Muse Spark 1.3 Contributor vs Qwen3.8 27B

Overall
66
vs
Overall
48

Muse Spark 1.3 Contributor is clearly better. It wins 59 of the 68 test cases where they differ.

Learn to build evals like this in the AI Engineering Academy

Task by task

A score turns orange only when that model wins clearly more of the task's test cases. Under each score, the average time per test case on the machine it ran on. Muse Spark 1.3 Contributor spends 1.5× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
661m 42s
8.7K, 91% thinking5.9K, 91% thinking
4833.3s
Agents
573m 16s
14.7K, 88% thinking5.2K, 78% thinking
5736.6s
Structured Extraction
9037.9s
4.2K, 91% thinking4.6K, 92% thinking
7320.6s
Coding
482m 1s
10.8K, 95% thinking9.3K, 94% thinking
3753.0s
Grounded Answers
811m 1s
6.1K, 91% thinking4.2K, 91% thinking
6131.6s
Instruction Following
841m 18s
8.7K, 91% thinking6.9K, 93% thinking
6733.6s
Business Logic
761m 7s
6K, 95% thinking7.6K, 98% thinking
3632.9s
Security & permissions
761m 59s
8.2K, 87% thinking3.7K, 85% thinking
5122.6s
Diagnosis & investigation
173m 34s
17.2K, 90% thinking5.9K, 85% thinking
040.0s

What you'd run

The build behind each score. Orange marks the faster, smaller, cheaper or longer-context side.

SpecAB
Speed
107 tok/s
196 tok/s (better)
Machine
Hosted
Hosted
Memory at 32K
—
—
Context
1M (better)
256K
Cost per run
$0.41 (better)
$1.28
Cost per case
$0.0027 (better)
$0.0083
Time per case
1m 42s
33.3s (better)
Quantization
—
FP8
Harness
openrouter
openrouter

The race

3 lead changes

Each model's running score as the test cases go by. The dashed line is a perfect run.

Muse Spark 1.3 ContributorQwen3.8 27B
025507510050100150Perfect run6648

Every test case

153 test cases

Each square is one test case, colored by the model that scored higher.

59 won by Muse Spark 1.3 Contributor9 won by Qwen3.8 27B85 tied