Model comparison

DeepSeek-V3.2-Exp vs Mistral Nemo

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 26.4 on the Noometry Index. Mistral Nemo costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

Last verified October 10, 2026. 5 shared benchmarks.

DeepSeek-V3.2-Exp

DeepSeek

44.3

Rank #78 Confirmed

Mistral Nemo

Mistral AI

26.4

Rank #337 Confirmed

Summary

They share 5 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 5 categories and Mistral Nemo in 0 categories; 5 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 12.3.
The biggest single-benchmark swing is GPQA Diamond: 83.4% for DeepSeek-V3.2-Exp and 29.9% for Mistral Nemo.
Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3.2-Exp and Mistral Nemo specifications
	DeepSeek-V3.2-Exp	Mistral Nemo
Provider	DeepSeek	Mistral AI
Noometry Index	44.3	26.4
Released	2025-09-29	2024-07-01
Weights	Open	Open
Context window	164K	128K
Max output	66K	128K
Input $ / M tokens	$0.26	$0.15
Output $ / M tokens	$0.38	$0.15
Results tracked	49	10

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

Category by category

Coding Not comparable

DeepSeek-V3.2-Exp: 46.5 (#65), Mistral Nemo: —

Coding benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
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: 32.7 (#59), Mistral Nemo: 23.5 (#125)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
Berkeley Function Calling Leaderboard	56.7%	27.6%
Terminal-Bench	39.6%	—
APEX-Agents	21.3%	—
TheAgentCompany	42.9%	—
BALROG	—	17.6%
Vending-Bench 2	1,034	—

Reasoning DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 22.1 (#208), Mistral Nemo: 20.7 (#232)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
DTBench	87.7%	48.6%
Epoch Capabilities Index	146.27	118.68
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%	—
LMArena Hard Prompts	1434	—
LMCA	29.1%	—
PIQA	—	83.5%

Math DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 41.7 (#87), Mistral Nemo: 25.5 (#268)

Math benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
MathArena Final-Answer Competitions	57.7%	—
OTIS Mock AIME 2024-2025	87.8%	—
ProofBench	8%	—
LMArena Math	1435	—
MATH Level 5	—	10.8%
FrontierMath (Feb 2025 set)	22.1%	—
FrontierMath Tier 4 (v1)	2.1%	—
GSM8K	—	84.2%

Knowledge DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 51.7 (#66), Mistral Nemo: 12.3 (#298)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
GPQA Diamond	83.4%	29.9%
Vectara Hallucination Rate	5.3%	—
LMArena Expert	1436	—
BoolQ	—	82.5%

Multilingual Not comparable

DeepSeek-V3.2-Exp: 52.2 (#90), Mistral Nemo: —

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
LMArena Non-English	1409	—
LMArena Chinese	1461	—
LMArena French	1433	—
LMArena German	1440	—
LMArena Japanese	1374	—
LMArena Korean	1371	—
LMArena Russian	1424	—
LMArena Spanish	1440	—

Instruction Following Not comparable

DeepSeek-V3.2-Exp: 74.5 (#93), Mistral Nemo: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
LMArena Instruction Following	1413	—

Long Context Not comparable

DeepSeek-V3.2-Exp: 47.6 (#16), Mistral Nemo: —

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

Writing & Preference DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 62.4 (#77), Mistral Nemo: 28.5 (#296)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Exp	Mistral Nemo
EQ-Bench Creative Writing	1515	881
LMArena Text	1425	—
LMArena Creative Writing	1403	—
LMArena Multi-Turn	1427	—

Frequently asked questions

Is DeepSeek-V3.2-Exp better than Mistral Nemo?

Which is cheaper, DeepSeek-V3.2-Exp or Mistral Nemo?

Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 128K.

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

5 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Mistral Nemo has 10.