Model comparison

DeepSeek-V3.2-Speciale vs Mistral 7B

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 23.0 on the Noometry Index. Mistral 7B costs 3.4× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.

Last verified October 10, 2026. 0 shared benchmarks.

DeepSeek-V3.2-Speciale

DeepSeek

39.7

Rank #162 Reported

Mistral 7B

Mistral AI

23.0

Rank #351 Confirmed

Summary

The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 13.1.
Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
DeepSeek-V3.2-Speciale accepts more context: 128K tokens versus 8K.

Side by side

DeepSeek-V3.2-Speciale and Mistral 7B specifications
	DeepSeek-V3.2-Speciale	Mistral 7B
Provider	DeepSeek	Mistral AI
Noometry Index	39.7	23.0
Released	2025-12-01	2023-09-27
Weights	Open	Open
Context window	128K	8K
Max output	128K	8K
Input $ / M tokens	$0.58	$0.25
Output $ / M tokens	$1.68	$0.25
Results tracked	3	37

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Category by category

Coding DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 40.4 (#140), Mistral 7B: 26.4 (#326)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
WeirdML	46.7%	—
BigCodeBench Instruct	—	19.5%
LMArena Coding	—	1082
BigCodeBench Complete	—	27.3%
HumanEval+	—	36%
MBPP+	—	42.1%

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.9 (#73), Mistral 7B: 13.1 (#336)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
SimpleBench	52.6%	—
Chess Puzzles	—	0%
LMArena Hard Prompts	—	1067
DTBench	—	42.5%
Adversarial NLI	—	47.1%
BIG-Bench Hard	—	56.1%
Epoch Capabilities Index	—	112.21
HellaSwag	—	81%
PIQA	—	83%
WinoGrande	—	75.3%

Math Not comparable

DeepSeek-V3.2-Speciale: —, Mistral 7B: 8.1 (#325)

Math benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
OTIS Mock AIME 2024-2025	—	0.3%
LMArena Math	—	1085
MATH Level 5	—	3.7%
GSM8K	—	54.4%

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, Mistral 7B: 7.4 (#311)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
GPQA Diamond	—	15.2%
LMArena Expert	—	1036
ARC (AI2) Challenge	—	78.6%
BoolQ	—	87.4%
MMLU	—	62.5%
OpenBookQA	—	79.8%
TriviaQA	—	75.2%

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, Mistral 7B: 25.8 (#283)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
LMArena Non-English	—	1012
LMArena Chinese	—	1009
LMArena French	—	1037
LMArena German	—	987
LMArena Japanese	—	878
LMArena Russian	—	1018
LMArena Spanish	—	1026

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, Mistral 7B: 54.2 (#280)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
LMArena Instruction Following	—	1060

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, Mistral 7B: 32.2 (#271)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
LMArena Longer Query	—	1060

Writing & Preference DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 46.0 (#222), Mistral 7B: 30.7 (#286)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Mistral 7B
LMArena Text	—	1090
LMArena Creative Writing	—	1068
EQ-Bench Creative Writing	1276	—
LMArena Multi-Turn	—	1062

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than Mistral 7B?

Which is cheaper, DeepSeek-V3.2-Speciale or Mistral 7B?

Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.

Is DeepSeek-V3.2-Speciale or Mistral 7B better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 26.4 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.2-Speciale does, with 128K tokens against 8K.

How many benchmarks do DeepSeek-V3.2-Speciale and Mistral 7B share?

0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Mistral 7B has 37.