Noometry

Model comparison

DBRX vs DeepSeek-V3.2-Exp

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 29.4 on the Noometry Index.

Last verified . 18 shared benchmarks.

DBRX Databricks

29.4

Rank #311 Confirmed

DeepSeek-V3.2-Exp DeepSeek

44.3

Rank #78 Confirmed

Summary

  • They share 18 benchmarks with published results for both. DBRX scores higher in 0 categories and DeepSeek-V3.2-Exp in 8 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 14.9.
  • The biggest single-benchmark swing is GPQA Diamond: 32.9% for DBRX and 83.4% for DeepSeek-V3.2-Exp.

Side by side

DBRXDeepSeek-V3.2-Exp
ProviderDatabricksDeepSeek
Noometry Index29.444.3
Released2024-03-272025-09-29
WeightsOpenOpen
Context window—164K
Max output—66K
Input $ / M tokens—$0.26
Output $ / M tokens—$0.38
Results tracked2149

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Category by category

Coding DeepSeek-V3.2-Exp leads

DBRX: 32.9 (#266), DeepSeek-V3.2-Exp: 46.5 (#65)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Coding11321454
SWE-bench Verified (bash only)—70%
Aider Polyglot—74.2%
LMArena WebDev—1362
SWE-bench Multilingual—59%
SciCode—38.9%
WeirdML—39.5%
HumanEval+70.1%—
MBPP+55.8%—

Agentic & Tool Use Not comparable

DBRX: —, DeepSeek-V3.2-Exp: 32.7 (#59)

BenchmarkDBRXDeepSeek-V3.2-Exp
Terminal-Bench—39.6%
APEX-Agents—21.3%
Berkeley Function Calling Leaderboard—56.7%
TheAgentCompany—42.9%
Vending-Bench 2—1,034

Reasoning Too close to call

DBRX: 21.4 (#222), DeepSeek-V3.2-Exp: 22.1 (#208)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Hard Prompts11131434
ARC-AGI-2—4%
Kagi LLM Benchmark—52.2%
NYT Connections (extended)—36.7%
ARC-AGI-1—57%
CritPt—2.9%
Chess Puzzles—14%
Thematic Generalization—65%
DTBench—87.7%
LMCA—29.1%
Epoch Capabilities Index—146.27

Math DeepSeek-V3.2-Exp leads

DBRX: 24.3 (#269), DeepSeek-V3.2-Exp: 41.7 (#87)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Math11451435
MathArena Final-Answer Competitions—57.7%
OTIS Mock AIME 2024-2025—87.8%
ProofBench—8%
MATH Level 511.7%—
FrontierMath (Feb 2025 set)—22.1%
FrontierMath Tier 4 (v1)—2.1%

Knowledge DeepSeek-V3.2-Exp leads

DBRX: 14.9 (#294), DeepSeek-V3.2-Exp: 51.7 (#66)

BenchmarkDBRXDeepSeek-V3.2-Exp
GPQA Diamond32.9%83.4%
LMArena Expert10761436
Vectara Hallucination Rate—5.3%

Multilingual DeepSeek-V3.2-Exp leads

DBRX: 29.3 (#268), DeepSeek-V3.2-Exp: 52.2 (#90)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Non-English10711409
LMArena Chinese10681461
LMArena French10961433
LMArena German10571440
LMArena Japanese9901374
LMArena Korean9931371
LMArena Russian10781424
LMArena Spanish10641440

Instruction Following DeepSeek-V3.2-Exp leads

DBRX: 57.5 (#270), DeepSeek-V3.2-Exp: 74.5 (#93)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Instruction Following11121413

Long Context DeepSeek-V3.2-Exp leads

DBRX: 33.7 (#258), DeepSeek-V3.2-Exp: 47.6 (#16)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Longer Query11121428
Fiction.LiveBench—83.3%
CL-bench—13.2%
CL-bench Life—9.5%

Writing & Preference DeepSeek-V3.2-Exp leads

DBRX: 33.6 (#275), DeepSeek-V3.2-Exp: 62.4 (#77)

BenchmarkDBRXDeepSeek-V3.2-Exp
LMArena Text11191425
LMArena Creative Writing11041403
LMArena Multi-Turn11111427
EQ-Bench Creative Writing—1515

Frequently asked questions

Is DBRX better than DeepSeek-V3.2-Exp?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 29.4 on the Noometry Index.

Is DBRX or DeepSeek-V3.2-Exp better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 32.9 in the Noometry coding category.

How many benchmarks do DBRX and DeepSeek-V3.2-Exp share?

18 benchmarks have published results for both models. DBRX has 21 scored results on Noometry and DeepSeek-V3.2-Exp has 49.

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