Compare · ModelsLive · 2 picked · head to head
DeepSeek V4.1 Flash 1017 vs GPT-5 Mini
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
DeepSeek V4.1 Flash 1017 wins on 9/9 benchmarks
DeepSeek V4.1 Flash 1017 wins 9 of 9 shared benchmarks. Leads in reasoning · knowledge · math.
Category leads
reasoning·DeepSeek V4.1 Flash 1017knowledge·DeepSeek V4.1 Flash 1017math·DeepSeek V4.1 Flash 1017general·DeepSeek V4.1 Flash 1017
Hype vs Reality
Attention vs performance
DeepSeek V4.1 Flash 1017
#41 by perf·#4 by attention
GPT-5 Mini
#120 by perf·#9 by attention
Best value
DeepSeek V4.1 Flash 1017
5.6x better value than GPT-5 Mini
DeepSeek V4.1 Flash 1017
254.9 pts/$
$0.25/M
GPT-5 Mini
45.8 pts/$
$1.13/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
9 benchmarks · 2 models
DeepSeek V4.1 Flash 1017GPT-5 Mini
ARC-AGI
DeepSeek V4.1 Flash 1017 leads by +40.2
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
DeepSeek V4.1 Flash 1017
94.5
GPT-5 Mini
54.3
ARC-AGI-2
DeepSeek V4.1 Flash 1017 leads by +73.6
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
DeepSeek V4.1 Flash 1017
78.1
GPT-5 Mini
4.4
Chess Puzzles
DeepSeek V4.1 Flash 1017 leads by +23.1
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
DeepSeek V4.1 Flash 1017
43.2
GPT-5 Mini
20.0
FrontierMath-Tier-4-v2-Private
DeepSeek V4.1 Flash 1017 leads by +34.1
DeepSeek V4.1 Flash 1017
46.3
GPT-5 Mini
12.2
FrontierMath-Tiers-1-3-v2-Private
DeepSeek V4.1 Flash 1017 leads by +25.9
DeepSeek V4.1 Flash 1017
75.0
GPT-5 Mini
49.1
GPQA diamond
DeepSeek V4.1 Flash 1017 leads by +25.3
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
DeepSeek V4.1 Flash 1017
94.7
GPT-5 Mini
69.4
Mystery Game Puzzles
DeepSeek V4.1 Flash 1017 leads by +43.0
DeepSeek V4.1 Flash 1017
43.8
GPT-5 Mini
0.9
OTIS Mock AIME 2024-2025
DeepSeek V4.1 Flash 1017 leads by +12.8
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
DeepSeek V4.1 Flash 1017
99.4
GPT-5 Mini
86.7
SimpleQA Verified
DeepSeek V4.1 Flash 1017 leads by +21.5
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
DeepSeek V4.1 Flash 1017
42.5
GPT-5 Mini
21.0
Full benchmark table
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5 Mini |
|---|---|---|
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 94.5 | 54.3 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 78.1 | 4.4 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 43.2 | 20.0 |
FrontierMath-Tier-4-v2-Private | 46.3 | 12.2 |
FrontierMath-Tiers-1-3-v2-Private | 75.0 | 49.1 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 94.7 | 69.4 |
Mystery Game Puzzles | 43.8 | 0.9 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 99.4 | 86.7 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 42.5 | 21.0 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.01 | $0.50 | 1.0M tokens (~524 books) | $1.29 | |
| $0.25 | $2.00 | 400K tokens (~200 books) | $6.88 |
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