Model comparison

DeepSeek-V3.2-Speciale vs Llama 3-8B

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 25.3 on the Noometry Index. Llama 3-8B costs 6.1× 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

39.1

Rank #159 Reported

Llama 3-8B

Summary

The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 13.8.
Llama 3-8B is cheaper at $0.14 / $0.14 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 Llama 3-8B specifications
	DeepSeek-V3.2-Speciale	Llama 3-8B
Provider	DeepSeek	Meta
Noometry Index	39.1	25.3
Released	2025-12-01	2024-04-18
Weights	Open	Open
Context window	128K	8K
Max output	128K	8K
Input $ / M tokens	$0.58	$0.14
Output $ / M tokens	$1.68	$0.14
Results tracked	3	34

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Category by category

Coding DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 39.9 (#136), Llama 3-8B: 30.5 (#285)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
WeirdML	46.7%	—
BigCodeBench Instruct	—	31.9%
LMArena Coding	—	1152
BigCodeBench Complete	—	36.9%
HumanEval+	—	56.7%
MBPP+	—	54.8%

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.1 (#65), Llama 3-8B: 13.8 (#321)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
SimpleBench	52.6%	—
Chess Puzzles	—	0%
LMArena Hard Prompts	—	1133
DTBench	—	43.9%
Adversarial NLI	—	57.3%
Epoch Capabilities Index	—	116.33
ForecastBench	—	58.6
WinoGrande	—	75.7%

Math Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3-8B: 8.3 (#317)

Math benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
OTIS Mock AIME 2024-2025	—	1.9%
LMArena Math	—	1151
MATH Level 5	—	6.1%

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3-8B: 7.8 (#303)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
GPQA Diamond	—	26.1%
LMArena Expert	—	1113
ARC (AI2) Challenge	—	82.8%
MMLU	—	68.8%
OpenBookQA	—	82.6%
TriviaQA	—	67.7%

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3-8B: 31.0 (#256)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
LMArena Non-English	—	1098
LMArena Chinese	—	1076
LMArena French	—	1158
LMArena German	—	1104
LMArena Japanese	—	967
LMArena Korean	—	1003
LMArena Russian	—	1109
LMArena Spanish	—	1172

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3-8B: 58.7 (#255)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
LMArena Instruction Following	—	1127

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, Llama 3-8B: 34.2 (#246)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
LMArena Longer Query	—	1128

Writing & Preference DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 46.1 (#217), Llama 3-8B: 37.7 (#251)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Speciale	Llama 3-8B
LMArena Text	—	1166
LMArena Creative Writing	—	1150
EQ-Bench Creative Writing	1276	—
LMArena Multi-Turn	—	1152

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than Llama 3-8B?

Which is cheaper, DeepSeek-V3.2-Speciale or Llama 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.

Is DeepSeek-V3.2-Speciale or Llama 3-8B better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 39.9 versus 30.5 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 Llama 3-8B share?

0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Llama 3-8B has 34.