Model comparison

DeepSeek-V3.1 vs Gemini 2.5 Flash

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.3 on the Noometry Index.

Last verified October 10, 2026. 27 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Gemini 2.5 Flash

Google

39.3

Rank #170 Confirmed

Summary

They share 27 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Gemini 2.5 Flash in 4 categories; 7 gaps are clear of the uncertainty.
The widest gap is in long context, where Gemini 2.5 Flash leads 47.5 to 36.3.
The biggest single-benchmark swing is Fiction.LiveBench: 52.8% for DeepSeek-V3.1 and 77.8% for Gemini 2.5 Flash.
DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.30 / $2.50 for Gemini 2.5 Flash.
Gemini 2.5 Flash accepts more context: 1.05M tokens versus 164K.
DeepSeek-V3.1 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.1 and Gemini 2.5 Flash specifications
	DeepSeek-V3.1	Gemini 2.5 Flash
Provider	DeepSeek	Google
Noometry Index	42.8	39.3
Released	2025-08-21	2025-04-17
Weights	Open	Proprietary
Context window	164K	1.05M
Max output	8K	66K
Input $ / M tokens	$0.25	$0.30
Output $ / M tokens	$0.95	$2.50
Results tracked	27	54

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Gemini 2.5 Flash: 35.8 (#220)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
WeirdML	38.4%	41.9%
LMArena Coding	1417	1424
SWE-bench Verified (bash only)	—	28.7%
Aider Polyglot	—	55.1%
ALE-Bench	—	661.88

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Gemini 2.5 Flash: 30.8 (#74)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
Terminal-Bench	—	17.1%
Berkeley Function Calling Leaderboard	—	56.2%
TheAgentCompany	—	41.1%
BALROG	—	33.5%
Vending-Bench 2	—	548.84

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Gemini 2.5 Flash: 18.1 (#286)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
SimpleBench	40%	41.2%
Kagi LLM Benchmark	53.2%	56.8%
LMArena Hard Prompts	1417	1422
DTBench	82.7%	76.5%
LMCA	24.3%	27.5%
Epoch Capabilities Index	139.92	143.03
ForecastBench	58	60.6
ARC-AGI-2	—	2.5%
ARC-AGI-1	—	33.3%
CritPt	—	1.1%
EnigmaEval	—	2.7%

Math Gemini 2.5 Flash leads

DeepSeek-V3.1: 38.9 (#122), Gemini 2.5 Flash: 39.9 (#98)

Math benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
LMArena Math	1420	1415
OTIS Mock AIME 2024-2025	—	73.1%
Omni-MATH	—	38.5%
FrontierMath (Feb 2025 set)	—	4.8%
FrontierMath Tier 4 (v1)	—	4.2%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Gemini 2.5 Flash: 36.4 (#168)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
Vectara Hallucination Rate	5.5%	7.8%
LMArena Expert	1405	1426
Humanity's Last Exam	—	12.1%
MMLU-Pro	—	63.9%
Confabulations	—	16.8%
GPQA (HELM)	—	39%

Multimodal Not comparable

DeepSeek-V3.1: —, Gemini 2.5 Flash: 41.8 (#32)

Multimodal benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
LMArena Vision	—	1253
GeoBench	—	76%
VPCT	—	46.2%
SpatialViz-Bench	—	36.9%

Multilingual Too close to call

DeepSeek-V3.1: 51.6 (#106), Gemini 2.5 Flash: 52.3 (#88)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
LMArena Non-English	1400	1409
LMArena Chinese	1469	1450
LMArena French	1447	1433
LMArena German	1411	1418
LMArena Japanese	1378	1405
LMArena Korean	1337	1385
LMArena Russian	1405	1415
LMArena Spanish	1431	1421

Instruction Following Gemini 2.5 Flash leads

DeepSeek-V3.1: 73.9 (#110), Gemini 2.5 Flash: 75.7 (#54)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
LMArena Instruction Following	1400	1405
IFEval	—	89.8%

Long Context Gemini 2.5 Flash leads

DeepSeek-V3.1: 36.3 (#232), Gemini 2.5 Flash: 47.5 (#17)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
Fiction.LiveBench	52.8%	77.8%
LMArena Longer Query	1422	1419

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Gemini 2.5 Flash: 53.8 (#157)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash
LMArena Text	1420	1417
LMArena Creative Writing	1401	1400
EQ-Bench Creative Writing	1436	1137
LMArena Multi-Turn	1408	1408
Short-Story Creative Writing	—	76.5%
WildBench	—	81.7%

Frequently asked questions

Is DeepSeek-V3.1 better than Gemini 2.5 Flash?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.3 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or Gemini 2.5 Flash?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Gemini 2.5 Flash lists at $0.30 and $2.50.

Is DeepSeek-V3.1 or Gemini 2.5 Flash better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 35.8 in the Noometry coding category.

Which has the bigger context window?

Gemini 2.5 Flash does, with 1.05M tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and Gemini 2.5 Flash share?

27 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemini 2.5 Flash has 54.