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DeepSeek V4.1 Flash (high) vs Qwen3.8 Max (low)

Qwen3.8 Max (low) leads on average score with 8.6 vs 8.5. DeepSeek V4.1 Flash (high) has the lower benchmark cost at $0.287 vs $0.702. Qwen3.8 Max (low) is faster at 21.83s vs 32.78s, with pass rates of 74.2% vs 80.3%.

Last updated at: 2026-09-10

Compared models

Rank
#52
Total Output Tokens
452,940
Response Time (avg)
32.78s
Total Cost
$0.287
Rank
#49
Total Output Tokens
83,821
Response Time (avg)
21.83s
Total Cost
$0.702
Recommended model DeepSeek V4.1 Flash (high)

Its score stays close to the best score here (8.5 vs 8.6), while costing about 2.4x less than Qwen3.8 Max (low).

Detailed comparison

Metric DeepSeek V4.1 Flash DeepSeek V4.1 Flash high Release: 2026-09-10 Qwen3.8 Max Qwen3.8 Max low Release: 2026-08-04
Score 8.5 8.6
Rank #52 #49
Reliability 9.6 10.0
Consistency 8.9 9.0
Attempts 66/66 66/66
Tests Correct
Attempt pass rate 74.2% 80.3%
Flaky tests 3 3
Total Runs 66 66
Cost per result 1.912 4.384
Total Cost $0.287 $0.702
Input Price $0.150 / 1M $2.000 / 1M
Output Price $0.600 / 1M $6.000 / 1M
Total Input Tokens 100,111 99,181
Output Tokens 6,642 6,132
Reasoning Tokens 446,298 77,689
Response Time (avg) 32.78s 21.83s
Response Time (max) 205.53s 155.75s
Response Time (total) 721.15s 480.37s
Parameters 748B total (16B active) 2.4T total (~100B active)
Availability Open source Closed

Model generation showcase

Hamster playing table tennis

Prompt: Create a detailed SVG illustration of a hamster playing table tennis.

#52 DeepSeek V4.1 Flash

high
Cost
$0.035
Time
111.0s
Tokens
29,201 tok

#49 Qwen3.8 Max

low
Cost
$0.026
Time
68.5s
Tokens
4,280 tok

Top Models by Score

Score vs Total Cost

Response Time (avg)

Score vs Response Time (avg)

Total Output Tokens

Score vs Total Output Tokens

Category Breakdown

Coding Score Consistency Attempt pass rate Flaky tests Tests Correct Response Time (avg) Input Tokens Output Tokens Reasoning Tokens
DeepSeek V4.1 Flash 10.0 10.0 100.0% 0 49.19s 7,509 376 108,285
Qwen3.8 Max 8.4 7.4 88.9% 1 28.69s 8,127 512 15,952

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