Phi 4 vs Qwen2.5 32B Instruct

Phi 4 leads llmrun Score 83.8 to 80.4 (ranges overlap); both fit 24 GB at Q4_K_M; Phi 4 is 2.2× faster on RTX 4090 (~69 vs ~32 tok/s).

Model A · Microsoft

Phi 4

83.8llmrun Score

14.7B · 16K context · MIT · Dec 2024

ChatMathCode

Model B · Alibaba

Qwen2.5 32B Instruct

80.4llmrun Score

32.8B · 32K context · Apache 2.0 · Sep 2024

ChatFunctions

Which should you choose?

Choose Phi 4 if you want…

Higher llmrun Score
83.8 vs 80.4, ranges overlap
Ahead on the benchmarks
5 of 5 shared boards
Fits a 12 GB GPU
Q4_K_M · 12.5 GB; Qwen2.5 32B Instruct needs 13.6 GB at its smallest quant
2.2× faster on RTX 4090
~69 vs ~32 tok/s
Lighter on memory
9.5 vs 20.5 GB at Q4_K_M
Also does math, code
Qwen2.5 32B Instruct doesn't list it

Choose Qwen2.5 32B Instruct if you want…

Longer context window
32K vs 16K tokens
Also does function-calling
Phi 4 doesn't list it

On your hardware

Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.

MemoryPhi 4Qwen2.5 32B Instruct

8 GB

RTX 4060 · 8 GB tier

Phi 4

IQ2_XXS · ~37 tok/s

7.7 GB · up to 16Kfits

Qwen2.5 32B Instruct

Doesn't fit

smallest quant needs 13.6 GB

12 GB

RTX 3060 12GB · 12 GB tier

Phi 4

Q4_K_M · ~25 tok/stight

12.5 GB · up to 8Kfits

Qwen2.5 32B Instruct

Doesn't fit

smallest quant needs 13.6 GB

16 GB

RTX 5060 Ti 16GB · 16 GB tier

Phi 4

Q4_K_M · ~31 tok/s

12.5 GB · up to 16Kmore headroom

Qwen2.5 32B Instruct

IQ2_M · ~25 tok/s

15.7 GB · up to 16K

24 GBreference

RTX 4090 · 24 GB tier

Phi 4

Q4_K_M · ~69 tok/s

12.5 GB · up to 16Kmore headroom

Qwen2.5 32B Instruct

Q4_K_M · ~32 tok/stight

24.3 GB · up to 8K

32 GB

RTX 5090 · 32 GB tier

Phi 4

Q4_K_M · ~122 tok/s

12.5 GB · up to 16Kfaster

Qwen2.5 32B Instruct

Q4_K_M · ~57 tok/s

24.3 GB · up to 32K

48 GB

RTX A6000 · 48 GB tier

Phi 4

Q4_K_M · ~52 tok/s

12.5 GB · up to 16Kfaster

Qwen2.5 32B Instruct

Q4_K_M · ~24 tok/s

24.3 GB · up to 32K

Mac 32 GB

Mac Mini M4 · Macs

Phi 4

Q4_K_M · ~9 tok/s

12.5 GB · up to 16Kfaster

Qwen2.5 32B Instruct

Q4_K_M · ~4 tok/s

24.3 GB · up to 16K

Mac 64 GB

Mac Studio M4 Max · Macs

Phi 4

Q4_K_M · ~40 tok/s

12.5 GB · up to 16Kfaster

Qwen2.5 32B Instruct

Q4_K_M · ~19 tok/s

24.3 GB · up to 32K

Mac 128 GB

Mac Studio M4 Max · Macs

Phi 4

Q4_K_M · ~40 tok/s

12.5 GB · up to 16Kfaster

Qwen2.5 32B Instruct

Q4_K_M · ~19 tok/s

24.3 GB · up to 32K

Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).

llmrun Score

with its 90% range · bars share one scale

Phi 4 Qwen2.5 32B Instruct

Overall

Phi 4

83.879–89

Qwen2.5 32B Instruct

80.475–86

Phi 4 · #70 of 92 open models · #22 of 34 that fit 24 GB

Qwen2.5 32B Instruct · #76 of 92 open models · #23 of 34 that fit 24 GB

Benchmarks

5 shared boards: Phi 4 leads 5, Qwen2.5 32B Instruct leads 0 · Benchmarks imported Oct 2026

BoardPhi 4Qwen2.5 32B Instruct

GPQA Diamond

Phi 4

56.1%#45/97 open

IndependentEpoch AI · Dec 2024

Qwen2.5 32B Instruct

46.1%#62/97 open

IndependentEpoch AI · Sep 2024

AIME 2024/2025

Phi 4

13.8%#56/85 open

IndependentEpoch AI · Dec 2024

Qwen2.5 32B Instruct

7.4%#64/85 open

IndependentEpoch AI · Sep 2024

MMLU-Pro

Phi 4

70.4%#37/144 open

IndependentTIGER-Lab

Qwen2.5 32B Instruct

69.2%#39/144 open

IndependentTIGER-Lab

MATH Level 5

Phi 4

64.9%#9/39 open

IndependentEpoch AI · Dec 2024

Qwen2.5 32B Instruct

56.1%#13/39 open

IndependentEpoch AI · Sep 2024

Chess Puzzles

Phi 4

1.0%#42/67 open

IndependentEpoch AI · Dec 2024

Qwen2.5 32B Instruct

0.0%#63/67 open

IndependentEpoch AI · Sep 2024

3 more boards (other shared and single-model boards)

Only Phi 4 (3)

LMArena Text1256.3
MMLU84.8%
BALROG11.6%

VRAM by quantization

Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.

QuantPhi 4Qwen2.5 32B Instruct

Q2_K

Phi 4

7.0 GB5.5 GB file

32K — · 128K —

Qwen2.5 32B Instruct

14.8 GB12.3 GB file

32K 22.8 GB · 128K —

Q3_K_S

Phi 4

7.1 GB6.5 GB file

32K — · 128K —

Qwen2.5 32B Instruct

15.2 GB14.4 GB file

32K 23.2 GB · 128K —

Q3_K_M

Phi 4

7.9 GB7.4 GB file

32K — · 128K —

Qwen2.5 32B Instruct

16.8 GB15.9 GB file

32K 24.9 GB · 128K —

Q4_0

Phi 4

8.1 GB8.4 GB file

32K — · 128K —

Qwen2.5 32B Instruct

17.2 GB18.6 GB file

32K 25.3 GB · 128K —

Q4_K_M

Phi 4

9.5 GB9.1 GB file

32K — · 128K —

Qwen2.5 32B Instruct

20.5 GB19.9 GB file

32K 28.6 GB · 128K —

Q5_K_M

Phi 4

11.2 GB10.6 GB file

32K — · 128K —

Qwen2.5 32B Instruct

24.2 GB23.3 GB file

32K 32.2 GB · 128K —

Q6_K

Phi 4

12.8 GB12.0 GB file

32K — · 128K —

Qwen2.5 32B Instruct

27.9 GB26.9 GB file

32K 35.9 GB · 128K —

Q8_0

Phi 4

15.4 GB15.6 GB file

32K — · 128K —

Qwen2.5 32B Instruct

33.6 GB34.8 GB file

32K 41.7 GB · 128K —

19 more quants

IQ2_XXS

Phi 4

4.8 GB4.1 GB file

32K — · 128K —

Qwen2.5 32B Instruct

9.9 GB9.0 GB file

32K 17.9 GB · 128K —

IQ2_XS

Phi 4

5.1 GB4.5 GB file

32K — · 128K —

Qwen2.5 32B Instruct

10.7 GB10.0 GB file

32K 18.7 GB · 128K —

IQ2_S

Phi 4

5.3 GB4.7 GB file

32K — · 128K —

Qwen2.5 32B Instruct

11.1 GB10.4 GB file

32K 19.1 GB · 128K —

IQ2_M

Phi 4

5.7 GB5.1 GB file

32K — · 128K —

Qwen2.5 32B Instruct

11.9 GB11.3 GB file

32K 19.9 GB · 128K —

IQ3_XXS

Phi 4

6.4 GB5.8 GB file

32K — · 128K —

Qwen2.5 32B Instruct

13.5 GB12.8 GB file

32K 21.6 GB · 128K —

Q2_K_S

Phi 4

6.6 GB5.2 GB file

32K — · 128K —

Qwen2.5 32B Instruct

13.9 GB11.5 GB file

32K 22.0 GB · 128K —

IQ3_XS

Phi 4

6.8 GB6.2 GB file

32K — · 128K —

Qwen2.5 32B Instruct

14.4 GB13.7 GB file

32K 22.4 GB · 128K —

IQ3_S

Phi 4

7.0 GB6.5 GB file

32K — · 128K —

Qwen2.5 32B Instruct

14.8 GB14.4 GB file

32K 22.8 GB · 128K —

IQ3_M

Phi 4

7.3 GB6.9 GB file

32K — · 128K —

Qwen2.5 32B Instruct

15.6 GB14.8 GB file

32K 23.6 GB · 128K —

Q3_K_L

Phi 4

8.2 GB7.9 GB file

32K — · 128K —

Qwen2.5 32B Instruct

17.6 GB17.2 GB file

32K 25.7 GB · 128K —

IQ4_XS

Phi 4

8.6 GB7.9 GB file

32K — · 128K —

Qwen2.5 32B Instruct

18.5 GB17.7 GB file

32K 26.5 GB · 128K —

IQ4_NL

Phi 4

9.0 GB8.4 GB file

32K — · 128K —

Qwen2.5 32B Instruct

not listed

Q4_1

Phi 4

9.0 GB9.3 GB file

32K — · 128K —

Qwen2.5 32B Instruct

19.3 GB20.6 GB file

32K 27.3 GB · 128K —

Q4_K_S

Phi 4

9.0 GB8.4 GB file

32K — · 128K —

Qwen2.5 32B Instruct

19.3 GB18.8 GB file

32K 27.3 GB · 128K —

Q4_K_L

Phi 4

9.7 GB9.4 GB file

32K — · 128K —

Qwen2.5 32B Instruct

20.9 GB20.4 GB file

32K 29.0 GB · 128K —

Q5_0

Phi 4

9.9 GB10.2 GB file

32K — · 128K —

Qwen2.5 32B Instruct

21.3 GB22.6 GB file

32K 29.4 GB · 128K —

Q5_K_S

Phi 4

10.8 GB10.2 GB file

32K — · 128K —

Qwen2.5 32B Instruct

23.4 GB22.6 GB file

32K 31.4 GB · 128K —

Q5_K_L

Phi 4

11.4 GB10.9 GB file

32K — · 128K —

Qwen2.5 32B Instruct

24.6 GB23.7 GB file

32K 32.6 GB · 128K —

BF16

Phi 4

30.0 GB~29.3 GB file

32K — · 128K —

Qwen2.5 32B Instruct

66.4 GB~65.5 GB file

32K 74.4 GB · 128K —

How to run each

Phi 4

Ollama · Q4_K_M

$ollama run hf.co/MaziyarPanahi/phi-4-GGUF:Q4_K_M

All ways to run Phi 4 →

Qwen2.5 32B Instruct

Ollama · Q4_K_M

$ollama run hf.co/bartowski/Qwen2.5-32B-Instruct-GGUF:Q4_K_M

All ways to run Qwen2.5 32B Instruct →

Specifications

Specifications of Phi 4 and Qwen2.5 32B Instruct
Spec	Phi 4	Qwen2.5 32B Instruct
Publisher	Microsoft	Alibaba
Parameters	14.7B	32.8B
Architecture	Phi3ForCausalLM	Qwen2ForCausalLM
Context	16K tokens	32K tokens
Modalities	Chat, Math, Code	Chat, Function-calling
License	MIT	Apache 2.0
Released	Dec 2024	Sep 2024

Frequently Asked Questions

Which is better, Phi 4 or Qwen2.5 32B Instruct?

Phi 4 leads llmrun Score 83.8 to 80.4 (ranges overlap); both fit 24 GB at Q4_K_M; Phi 4 is 2.2× faster on RTX 4090 (~69 vs ~32 tok/s). Choose Phi 4 for higher llmrun Score and ahead on the benchmarks; choose Qwen2.5 32B Instruct for longer context window and also does function-calling.

Which needs less VRAM, Phi 4 or Qwen2.5 32B Instruct?

At Q4_K_M, Phi 4 requires 9.5 GB while Qwen2.5 32B Instruct needs 20.5 GB, making Phi 4 the easier model to run on your local setup.

Which is faster on RTX 4090?

Estimated decode speed on RTX 4090 is about 69 tok/s for Phi 4 (Q4_K_M) and 32 tok/s for Qwen2.5 32B Instruct (Q4_K_M), so Phi 4 is faster. Estimates, ±20%.

Can I run Phi 4 or Qwen2.5 32B Instruct on a 16 GB GPU?

Phi 4 fits at Q4_K_M; Qwen2.5 32B Instruct fits at IQ2_M.

Which has a longer context window, Phi 4 or Qwen2.5 32B Instruct?

Phi 4 supports 16,384 tokens and Qwen2.5 32B Instruct supports 32,768 tokens.