Ling 3.1 Flash vs Mellum2.1 Thinking

Overall
45
vs
Overall
9

Ling 3.1 Flash is clearly better. It wins 67 of the 71 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. Both models spend about the same on thinking per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
451m 16s
7.9K, 73% thinking10.3K, 63% thinking
91m 56s
Agents
4155.9s
4.5K, 66% thinking10.8K, 93% thinking
92m 3s
Structured Extraction
711m 11s
8.1K, 69% thinking8.3K, 51% thinking
51m 36s
Coding
241m 42s
11.3K, 89% thinking11.3K, 38% thinking
52m 11s
Grounded Answers
5258.9s
6.7K, 74% thinking7.3K, 62% thinking
131m 24s
Instruction Following
761m 55s
11.4K, 77% thinking17.3K, 63% thinking
283m 10s
Business Logic
381m 22s
9.7K, 56% thinking9K, 50% thinking
01m 39s
Security & permissions
5456.1s
5.2K, 74% thinking9.1K, 75% thinking
81m 40s
Diagnosis & investigation
042.1s
3.2K, 72% thinking6.8K, 98% thinking
01m 15s

What you'd run

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

SpecAB
Speed
112 tok/s
90 tok/s
Machine
Hosted
Apple M5 Pro, 48 GB
Memory at 32K
—
12.9 GB
Context
256K (better)
128K
Cost per run
Not measured
$2.46 est.
Cost per case
Not measured
$0.016 est.
Time per case
1m 16s (better)
1m 56s
Quantization
—
Q8_0
Harness
openrouter
llama.cpp

The race

No lead changes

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

Ling 3.1 FlashMellum2.1 Thinking
025507510050100150Perfect run459

Every test case

153 test cases

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

67 won by Ling 3.1 Flash4 won by Mellum2.1 Thinking82 tied