Muse Spark 1.3 vs Qwen3.8-27B
Muse Spark 1.3 leads the LLM Stats Score 54.3 to 45.1. Qwen3.8-27B is 1.9x cheaper per token.
Meta · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Muse Spark 1.3 leads the overall LLM Stats Score 54.3 to 45.1, ranking #6 overall.
In the 3 individual benchmarks reported for both models, Muse Spark 1.3 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8-27B is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Spark 1.3 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Muse Spark 1.3
- overall performance matters — it scores 54.3 and ranks #6 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3.8-27B
- cost matters — it's about 1.9x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for Muse Spark 1.3 · 26 for Qwen3.8-27B
Muse Spark 1.3 outperforms in 3 benchmarks (DeepSWE 1.1, Job Bench, Terminal-Bench 2.1), while Qwen3.8-27B is better at 0 benchmarks.
Muse Spark 1.3 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Muse Spark 1.3 ($1.25/1M tokens) is 3.1x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, Muse Spark 1.3 ($4.25/1M tokens) is 1.4x more expensive than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Muse Spark 1.3 is more expensive than Qwen3.8-27B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Spark 1.3 accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while Qwen3.8-27B is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Both Muse Spark 1.3 and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Muse Spark 1.3
Qwen3.8-27B
License
Usage and distribution terms
Muse Spark 1.3 is licensed under a proprietary license, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
Muse Spark 1.3 was released on 2026-09-02, while Qwen3.8-27B was released on 2026-08-14.
Muse Spark 1.3 is 1 month newer than Qwen3.8-27B.
Sep 2, 2026
2 weeks ago
2w newerAug 14, 2026
1 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Muse Spark 1.3 is available from Meta Model API. Qwen3.8-27B is available from DeepInfra, FriendliAI.
Muse Spark 1.3
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against Muse Spark 1.3 and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Muse Spark 1.3 vs Qwen3.8-27B.