Mistral Large 2 vs DeepSeek R1
Architecture Comparison
SpecMistral Large 2DeepSeek R1
TypeDENSEMOE
Total Parameters123B671B
Active Parameters123B37B
Layers8861
Hidden Dimension12,2887,168
Attention Heads96128
KV Heads81
Context Length131,072131,072
Precision (default)BF16BF16
Total ExpertsN/A256
Active ExpertsN/A8
Memory Requirements
PrecisionMistral Large 2DeepSeek R1
BF16 Weights246.0 GB1342.0 GB
FP8 Weights123.0 GB671.0 GB
INT4 Weights61.5 GB335.5 GB
KV-Cache / Token360448 B31232 B
Activation Estimate3.50 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXM4 GPUsN/A
L40S7 GPUsN/A
Quality Benchmarks
BenchmarkMistral Large 2DeepSeek R1
Overall8292
MMLU84.090.8
HumanEval53.071.7
GSM8K91.297.3
MT-Bench84.089.0
Mistral Large 2
MMLU
84.0
HumanEval
53.0
GSM8K
91.2
MT-Bench
84.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Capabilities
FeatureMistral Large 2DeepSeek R1
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✓ Yes
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Mistral Large 2)
$2.50/M
Input: $2.50/M
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
| Provider | Mistral Large 2 In $/M | Out $/M | DeepSeek R1 In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | — | — | $0.55 | $2.19 |
| together | $2.50 | $2.50 | $3.00 | $7.00 |
| 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.
- ‣Mistral Large 2 uses DENSE architecture while DeepSeek R1 uses MOE. 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).