GLM 5.3 Flash vs Qwen3.8 27B

Overall
35
vs
Overall
48

Qwen3.8 27B is clearly better. It wins 50 of the 81 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. Qwen3.8 27B spends 2.2× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
3555.5s
2.8K, 89% thinking5.9K, 91% thinking
4833.3s
Agents
1037.0s
607, 81% thinking5.2K, 78% thinking
5736.6s
Structured Extraction
4847.2s
2.7K, 93% thinking4.6K, 92% thinking
7320.6s
Coding
541m 52s
5.3K, 83% thinking9.3K, 94% thinking
3753.0s
Grounded Answers
3756.8s
2.9K, 93% thinking4.2K, 91% thinking
6131.6s
Instruction Following
4340.8s
2K, 77% thinking6.9K, 93% thinking
6733.6s
Business Logic
331m 18s
5.8K, 98% thinking7.6K, 98% thinking
3632.9s
Security & permissions
5525.2s
963, 81% thinking3.7K, 85% thinking
5122.6s
Diagnosis & investigation
027.0s
563, 63% thinking5.9K, 85% thinking
040.0s

What you'd run

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

SpecAB
Speed
60 tok/s
196 tok/s (better)
Machine
Hosted
Hosted
Memory at 32K
—
—
Context
1M (better)
256K
Cost per run
$0.19 (better)
$1.28
Cost per case
$0.0013 (better)
$0.0083
Time per case
55.5s
33.3s (better)
Quantization
FP8
FP8
Harness
openrouter
openrouter

The race

No lead changes

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

GLM 5.3 FlashQwen3.8 27B
025507510050100150Perfect run3548

Every test case

153 test cases

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

31 won by GLM 5.3 Flash50 won by Qwen3.8 27B72 tied