Head to head

Any two models from the board, on the same tasks and graders.
Side A
Profile
Side B
Profile
Overall
53

Wins 5 tasks, 25 test cases

vs
Overall
54

Wins 4 tasks, 24 test cases

Qwen3.8 Flash leads by 1 point, though DeepSeek V4 Flash 0731 wins 5 of 9 tasks.

Task by task

DeepSeek V4 Flash 0731 wins 5 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
53
54
Tool Calling
85
79
Structured Output
37
47
RAG / Retrieval QA
59
41
Context Recall
94
89
Coding
52
75
Reasoning & Math
36
32
Instruction Following
18
25
Classification
45
38
Summarization
52
60

The race

3 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.

DeepSeek V4 Flash 0731Qwen3.8 Flash
025507510050100150200Perfect run5152

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.

25 won by DeepSeek V4 Flash 073124 won by Qwen3.8 Flash189 tied

Where it breaks

238 shared test cases

On the impossible tier, DeepSeek V4 Flash 0731 passes 38% of test cases and Qwen3.8 Flash passes 35%.

TierAB
Baseline
36 test cases
78%
89%
Hard
69 test cases
61%
57%
Impossible
133 test cases
38%
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

DeepSeek V4 Flash 0731 spends 1.2× as many thinking tokens per test case.

DeepSeek V4 Flash 0731
3.5Ktokens
Per test case, 76% of it thinking
Qwen3.8 Flash
3Ktokens
Per test case, 70% of it thinking
TaskAB
Tool Calling
4.1K52% thinking
6.7K66% thinking
Structured Output
2.8K78% thinking
1.9K67% thinking
RAG / Retrieval QA
2.9K85% thinking
2.4K66% thinking
Context Recall
75787% thinking
55595% thinking
Coding
6.1K86% thinking
5.2K71% thinking
Reasoning & Math
5.9K73% thinking
4.3K73% thinking
Instruction Following
3.4K71% thinking
2.3K69% thinking
Classification
2.7K81% thinking
1.8K72% thinking
Summarization
1.8K81% 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
65 tok/s (better)
64 tok/s
Memory
—
—
Context
1M
1M
Cost per run
$0.2742 (better)
$0.5705
Quantization
—
—
Harness
openrouter
openrouter

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