DeepSeek R1 vs Mistral Large 2
Architecture Comparison
SpecDeepSeek R1Mistral Large 2
TypeMOEDENSE
Total Parameters671B123B
Active Parameters37B123B
Layers6188
Hidden Dimension7,16812,288
Attention Heads12896
KV Heads18
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts256N/A
Active Experts8N/A
Memory Requirements
PrecisionDeepSeek R1Mistral Large 2
BF16 Weights1342.0 GB246.0 GB
FP8 Weights671.0 GB123.0 GB
INT4 Weights335.5 GB61.5 GB
KV-Cache / Token31232 B360448 B
Activation Estimate3.00 GB3.50 GB
Minimum GPUs Needed (BF16)
H100 SXMN/A4 GPUs
L40SN/A7 GPUs
Quality Benchmarks
BenchmarkDeepSeek R1Mistral Large 2
Overall9282
MMLU90.884.0
HumanEval71.753.0
GSM8K97.391.2
MT-Bench89.084.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Mistral Large 2
MMLU
84.0
HumanEval
53.0
GSM8K
91.2
MT-Bench
84.0
Capabilities
FeatureDeepSeek R1Mistral Large 2
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✓ Yes✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
Cheapest Output (Mistral Large 2)
$2.50/M
Input: $2.50/M
| Provider | DeepSeek R1 In $/M | Out $/M | Mistral Large 2 In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | $0.55 | $2.19 | — | — |
| together | $3.00 | $7.00 | $2.50 | $2.50 |
| mistral | — | — | $2.00 | $6.00 |
Recommendation Summary
- ‣DeepSeek R1 scores higher on overall quality (92 vs 82).
- ‣DeepSeek R1 is cheaper per output token ($2.19/M vs $2.50/M).
- ‣Mistral Large 2 has a smaller memory footprint (246.0 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣DeepSeek R1 uses MOE architecture while Mistral Large 2 uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek R1 is stronger at code generation (HumanEval: 71.7 vs 53.0).
- ‣DeepSeek R1 is better at math reasoning (GSM8K: 97.3 vs 91.2).