Model comparison

DeepSeek-V3.1 vs Step 3.7 Flash

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.3 on the Noometry Index.

Last verified October 10, 2026. 0 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Step 3.7 Flash

StepFun

37.3

Rank #207 Reported

Summary

The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 21.6.
Both cost about the same: $0.25 input and $0.95 output per million tokens.
Step 3.7 Flash accepts more context: 256K tokens versus 164K.

Side by side

DeepSeek-V3.1 and Step 3.7 Flash specifications
	DeepSeek-V3.1	Step 3.7 Flash
Provider	DeepSeek	StepFun
Noometry Index	42.8	37.3
Released	2025-08-21	2026-05-29
Weights	Open	Open
Context window	164K	256K
Max output	8K	256K
Input $ / M tokens	$0.25	$0.18
Output $ / M tokens	$0.95	$1.11
Results tracked	27	5

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding Too close to call

DeepSeek-V3.1: 40.3 (#144), Step 3.7 Flash: 40.0 (#150)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
SciCode	—	40%
WeirdML	38.4%	—
LMArena Coding	1417	—
ALE-Bench	—	694.12

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Step 3.7 Flash: 21.6 (#219)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
NYT Connections (extended)	—	39.7%
CritPt	—	2.3%
LMArena Hard Prompts	1417	—
DTBench	82.7%	—
LMCA	24.3%	—
Epoch Capabilities Index	139.92	—
ForecastBench	58	—

Math Step 3.7 Flash leads

DeepSeek-V3.1: 38.9 (#122), Step 3.7 Flash: 42.9 (#82)

Math benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
MathArena Final-Answer Competitions	—	68.5%
LMArena Math	1420	—

Knowledge Not comparable

DeepSeek-V3.1: 43.7 (#90), Step 3.7 Flash: —

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
Vectara Hallucination Rate	5.5%	—
LMArena Expert	1405	—

Multilingual Not comparable

DeepSeek-V3.1: 51.6 (#106), Step 3.7 Flash: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
LMArena Non-English	1400	—
LMArena Chinese	1469	—
LMArena French	1447	—
LMArena German	1411	—
LMArena Japanese	1378	—
LMArena Korean	1337	—
LMArena Russian	1405	—
LMArena Spanish	1431	—

Instruction Following Not comparable

DeepSeek-V3.1: 73.9 (#110), Step 3.7 Flash: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
LMArena Instruction Following	1400	—

Long Context Not comparable

DeepSeek-V3.1: 36.3 (#232), Step 3.7 Flash: —

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
Fiction.LiveBench	52.8%	—
LMArena Longer Query	1422	—

Writing & Preference Not comparable

DeepSeek-V3.1: 60.3 (#98), Step 3.7 Flash: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Step 3.7 Flash
LMArena Text	1420	—
LMArena Creative Writing	1401	—
EQ-Bench Creative Writing	1436	—
LMArena Multi-Turn	1408	—

Frequently asked questions

Is DeepSeek-V3.1 better than Step 3.7 Flash?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.3 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or Step 3.7 Flash?

Step 3.7 Flash is cheaper. It lists at $0.18 per million input tokens and $1.11 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

Is DeepSeek-V3.1 or Step 3.7 Flash better for coding?

They score almost the same on coding (40.3 vs 40.0); test both on your own repository before choosing.

Which has the bigger context window?

Step 3.7 Flash does, with 256K tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and Step 3.7 Flash share?

0 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Step 3.7 Flash has 5.