Head to head

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

Wins 8 tasks, 67 test cases

vs
Overall
32

Wins 1 task, 18 test cases

DeepSeek V4 Flash 0731 leads by 21 points and wins 8 of 9 tasks.

Task by task

DeepSeek V4 Flash 0731 wins 8 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
32
Tool Calling
85
60
Structured Output
37
30
RAG / Retrieval QA
59
19
Context Recall
94
50
Coding
52
54
Reasoning & Math
36
11
Instruction Following
18
4
Classification
45
17
Summarization
52
41

The race

2 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 0731Laguna S 2.1
025507510050100150200Perfect run5131

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.

67 won by DeepSeek V4 Flash 073118 won by Laguna S 2.1153 tied

Where it breaks

238 shared test cases

On the impossible tier, DeepSeek V4 Flash 0731 passes 38% of test cases and Laguna S 2.1 passes 19%.

TierAB
Baseline
36 test cases
78%
53%
Hard
69 test cases
61%
29%
Impossible
133 test cases
38%
19%

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 1.4× as many thinking tokens per test case.

DeepSeek V4 Flash 0731
3.5Ktokens
Per test case, 76% of it thinking
Laguna S 2.1
2.5Ktokens
Per test case, 76% of it thinking
TaskAB
Tool Calling
4.1K52% thinking
4.2K74% thinking
Structured Output
2.8K78% thinking
1.4K97% thinking
RAG / Retrieval QA
2.9K85% thinking
1.5K79% thinking
Context Recall
75787% thinking
66683% thinking
Coding
6.1K86% thinking
2.9K42% thinking
Reasoning & Math
5.9K73% thinking
5.6K87% thinking
Instruction Following
3.4K71% thinking
1.8K34% thinking
Classification
2.7K81% thinking
2.8K100% thinking
Summarization
1.8K81% thinking
51393% 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)
48 tok/s
Memory
—
—
Context
1M (better)
262.1K
Cost per run
$0.2742
$0 (better)
Quantization
—
—
Harness
openrouter
openrouter

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