Compare · ModelsLive · 2 picked · head to head
GPT-4.1 Mini vs Mistral Large 2407
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-4.1 Mini wins on 3/3 benchmarks
GPT-4.1 Mini wins 3 of 3 shared benchmarks. Leads in knowledge · math.
Category leads
knowledge·GPT-4.1 Minimath·GPT-4.1 Mini
Hype vs Reality
Attention vs performance
GPT-4.1 Mini
#211 by perf·no signal
Mistral Large 2407
#195 by perf·#19 by attention
Best value
GPT-4.1 Mini
3.7x better value than Mistral Large 2407
GPT-4.1 Mini
36.6 pts/$
$1.00/M
Mistral Large 2407
9.8 pts/$
$4.00/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Mistral AI
$14.0B·Tier 1
Head to head
3 benchmarks · 2 models
GPT-4.1 MiniMistral Large 2407
GPQA diamond
GPT-4.1 Mini leads by +22.4
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GPT-4.1 Mini
54.5
Mistral Large 2407
32.0
MATH level 5
GPT-4.1 Mini leads by +42.5
MATH Level 5 · the hardest tier of the MATH benchmark, featuring competition-level problems from AMC, AIME, and Olympiad-style mathematics.
GPT-4.1 Mini
87.3
Mistral Large 2407
44.8
OTIS Mock AIME 2024-2025
GPT-4.1 Mini leads by +36.3
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GPT-4.1 Mini
44.7
Mistral Large 2407
8.4
Full benchmark table
| Benchmark | GPT-4.1 Mini | Mistral Large 2407 |
|---|---|---|
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 54.5 | 32.0 |
MATH level 5 MATH Level 5 · the hardest tier of the MATH benchmark, featuring competition-level problems from AMC, AIME, and Olympiad-style mathematics. | 87.3 | 44.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 44.7 | 8.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.40 | $1.60 | 1.0M tokens (~524 books) | $7.00 | |
| $2.00 | $6.00 | 131K tokens (~66 books) | $30.00 |
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