Model comparison

DeepSeek-V3 vs Mistral Nemo

DeepSeek-V3 is the stronger model overall, scoring 39.5 to 26.4 on the Noometry Index. Mistral Nemo costs 2.7× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

Last verified October 12, 2026. 6 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

Mistral Nemo

Mistral AI

26.4

Rank #337 Confirmed

Summary

They share 6 benchmarks with published results for both. DeepSeek-V3 scores higher in 3 categories and Mistral Nemo in 1 category; 3 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-V3 leads 57.4 to 28.5.
The biggest single-benchmark swing is MATH Level 5: 75.5% for DeepSeek-V3 and 10.8% for Mistral Nemo.
Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
DeepSeek-V3 accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3 and Mistral Nemo specifications
	DeepSeek-V3	Mistral Nemo
Provider	DeepSeek	Mistral AI
Noometry Index	39.5	26.4
Released	2024-12-26	2024-07-01
Weights	Open	Open
Context window	164K	128K
Max output	164K	128K
Input $ / M tokens	$0.24	$0.15
Output $ / M tokens	$0.90	$0.15
Results tracked	60	10

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Category by category

Coding Not comparable

DeepSeek-V3: 42.3 (#106), Mistral Nemo: —

Coding benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
Aider Polyglot	55.1%	—
SciCode	35.8%	—
WeirdML	36.1%	—
BigCodeBench Instruct	50%	—
LiveBench Coding	70.9%	—
LMArena Coding	1368	—
BigCodeBench Complete	62.2%	—
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, Mistral Nemo: 23.5 (#125)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
Berkeley Function Calling Leaderboard	—	27.6%
BALROG	—	17.6%
METR Time Horizons	49.6%	—

Reasoning Too close to call

DeepSeek-V3: 20.5 (#236), Mistral Nemo: 20.7 (#232)

Reasoning benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
DTBench	64.8%	48.6%
Epoch Capabilities Index	135.94	118.68
PIQA	84.7%	83.5%
SimpleBench	27.2%	—
Kagi LLM Benchmark	52.3%	—
CritPt	0%	—
LiveBench Reasoning	65.8%	—
LMArena Hard Prompts	1365	—
LiveBench Data Analysis	60.9%	—
LMCA	15.5%	—
BIG-Bench Hard	87.5%	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
WinoGrande	85.2%	—

Math DeepSeek-V3 leads

DeepSeek-V3: 32.1 (#219), Mistral Nemo: 25.5 (#268)

Math benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
MATH Level 5	75.5%	10.8%
OTIS Mock AIME 2024-2025	37.8%	—
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
LMArena Math	1373	—
FrontierMath (Feb 2025 set)	1.7%	—
GSM8K	—	84.2%

Knowledge DeepSeek-V3 leads

DeepSeek-V3: 37.5 (#155), Mistral Nemo: 12.3 (#298)

Knowledge benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
GPQA Diamond	67.6%	29.9%
MMLU-Pro	72.3%	—
Confabulations	26.1%	—
Vectara Hallucination Rate	6.1%	—
GPQA (HELM)	53.8%	—
LMArena Expert	1351	—
ARC (AI2) Challenge	95.3%	—
BoolQ	—	82.5%
MMLU	87.2%	—
TriviaQA	82.9%	—

Multilingual Not comparable

DeepSeek-V3: 48.5 (#143), Mistral Nemo: —

Multilingual benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
LMArena Non-English	1358	—
LMArena Chinese	1391	—
LMArena French	1385	—
LMArena German	1374	—
LMArena Japanese	1333	—
LMArena Korean	1319	—
LMArena Russian	1373	—
LMArena Spanish	1358	—

Instruction Following Not comparable

DeepSeek-V3: 72.8 (#130), Mistral Nemo: —

Instruction Following benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—
LMArena Instruction Following	1345	—

Long Context Not comparable

DeepSeek-V3: 34.0 (#253), Mistral Nemo: —

Long Context benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
Fiction.LiveBench	50%	—
LMArena Longer Query	1352	—

Writing & Preference DeepSeek-V3 leads

DeepSeek-V3: 57.4 (#130), Mistral Nemo: 28.5 (#296)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	Mistral Nemo
EQ-Bench Creative Writing	1472	881
LMArena Text	1375	—
LMArena Creative Writing	1364	—
Short-Story Creative Writing	77%	—
WildBench	83%	—
LMArena Multi-Turn	1389	—
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than Mistral Nemo?

Which is cheaper, DeepSeek-V3 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 lists at $0.24 and $0.90.

Which has the bigger context window?

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

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

6 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Mistral Nemo has 10.