Model comparison

DeepSeek-R1 vs Mistral Nemo

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 26.4 on the Noometry Index. Mistral Nemo costs 6.1× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

Last verified October 10, 2026. 5 shared benchmarks.

DeepSeek-R1

DeepSeek

42.3

Rank #115 Confirmed

Mistral Nemo

Mistral AI

26.4

Rank #337 Confirmed

Summary

They share 5 benchmarks with published results for both. DeepSeek-R1 scores higher in 4 categories and Mistral Nemo in 1 category; 5 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-R1 leads 61.4 to 28.5.
The biggest single-benchmark swing is MATH Level 5: 96.6% for DeepSeek-R1 and 10.8% for Mistral Nemo.
Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
DeepSeek-R1 accepts more context: 164K tokens versus 128K.
Mistral Nemo has downloadable open weights; the other is API-only.

Side by side

DeepSeek-R1 and Mistral Nemo specifications
	DeepSeek-R1	Mistral Nemo
Provider	DeepSeek	Mistral AI
Noometry Index	42.3	26.4
Released	2025-01-20	2024-07-01
Weights	Proprietary	Open
Context window	164K	128K
Max output	64K	128K
Input $ / M tokens	$0.50	$0.15
Output $ / M tokens	$2.15	$0.15
Results tracked	52	10

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Category by category

Coding Not comparable

DeepSeek-R1: 46.3 (#68), Mistral Nemo: —

Coding benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
Aider Polyglot	71.4%	—
SciCode	35.7%	—
WeirdML	41.6%	—
LiveBench Coding	66.7%	—
LMArena Coding	1427	—
ALE-Bench	804.12	—
AlgoTune	1.7	—

Agentic & Tool Use DeepSeek-R1 leads

DeepSeek-R1: 30.7 (#75), Mistral Nemo: 23.5 (#125)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
BALROG	34.9%	17.6%
Berkeley Function Calling Leaderboard	—	27.6%
DeepResearch Bench	35.1%	—
METR Time Horizons	53.8%	—

Reasoning Mistral Nemo leads

DeepSeek-R1: 18.6 (#278), Mistral Nemo: 20.7 (#232)

Reasoning benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
Epoch Capabilities Index	141.29	118.68
ARC-AGI-2	1.3%	—
SimpleBench	40.8%	—
Kagi LLM Benchmark	69.4%	—
ARC-AGI-1	21.2%	—
CritPt	1.1%	—
LiveBench Reasoning	83.2%	—
LMArena Hard Prompts	1416	—
DTBench	—	48.6%
LiveBench Data Analysis	69.8%	—
ForecastBench	60	—
LiveBench	71.6%	—
PIQA	—	83.5%

Math DeepSeek-R1 leads

DeepSeek-R1: 43.8 (#79), Mistral Nemo: 25.5 (#268)

Math benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
MATH Level 5	96.6%	10.8%
OTIS Mock AIME 2024-2025	66.4%	—
Omni-MATH	42.4%	—
LiveBench Math	80.7%	—
LMArena Math	1400	—
GSM8K	—	84.2%

Knowledge DeepSeek-R1 leads

DeepSeek-R1: 44.5 (#87), Mistral Nemo: 12.3 (#298)

Knowledge benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
GPQA Diamond	76.3%	29.9%
MMLU-Pro	79.3%	—
Confabulations	12.7%	—
Vectara Hallucination Rate	11.3%	—
GPQA (HELM)	66.6%	—
LMArena Expert	1394	—
BoolQ	—	82.5%

Multilingual Not comparable

DeepSeek-R1: 52.4 (#85), Mistral Nemo: —

Multilingual benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
LMArena Non-English	1412	—
LMArena Chinese	1442	—
LMArena French	1417	—
LMArena German	1404	—
LMArena Japanese	1391	—
LMArena Korean	1360	—
LMArena Russian	1423	—
LMArena Spanish	1411	—

Instruction Following Not comparable

DeepSeek-R1: 72.0 (#143), Mistral Nemo: —

Instruction Following benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
LiveBench Instruction Following	80.5%	—
IFEval	78.4%	—
LMArena Instruction Following	1382	—

Long Context Not comparable

DeepSeek-R1: 45.4 (#36), Mistral Nemo: —

Long Context benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
Fiction.LiveBench	75%	—
LMArena Longer Query	1391	—

Writing & Preference DeepSeek-R1 leads

DeepSeek-R1: 61.4 (#88), Mistral Nemo: 28.5 (#296)

Writing & Preference benchmarks
Benchmark	DeepSeek-R1	Mistral Nemo
EQ-Bench Creative Writing	1500	881
LMArena Text	1428	—
LMArena Creative Writing	1405	—
Short-Story Creative Writing	83%	—
WildBench	82.8%	—
LMArena Multi-Turn	1405	—
LiveBench Language	48.5%	—

Frequently asked questions

Is DeepSeek-R1 better than Mistral Nemo?

Which is cheaper, DeepSeek-R1 or Mistral Nemo?

Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.

Which has the bigger context window?

DeepSeek-R1 does, with 164K tokens against 128K.

How many benchmarks do DeepSeek-R1 and Mistral Nemo share?

5 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mistral Nemo has 10.