Model comparison

DeepSeek-V3.2-Exp vs Phi-4

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 31.2 on the Noometry Index. Phi-4 costs 3.3× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

Last verified October 10, 2026. 23 shared benchmarks.

DeepSeek-V3.2-Exp

DeepSeek

44.3

Rank #78 Confirmed

Phi-4

Microsoft

31.2

Rank #279 Confirmed

Summary

They share 23 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Phi-4 in 0 categories; 9 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-V3.2-Exp leads 62.4 to 40.5.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 13.8% for Phi-4.
Phi-4 is cheaper at $0.07 / $0.14 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3.2-Exp and Phi-4 specifications
	DeepSeek-V3.2-Exp	Phi-4
Provider	DeepSeek	Microsoft
Noometry Index	44.3	31.2
Released	2025-09-29	2024-12-11
Weights	Open	Open
Context window	164K	128K
Max output	66K	4K
Input $ / M tokens	$0.26	$0.07
Output $ / M tokens	$0.38	$0.14
Results tracked	49	37

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Category by category

Coding DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 46.5 (#65), Phi-4: 34.4 (#239)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
LMArena Coding	1454	1231
SWE-bench Verified (bash only)	70%	—
Aider Polyglot	74.2%	—
LMArena WebDev	1362	—
SWE-bench Multilingual	59%	—
SciCode	38.9%	—
WeirdML	39.5%	—
BigCodeBench Instruct	—	45.5%
LiveBench Coding	—	30.7%
BigCodeBench Complete	—	55.4%

Agentic & Tool Use DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 32.7 (#59), Phi-4: 22.8 (#128)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
Berkeley Function Calling Leaderboard	56.7%	28.8%
Terminal-Bench	39.6%	—
APEX-Agents	21.3%	—
TheAgentCompany	42.9%	—
BALROG	—	11.6%
Vending-Bench 2	1,034	—

Reasoning DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 22.1 (#208), Phi-4: 17.7 (#291)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
Chess Puzzles	14%	1%
LMArena Hard Prompts	1434	1220
Epoch Capabilities Index	146.27	130.42
ARC-AGI-2	4%	—
Kagi LLM Benchmark	52.2%	—
NYT Connections (extended)	36.7%	—
ARC-AGI-1	57%	—
CritPt	2.9%	—
Thematic Generalization	65%	—
LiveBench Reasoning	—	47.8%
DTBench	87.7%	—
LiveBench Data Analysis	—	45.2%
LMCA	29.1%	—
LiveBench	—	41.6%

Math DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 41.7 (#87), Phi-4: 20.8 (#285)

Math benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
OTIS Mock AIME 2024-2025	87.8%	13.8%
LMArena Math	1435	1246
MathArena Final-Answer Competitions	57.7%	—
ProofBench	8%	—
LiveBench Math	—	42%
MATH Level 5	—	64.9%
FrontierMath (Feb 2025 set)	22.1%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 51.7 (#66), Phi-4: 32.6 (#209)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
GPQA Diamond	83.4%	56.1%
Vectara Hallucination Rate	5.3%	3.7%
LMArena Expert	1436	1203
Confabulations	—	29.4%
MMLU	—	84.8%

Multilingual DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 52.2 (#90), Phi-4: 37.2 (#237)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
LMArena Non-English	1409	1197
LMArena Chinese	1461	1212
LMArena French	1433	1224
LMArena German	1440	1222
LMArena Japanese	1374	1158
LMArena Korean	1371	1151
LMArena Russian	1424	1209
LMArena Spanish	1440	1234

Instruction Following DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 74.5 (#93), Phi-4: 60.4 (#251)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
LMArena Instruction Following	1413	1201
LiveBench Instruction Following	—	58.4%

Long Context DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 47.6 (#16), Phi-4: 36.9 (#226)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
LMArena Longer Query	1428	1217
Fiction.LiveBench	83.3%	—
CL-bench	13.2%	—
CL-bench Life	9.5%	—

Writing & Preference DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 62.4 (#77), Phi-4: 40.5 (#244)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Exp	Phi-4
LMArena Text	1425	1217
LMArena Creative Writing	1403	1182
LMArena Multi-Turn	1427	1206
Short-Story Creative Writing	—	62.6%
EQ-Bench Creative Writing	1515	—
LiveBench Language	—	25.6%

Frequently asked questions

Is DeepSeek-V3.2-Exp better than Phi-4?

Which is cheaper, DeepSeek-V3.2-Exp or Phi-4?

Phi-4 is cheaper. It lists at $0.07 per million input tokens and $0.14 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

Is DeepSeek-V3.2-Exp or Phi-4 better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 34.4 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 128K.

How many benchmarks do DeepSeek-V3.2-Exp and Phi-4 share?

23 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Phi-4 has 37.