Noometry

Model comparison

DBRX vs DeepSeek-R1

DeepSeek-R1 is the stronger model overall, scoring 42.0 to 29.0 on the Noometry Index.

Last verified . 19 shared benchmarks.

DBRX Databricks

29.0

Rank #307 Confirmed

DeepSeek-R1 DeepSeek

42.0

Rank #110 Confirmed

Summary

  • They share 19 benchmarks with published results for both. DBRX scores higher in 1 category and DeepSeek-R1 in 7 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where DeepSeek-R1 leads 44.3 to 14.9.
  • The biggest single-benchmark swing is MATH Level 5: 11.7% for DBRX and 96.6% for DeepSeek-R1.
  • DBRX has downloadable open weights; the other is API-only.

Side by side

DBRXDeepSeek-R1
ProviderDatabricksDeepSeek
Noometry Index29.042.0
Released2024-03-272025-01-20
WeightsOpenProprietary
Context window—164K
Max output—64K
Input $ / M tokens—$0.50
Output $ / M tokens—$2.15
Results tracked2152

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Category by category

Coding DeepSeek-R1 leads

DBRX: 32.3 (#262), DeepSeek-R1: 45.9 (#63)

BenchmarkDBRXDeepSeek-R1
LMArena Coding11321427
Aider Polyglot—71.4%
SciCode—35.7%
WeirdML—41.6%
LiveBench Coding—66.7%
ALE-Bench—804.12
AlgoTune—1.7
HumanEval+70.1%—
MBPP+55.8%—

Agentic & Tool Use Not comparable

DBRX: —, DeepSeek-R1: 30.4 (#68)

BenchmarkDBRXDeepSeek-R1
DeepResearch Bench—35.1%
BALROG—34.9%
METR Time Horizons—53.8%

Reasoning DBRX leads

DBRX: 20.5 (#218), DeepSeek-R1: 18.7 (#253)

BenchmarkDBRXDeepSeek-R1
LMArena Hard Prompts11131415
ARC-AGI-2—1.3%
SimpleBench—40.8%
Kagi LLM Benchmark—69.4%
ARC-AGI-1—21.2%
CritPt—1.1%
LiveBench Reasoning—83.2%
LiveBench Data Analysis—69.8%
Epoch Capabilities Index—141.3
ForecastBench—60
LiveBench—71.6%

Math DeepSeek-R1 leads

DBRX: 22.3 (#265), DeepSeek-R1: 42.0 (#70)

BenchmarkDBRXDeepSeek-R1
LMArena Math11451401
MATH Level 511.7%96.6%
OTIS Mock AIME 2024-2025—66.4%
Omni-MATH—42.4%
LiveBench Math—80.7%

Knowledge DeepSeek-R1 leads

DBRX: 14.9 (#289), DeepSeek-R1: 44.3 (#81)

BenchmarkDBRXDeepSeek-R1
GPQA Diamond32.9%76.3%
LMArena Expert10761394
MMLU-Pro—79.3%
Confabulations—12.7%
Vectara Hallucination Rate—11.3%
GPQA (HELM)—66.6%

Multilingual DeepSeek-R1 leads

DBRX: 29.4 (#263), DeepSeek-R1: 52.6 (#80)

BenchmarkDBRXDeepSeek-R1
LMArena Non-English10711412
LMArena Chinese10681443
LMArena French10951415
LMArena German10571405
LMArena Japanese9901394
LMArena Korean9931359
LMArena Russian10781423
LMArena Spanish10641410

Instruction Following DeepSeek-R1 leads

DBRX: 57.7 (#265), DeepSeek-R1: 72.2 (#138)

BenchmarkDBRXDeepSeek-R1
LMArena Instruction Following11121382
LiveBench Instruction Following—80.5%
IFEval—78.4%

Long Context DeepSeek-R1 leads

DBRX: 33.7 (#253), DeepSeek-R1: 45.4 (#36)

BenchmarkDBRXDeepSeek-R1
LMArena Longer Query11121391
Fiction.LiveBench—75%

Writing & Preference DeepSeek-R1 leads

DBRX: 33.7 (#271), DeepSeek-R1: 61.5 (#84)

BenchmarkDBRXDeepSeek-R1
LMArena Text11191428
LMArena Creative Writing11041406
LMArena Multi-Turn11111404
Short-Story Creative Writing—83%
EQ-Bench Creative Writing—1500
WildBench—82.8%
LiveBench Language—48.5%

Frequently asked questions

Is DBRX better than DeepSeek-R1?

DeepSeek-R1 is the stronger model overall, scoring 42.0 to 29.0 on the Noometry Index.

Is DBRX or DeepSeek-R1 better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 32.3 in the Noometry coding category.

How many benchmarks do DBRX and DeepSeek-R1 share?

19 benchmarks have published results for both models. DBRX has 21 scored results on Noometry and DeepSeek-R1 has 52.

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