Mistral Large 4 vs Muse Spark 1.3 Contributor

Overall
28
vs
Overall
66

Muse Spark 1.3 Contributor is clearly better. It wins 73 of the 81 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 1.8× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
281m 56s
6.2K, 72% thinking8.7K, 91% thinking
661m 42s
Agents
1126.1s
795, no thinking14.7K, 88% thinking
573m 16s
Structured Extraction
153.78s
288, no thinking4.2K, 91% thinking
9037.9s
Coding
332m 48s
9.3K, 37% thinking10.8K, 95% thinking
482m 1s
Grounded Answers
382m 45s
8.1K, 89% thinking6.1K, 91% thinking
811m 1s
Instruction Following
693m 47s
12.9K, 84% thinking8.7K, 91% thinking
841m 18s
Business Logic
242m 40s
8.8K, 77% thinking6K, 95% thinking
761m 7s
Security & permissions
331m 9s
3.7K, 83% thinking8.2K, 87% thinking
761m 59s
Diagnosis & investigation
01m 19s
4.4K, 85% thinking17.2K, 90% thinking
173m 34s

What you'd run

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

SpecAB
Speed
56 tok/s
107 tok/s (better)
Machine
Hosted
Hosted
Memory at 32K
—
—
Context
512K
1M (better)
Cost per run
$2.67
$0.41 (better)
Cost per case
$0.017
$0.0027 (better)
Time per case
1m 56s
1m 42s (better)
Quantization
—
—
Harness
openrouter
openrouter

The race

No lead changes

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

Mistral Large 4Muse Spark 1.3 Contributor
025507510050100150Perfect run2866

Every test case

153 test cases

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

8 won by Mistral Large 473 won by Muse Spark 1.3 Contributor72 tied