Model comparison

DeepSeek-V3.2-Speciale vs Llama 3.1-8B

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 23.0 on the Noometry Index. Llama 3.1-8B costs 15× 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. 2 shared benchmarks.

DeepSeek-V3.2-Speciale

DeepSeek

39.7

Rank #162 Reported

Llama 3.1-8B

Summary

They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 3 categories and Llama 3.1-8B in 0 categories; 3 gaps are clear of the uncertainty.
The widest gap is in coding, where DeepSeek-V3.2-Speciale leads 40.4 to 20.2.
The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 1.7% for Llama 3.1-8B.
Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.

Side by side

DeepSeek-V3.2-Speciale and Llama 3.1-8B specifications
	DeepSeek-V3.2-Speciale	Llama 3.1-8B
Provider	DeepSeek	Meta
Noometry Index	39.7	23.0
Released	2025-12-01	2024-07-23
Weights	Open	Open
Context window	128K	128K
Max output	128K	4K
Input $ / M tokens	$0.58	$0.05
Output $ / M tokens	$1.68	$0.08
Results tracked	3	43

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Category by category

Coding DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 40.4 (#140), Llama 3.1-8B: 20.2 (#340)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
WeirdML	46.7%	1.7%
SciCode	—	13.2%
BigCodeBench Instruct	—	32.8%
LMArena Coding	—	1195
BigCodeBench Complete	—	40.5%
HumanEval+	—	62.8%
MBPP+	—	55.6%

Agentic & Tool Use Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3.1-8B: 22.5 (#131)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
Berkeley Function Calling Leaderboard	—	25.8%
BALROG	—	15.1%

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.9 (#73), Llama 3.1-8B: 14.9 (#321)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
SimpleBench	52.6%	—
CritPt	—	0%
Chess Puzzles	—	0%
LMArena Hard Prompts	—	1175
DTBench	—	50.9%
LMCA	—	5.4%
Epoch Capabilities Index	—	116.57
PIQA	—	81.2%

Math Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3.1-8B: 10.2 (#317)

Math benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
OTIS Mock AIME 2024-2025	—	1.7%
Omni-MATH	—	13.7%
LMArena Math	—	1179
MATH Level 5	—	22.9%
GSM8K	—	82.4%

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3.1-8B: 8.0 (#307)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
GPQA Diamond	—	27%
MMLU-Pro	—	40.6%
GPQA (HELM)	—	24.7%
LMArena Expert	—	1144
BoolQ	—	82.8%
MMLU	—	56.1%

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3.1-8B: 34.0 (#249)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
LMArena Non-English	—	1148
LMArena Chinese	—	1151
LMArena French	—	1177
LMArena German	—	1144
LMArena Japanese	—	1061
LMArena Korean	—	1053
LMArena Russian	—	1158
LMArena Spanish	—	1169

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3.1-8B: 58.9 (#258)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
IFEval	—	74.3%
LMArena Instruction Following	—	1159

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3.1-8B: 35.8 (#238)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
LMArena Longer Query	—	1182

Writing & Preference DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 46.0 (#222), Llama 3.1-8B: 29.7 (#290)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3.1-8B
EQ-Bench Creative Writing	1276	713
LMArena Text	—	1187
LMArena Creative Writing	—	1154
WildBench	—	68.7%
LMArena Multi-Turn	—	1172

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than Llama 3.1-8B?

Which is cheaper, DeepSeek-V3.2-Speciale or Llama 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.

Is DeepSeek-V3.2-Speciale or Llama 3.1-8B better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 20.2 in the Noometry coding category.

Which has the bigger context window?

Both accept 128K tokens.

How many benchmarks do DeepSeek-V3.2-Speciale and Llama 3.1-8B share?

2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Llama 3.1-8B has 43.