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DeepSeek V4.1 Flash (max) vs Qwen3.7 Max (medium)

DeepSeek V4.1 Flash (max) leads on average score with 8.6 vs 8.5. DeepSeek V4.1 Flash (max) has the lower benchmark cost at $0.368 vs $2.046. Qwen3.7 Max (medium) is faster at 48.35s vs 61.58s, with pass rates of 69.6% vs 85.5%.

Last updated at: 2026-10-01

Compared models

Rank
#59
Total Output Tokens
908,171
Response Time (avg)
61.58s
Total Cost
$0.368
Rank
#67
Total Output Tokens
259,032
Response Time (avg)
48.35s
Total Cost
$2.046
Recommended model DeepSeek V4.1 Flash (max)

It has the best score here (8.6), while costing about 5.6x less than Qwen3.7 Max (medium).

Detailed comparison

Metric DeepSeek V4.1 Flash DeepSeek V4.1 Flash max Release: 2026-09-10 Qwen3.7 Max Qwen3.7 Max medium Release: 2026-05-22
Score 8.6 8.5
Rank #59 #67
Reliability 9.4 10.0
Consistency 9.3 8.7
Attempts 69/69 69/69
Tests Correct
Attempt pass rate 69.6% 85.5%
Flaky tests 2 4
Total Runs 69 69
Cost per result 3.715 12.033
Total Cost $0.368 $2.046
Input Price $0.020 / 1M $1.475 / 1M
Output Price $0.396 / 1M $4.425 / 1M
Total Input Tokens 414,054 367,848
Output Tokens 7,635 8,753
Reasoning Tokens 900,536 250,279
Response Time (avg) 61.58s 48.35s
Response Time (max) 335.08s 556.06s
Response Time (total) 1416.24s 1112.11s
Parameters 748B total (16B active) ~1T total (~40B active)
Availability Open source Closed

Model generation showcase

Hamster playing table tennis

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

#59 DeepSeek V4.1 Flash

max
Cost
$0.066
Time
211.0s
Tokens
54,712 tok

#67 Qwen3.7 Max

medium
Cost
$0.017
Time
68.8s
Tokens
4,526 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 62.50s 7,509 373 132,357
Qwen3.7 Max 10.0 10.0 100.0% 0 35.31s 7,893 423 34,808

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