docs state audio understanding is not fully supported in 1.3 and requests containing audio may return degraded quality
- Latency-sensitive interactive work: Kingy measured mean task wall time of 99.0 seconds at xhigh versus 36.0 seconds for Muse Spark 1.2
- Budgeting on per-token rates alone: Artificial Analysis measured cost per task rising from $0.40 on 1.2 to $0.55 on 1.3 despite unchanged rates, from heavier input-token use on agentic evaluations
- Treating the launch chart's agent results as free: the strongest agent scores (OSWorld 2.0 66.9, GDPval-AA v2 1,754 Elo) belong to the max reasoning level, which Meta documents as reasoning longer and using more reasoning tokens than xhigh, so cost and wall time per task climb further
- High-concurrency production on the contributor tier: 100 RPM versus 3,000 RPM on standard, and no max reasoning level, per Meta's docs
- Buyers who require independent reproduction of the coding and long-context claims: every headline benchmark is Meta-run; only Artificial Analysis's index and cost-per-task figures are independently measured
- Unattended xhigh runs on a fixed token budget: Kingy's review found xhigh exhausting budgets without returning usable code artifacts, while high reasoning accepted 8 of 9 tasks

## Benchmarks

- Terminal-Bench 2.1: 88.8 (https://kingy.ai/blog/muse-spark-1-3-review-benchmarks-pricing-verdict/)
- DeepSWE v1.1: 75.4 (https://kingy.ai/blog/muse-spark-1-3-review-benchmarks-pricing-verdict/)
- SWE-Atlas Codebase Q&A: 59.4 (https://kingy.ai/blog/muse-spark-1-3-review-benchmarks-pricing-verdict/)
- MRCR v2 (256K-512K): 98.5 (https://kingy.ai/blog/muse-spark-1-3-review-benchmarks-pricing-verdict/)
- MRCR v2 (512K-1M): 98.1 (https://kingy.ai/blog/muse-spark-1-3-review-benchmarks-pricing-verdict/)
- Artificial Analysis Intelligence Index v4.1.1 (xhigh): 61 (https://venturebeat.com/technology/meta-says-muse-spark-1-3-has-frontier-performance-but-its-best-results-come-from-a-model-developers-cant-broadly-use-yet)
- OSWorld 2.0 (shipping xhigh): 57.2 (https://venturebeat.com/technology/meta-says-muse-spark-1-3-has-frontier-performance-but-its-best-results-come-from-a-model-developers-cant-broadly-use-yet)
- JobBench (shipping xhigh): 61.2 (https://venturebeat.com/technology/meta-says-muse-spark-1-3-has-frontier-performance-but-its-best-results-come-from-a-model-developers-cant-broadly-use-yet)
- GDPval-AA v2 (shipping xhigh): 1709 Elo (https://venturebeat.com/technology/meta-says-muse-spark-1-3-has-frontier-performance-but-its-best-results-come-from-a-model-developers-cant-broadly-use-yet)
- Cost per task (Artificial Analysis): $0.55 (https://venturebeat.com/technology/meta-says-muse-spark-1-3-has-frontier-performance-but-its-best-results-come-from-a-model-developers-cant-broadly-use-yet)

## Sources

- [Kingy AI — Muse Spark 1.3 review: benchmarks, pricing, verdict](https://kingy.ai/blog/muse-spark-1-3-review-benchmarks-pricing-verdict/)
- [BenchLM.ai — Muse Spark 1.3 profile](https://benchlm.ai/models/muse-spark-1-3)
- [Meta Model API docs: models (Muse Spark Open listed, 816B MoE with 42B active)](https://dev.meta.ai/docs/models)
- [Meta Model API docs: pricing and rate limits (no Muse Spark Open price listed)](https://dev.meta.ai/docs/pricing-rate-limits)
- [VentureBeat: Meta says Muse Spark 1.3 has frontier performance, but its best results come from a model developers can't broadly use yet](https://venturebeat.com/technology/meta-says-muse-spark-1-3-has-frontier-performance-but-its-best-results-come-from-a-model-developers-cant-broadly-use-yet)
- [Meta AI Research: Introducing Muse Spark 1.3 (official announcement)](https://research.meta.ai/blog/introducing-muse-spark-1-3)
- [Meta Model API: reasoning effort documentation, max level (official)](https://dev.meta.ai/docs/reasoning)

Canonical: https://neomanex.com/models/muse-spark-1-3