Head to head

Any two models from the board, on the same tasks and graders.
Side A
Profile
Side B
Profile
Overall
39

Wins 1 task, 10 test cases

vs
Overall
54

Wins 6 tasks, 53 test cases

Qwen3.8 Flash leads by 15 points and wins 6 of 9 tasks.

Task by task

Qwen3.8 Flash wins 6 of 9

Average score on each task, best build on each side. The higher score is highlighted. Hover a score for the test cases behind it.

TaskAB
Overall
39
54
Tool Calling
67
79
Structured Output
37
47
RAG / Retrieval QA
30
41
Context Recall
89
89
Coding
11
75
Reasoning & Math
32
32
Instruction Following
7
25
Classification
45
38
Summarization
34
60

The race

No lead changes

Score piled up over the 238 test cases both models ran, as a share of the suite's maximum. The dashed line is a perfect run.

Ling-3.0 flashQwen3.8 Flash
025507510050100150200Perfect run3752

Every test case

238 test cases

One square per test case, in suite order. A square takes the color of the model that scored higher on it.

10 won by Ling-3.0 flash53 won by Qwen3.8 Flash175 tied

Where it breaks

238 shared test cases

On the impossible tier, Ling-3.0 flash passes 20% of test cases and Qwen3.8 Flash passes 35%.

TierAB
Baseline
36 test cases
75%
89%
Hard
69 test cases
46%
57%
Impossible
133 test cases
20%
35%

Tiers come from the suite. Baseline is fair, hard is adversarial, and impossible is built so that nothing solves it. Each number is the share of test cases the model fully passed.

Thinking spend

Both models spend about the same on thinking per test case.

Ling-3.0 flash
4.1Ktokens
Per test case, 55% of it thinking
Qwen3.8 Flash
3Ktokens
Per test case, 70% of it thinking
TaskAB
Tool Calling
6.7K40% thinking
6.7K66% thinking
Structured Output
3K46% thinking
1.9K67% thinking
RAG / Retrieval QA
3.3K54% thinking
2.4K66% thinking
Context Recall
87459% thinking
55595% thinking
Coding
8K79% thinking
5.2K71% thinking
Reasoning & Math
6.2K52% thinking
4.3K73% thinking
Instruction Following
3.2K54% thinking
2.3K69% thinking
Classification
2.8K50% thinking
1.8K72% thinking
Summarization
2K49% thinking
1.4K70% thinking

Average output tokens per test case, and how much of it was thinking.

What you'd run

The build behind each score. The better value in each row is highlighted.

SpecAB
Speed
264 tok/s (better)
64 tok/s
Memory
—
—
Context
262.1K
1M (better)
Cost per run
$0 (better)
$0.5705
Quantization
—
—
Harness
openrouter
openrouter

Faster, smaller, cheaper and more context count as better. Quantization and harness only describe the builds.