Mistral Large 4 vs Qwen3.8 27B

Overall
28
vs
Overall
48

Qwen3.8 27B is clearly better. It wins 55 of the 76 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. Qwen3.8 27B spends 1.2× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
281m 56s
6.2K, 72% thinking5.9K, 91% thinking
4833.3s
Agents
1126.1s
795, no thinking5.2K, 78% thinking
5736.6s
Structured Extraction
153.78s
288, no thinking4.6K, 92% thinking
7320.6s
Coding
332m 48s
9.3K, 37% thinking9.3K, 94% thinking
3753.0s
Grounded Answers
382m 45s
8.1K, 89% thinking4.2K, 91% thinking
6131.6s
Instruction Following
693m 47s
12.9K, 84% thinking6.9K, 93% thinking
6733.6s
Business Logic
242m 40s
8.8K, 77% thinking7.6K, 98% thinking
3632.9s
Security & permissions
331m 9s
3.7K, 83% thinking3.7K, 85% thinking
5122.6s
Diagnosis & investigation
01m 19s
4.4K, 85% 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
56 tok/s
196 tok/s (better)
Machine
Hosted
Hosted
Memory at 32K
—
—
Context
512K (better)
256K
Cost per run
$2.67
$1.28 (better)
Cost per case
$0.017
$0.0083 (better)
Time per case
1m 56s
33.3s (better)
Quantization
—
FP8
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 4Qwen3.8 27B
025507510050100150Perfect run2848

Every test case

153 test cases

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

21 won by Mistral Large 455 won by Qwen3.8 27B77 tied