Model comparison

DeepSeek-V3.2-Exp vs Qwen3-1.7B

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 26.2 on the Noometry Index.

Last verified October 10, 2026. 4 shared benchmarks.

DeepSeek-V3.2-Exp

DeepSeek

44.9

Rank #68 Confirmed

Qwen3-1.7B

Alibaba (Qwen)

26.2

Rank #331 Reported

Summary

They share 4 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 4 categories and Qwen3-1.7B in 0 categories; 4 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 19.6.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 8.1% for Qwen3-1.7B.

Side by side

DeepSeek-V3.2-Exp and Qwen3-1.7B specifications
	DeepSeek-V3.2-Exp	Qwen3-1.7B
Provider	DeepSeek	Alibaba (Qwen)
Noometry Index	44.9	26.2
Released	2025-09-29	2025-04-29
Weights	Open	Open
Context window	164K	—
Max output	66K	—
Input $ / M tokens	$0.26	—
Output $ / M tokens	$0.38	—
Results tracked	48	4

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

Category by category

Coding Not comparable

DeepSeek-V3.2-Exp: 46.4 (#61), Qwen3-1.7B: —

Coding benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
SWE-bench Verified (bash only)	70%	—
Aider Polyglot	74.2%	—
LMArena WebDev	1362	—
SWE-bench Multilingual	59%	—
SciCode	38.9%	—
WeirdML	39.5%	—
LMArena Coding	1454	—

Agentic & Tool Use DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 37.9 (#31), Qwen3-1.7B: 24.5 (#108)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
Berkeley Function Calling Leaderboard	56.7%	28.4%
Terminal-Bench	39.6%	—
TheAgentCompany	42.9%	—
Vending-Bench 2	1,034	—

Reasoning DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 22.0 (#185), Qwen3-1.7B: 18.5 (#263)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
Chess Puzzles	14%	0%
ARC-AGI-2	4%	—
Kagi LLM Benchmark	52.2%	—
NYT Connections (extended)	36.7%	—
ARC-AGI-1	57%	—
CritPt	2.9%	—
Thematic Generalization	65%	—
LMArena Hard Prompts	1433	—
DTBench	87.7%	—
LMCA	29.1%	—
Epoch Capabilities Index	146.28	—

Math DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 40.8 (#75), Qwen3-1.7B: 15.4 (#288)

Math benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
OTIS Mock AIME 2024-2025	87.8%	8.1%
MathArena Final-Answer Competitions	57.7%	—
ProofBench	8%	—
LMArena Math	1435	—
FrontierMath (Feb 2025 set)	22.1%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 51.6 (#61), Qwen3-1.7B: 19.6 (#273)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
GPQA Diamond	83.4%	38%
Vectara Hallucination Rate	5.3%	—
LMArena Expert	1436	—

Multilingual Not comparable

DeepSeek-V3.2-Exp: 52.4 (#85), Qwen3-1.7B: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
LMArena Non-English	1409	—
LMArena Chinese	1461	—
LMArena French	1435	—
LMArena German	1440	—
LMArena Japanese	1377	—
LMArena Korean	1372	—
LMArena Russian	1423	—
LMArena Spanish	1441	—

Instruction Following Not comparable

DeepSeek-V3.2-Exp: 74.7 (#89), Qwen3-1.7B: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
LMArena Instruction Following	1413	—

Long Context Not comparable

DeepSeek-V3.2-Exp: 47.6 (#15), Qwen3-1.7B: —

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
Fiction.LiveBench	83.3%	—
CL-bench	13.2%	—
CL-bench Life	9.5%	—
LMArena Longer Query	1428	—

Writing & Preference Not comparable

DeepSeek-V3.2-Exp: 62.5 (#73), Qwen3-1.7B: —

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Exp	Qwen3-1.7B
LMArena Text	1425	—
LMArena Creative Writing	1403	—
EQ-Bench Creative Writing	1515	—
LMArena Multi-Turn	1427	—

Frequently asked questions

Is DeepSeek-V3.2-Exp better than Qwen3-1.7B?