Head to head
Any two models from the board, on the same tasks and graders.
Qwen3.8 27B leads by 7 points and wins 6 of 9 tasks.
Task by task
Average score on each task, best build on each side. The higher score is highlighted. Hover a score for the test cases behind it.
TaskAMiMo-V2.6-FlashBQwen3.8 27BLead
Overall
41
48
Tool Calling
66
65
Structured Output
47
48
RAG / Retrieval QA
33
59
Context Recall
100
94
Coding
31
7
Reasoning & Math
11
32
Instruction Following
14
25
Classification
17
38
Summarization
50
62
The race
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.
Every test case
One square per test case, in suite order. A square takes the color of the model that scored higher on it.
Tool Calling
Structured Output
RAG / Retrieval QA
Context Recall
Coding
Reasoning & Math
Instruction Following
Classification
Summarization
Where it breaks
On the impossible tier, MiMo-V2.6-Flash passes 30% of test cases and Qwen3.8 27B passes 29%.
TierAMiMo-V2.6-FlashBQwen3.8 27BLead
Baseline
53
86
Hard
45
57
Impossible
30
29
Thinking spend
Both models spend about the same on thinking per test case.
3.4Ktokens
Per test case, 95% of it thinking
3.4Ktokens
Per test case, 98% of it thinking
TaskAMiMo-V2.6-FlashBQwen3.8 27B
Tool Calling
3.5K85% thinking
4.8K88% thinking
Structured Output
2.6K99% thinking
2.6K99% thinking
RAG / Retrieval QA
2.2K91% thinking
2.5K100% thinking
Context Recall
23195% thinking
50398% thinking
Coding
7.2K95% thinking
6.2K100% thinking
Reasoning & Math
6.3K98% thinking
5.4K100% thinking
Instruction Following
2.9K99% thinking
2.9K100% thinking
Classification
2.9K98% thinking
2.7K100% thinking
Summarization
1.5K98% thinking
1.9K99% thinking
What you'd run
The build behind each score. The better value in each row is highlighted.
SpecAMiMo-V2.6-FlashBQwen3.8 27B
Speed
69 tok/s (better)
45 tok/s
Memory
—
—
Context
1M
—
Cost per run
$0.4321
$0 (better)
Quantization
—
—
Harness
openrouter
openai