Phi 4 vs Qwen3.8 27B

Qwen3.8 27B leads llmrun Score 121.5 to 83.8; both fit 24 GB at Q4_K_M; Phi 4 is 1.8× faster on RTX 4090 (~69 vs ~38 tok/s).

Model A · Microsoft

Phi 4

83.8llmrun Score

14.7B · 16K context · MIT · Dec 2024

ChatMathCode

Model B · Alibaba

Qwen3.8 27B

121.5llmrun Score

27.8B · 256K context · Apache 2.0 · Aug 2026

VisionFunctions

Which should you choose?

Choose Phi 4 if you want…

1.8× faster on RTX 4090
~69 vs ~38 tok/s
Lighter on memory
9.5 vs 17.4 GB at Q4_K_M
Also does chat, math, code
Qwen3.8 27B doesn't list it

Choose Qwen3.8 27B if you want…

Higher llmrun Score
121.5 vs 83.8
Longer context window
256K vs 16K tokens
Also does vision, function-calling
Phi 4 doesn't list it
Newer release
Aug 2026 vs Dec 2024

On your hardware

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

MemoryPhi 4Qwen3.8 27B

8 GB

RTX 4060 · 8 GB tier

Phi 4

IQ2_XXS · ~37 tok/s

7.7 GB · up to 16Kfits

Qwen3.8 27B

Doesn't fit

smallest quant needs 8.8 GB

12 GB

RTX 3060 12GB · 12 GB tier

Phi 4

Q4_K_M · ~25 tok/stight

12.5 GB · up to 8K

Qwen3.8 27B

IQ3_XXS · ~20 tok/s

12 GB · up to 16Kmore headroom

16 GB

RTX 5060 Ti 16GB · 16 GB tier

Phi 4

Q4_K_M · ~31 tok/s

12.5 GB · up to 16Kmore headroom

Qwen3.8 27B

Q3_K_L · ~19 tok/s

15.4 GB · up to 16K

24 GBreference

RTX 4090 · 24 GB tier

Phi 4

Q4_K_M · ~69 tok/s

12.5 GB · up to 16Kfaster

Qwen3.8 27B

Q4_K_M · ~38 tok/s

17.9 GB · up to 64K

32 GB

RTX 5090 · 32 GB tier

Phi 4

Q4_K_M · ~122 tok/s

12.5 GB · up to 16Kfaster

Qwen3.8 27B

Q4_K_M · ~67 tok/s

17.9 GB · up to 256K

48 GB

RTX A6000 · 48 GB tier

Phi 4

Q4_K_M · ~52 tok/s

12.5 GB · up to 16Kfaster

Qwen3.8 27B

Q4_K_M · ~29 tok/s

17.9 GB · up to 256K

Mac 32 GB

Mac Mini M4 · Macs

Phi 4

Q4_K_M · ~9 tok/s

12.5 GB · up to 16Kfaster

Qwen3.8 27B

Q4_K_M · ~5 tok/s

17.9 GB · up to 128K

Mac 64 GB

Mac Studio M4 Max · Macs

Phi 4

Q4_K_M · ~40 tok/s

12.5 GB · up to 16Kfaster

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256K

Mac 128 GB

Mac Studio M4 Max · Macs

Phi 4

Q4_K_M · ~40 tok/s

12.5 GB · up to 16Kfaster

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256K

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 Qwen3.8 27B

Overall

Phi 4

83.879–89

Qwen3.8 27B

121.5119–124

Coding

Phi 4

no data

Qwen3.8 27B

112.1105–119

Math

Phi 4

no data

Qwen3.8 27B

117.5111–125

Science

Phi 4

no data

Qwen3.8 27B

112.093–131

Reasoning

Phi 4

no data

Qwen3.8 27B

124.1121–127

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

Qwen3.8 27B · #10 of 92 open models · #1 of 34 that fit 24 GB

Benchmarks

1 shared board: Phi 4 leads 0, Qwen3.8 27B leads 1 · Benchmarks imported Oct 2026

BoardPhi 4Qwen3.8 27B

LMArena Text

Phi 4

1256.3#127/186 open

ArenaLMArena · Oct 2026

Qwen3.8 27B

1438.1#22/186 open

ArenaLMArena · Oct 2026

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

Only Phi 4 (7)

Only Qwen3.8 27B (13)

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 4Qwen3.8 27B

Q2_K

Phi 4

7.0 GB5.5 GB file

32K — · 128K —

Qwen3.8 27B

12.6 GB10.8 GB file

32K 13.9 GB · 128K 19.3 GB

Q3_K_S

Phi 4

7.1 GB6.5 GB file

32K — · 128K —

Qwen3.8 27B

12.9 GB12.7 GB file

32K 14.2 GB · 128K 19.6 GB

Q3_K_M

Phi 4

7.9 GB7.4 GB file

32K — · 128K —

Qwen3.8 27B

14.3 GB13.4 GB file

32K 15.6 GB · 128K 21.0 GB

Q4_0

Phi 4

8.1 GB8.4 GB file

32K — · 128K —

Qwen3.8 27B

14.6 GB~13.9 GB file

32K 16.0 GB · 128K 21.4 GB

Q4_K_M

Phi 4

9.5 GB9.1 GB file

32K — · 128K —

Qwen3.8 27B

17.4 GB16.5 GB file

32K 18.8 GB · 128K 24.1 GB

Q5_K_M

Phi 4

11.2 GB10.6 GB file

32K — · 128K —

Qwen3.8 27B

20.5 GB19.8 GB file

32K 21.9 GB · 128K 27.3 GB

Q6_K

Phi 4

12.8 GB12.0 GB file

32K — · 128K —

Qwen3.8 27B

23.7 GB22.0 GB file

32K 25.0 GB · 128K 30.4 GB

Q8_0

Phi 4

15.4 GB15.6 GB file

32K — · 128K —

Qwen3.8 27B

28.5 GB29.0 GB file

32K 29.9 GB · 128K 35.2 GB

19 more quants

IQ2_XXS

Phi 4

4.8 GB4.1 GB file

32K — · 128K —

Qwen3.8 27B

8.4 GB7.3 GB file

32K 9.7 GB · 128K 15.1 GB

IQ2_XS

Phi 4

5.1 GB4.5 GB file

32K — · 128K —

Qwen3.8 27B

9.1 GB9.1 GB file

32K 10.4 GB · 128K 15.8 GB

IQ2_S

Phi 4

5.3 GB4.7 GB file

32K — · 128K —

Qwen3.8 27B

9.4 GB8.4 GB file

32K 10.8 GB · 128K 16.1 GB

IQ2_M

Phi 4

5.7 GB5.1 GB file

32K — · 128K —

Qwen3.8 27B

10.1 GB10.5 GB file

32K 11.5 GB · 128K 16.8 GB

IQ3_XXS

Phi 4

6.4 GB5.8 GB file

32K — · 128K —

Qwen3.8 27B

11.5 GB10.9 GB file

32K 12.9 GB · 128K 18.2 GB

Q2_K_S

Phi 4

6.6 GB5.2 GB file

32K — · 128K —

Qwen3.8 27B

not listed

IQ3_XS

Phi 4

6.8 GB6.2 GB file

32K — · 128K —

Qwen3.8 27B

12.2 GB10.4 GB file

32K 13.6 GB · 128K 18.9 GB

IQ3_S

Phi 4

7.0 GB6.5 GB file

32K — · 128K —

Qwen3.8 27B

12.6 GB12.0 GB file

32K 13.9 GB · 128K 19.3 GB

IQ3_M

Phi 4

7.3 GB6.9 GB file

32K — · 128K —

Qwen3.8 27B

13.3 GB14.9 GB file

32K 14.6 GB · 128K 20.0 GB

Q3_K_L

Phi 4

8.2 GB7.9 GB file

32K — · 128K —

Qwen3.8 27B

15.0 GB14.1 GB file

32K 16.3 GB · 128K 21.7 GB

IQ4_XS

Phi 4

8.6 GB7.9 GB file

32K — · 128K —

Qwen3.8 27B

15.7 GB14.3 GB file

32K 17.0 GB · 128K 22.4 GB

IQ4_NL

Phi 4

9.0 GB8.4 GB file

32K — · 128K —

Qwen3.8 27B

16.4 GB17.4 GB file

32K 17.7 GB · 128K 23.1 GB

Q4_1

Phi 4

9.0 GB9.3 GB file

32K — · 128K —

Qwen3.8 27B

16.4 GB17.5 GB file

32K 17.7 GB · 128K 23.1 GB

Q4_K_S

Phi 4

9.0 GB8.4 GB file

32K — · 128K —

Qwen3.8 27B

16.4 GB15.4 GB file

32K 17.7 GB · 128K 23.1 GB

Q4_K_L

Phi 4

9.7 GB9.4 GB file

32K — · 128K —

Qwen3.8 27B

17.8 GB18.8 GB file

32K 19.1 GB · 128K 24.5 GB

Q5_0

Phi 4

9.9 GB10.2 GB file

32K — · 128K —

Qwen3.8 27B

not listed

Q5_K_S

Phi 4

10.8 GB10.2 GB file

32K — · 128K —

Qwen3.8 27B

19.9 GB18.7 GB file

32K 21.2 GB · 128K 26.6 GB

Q5_K_L

Phi 4

11.4 GB10.9 GB file

32K — · 128K —

Qwen3.8 27B

20.9 GB21.5 GB file

32K 22.2 GB · 128K 27.6 GB

BF16

Phi 4

30.0 GB~29.3 GB file

32K — · 128K —

Qwen3.8 27B

56.3 GB54.7 GB file

32K 57.7 GB · 128K 63.0 GB

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 →

Qwen3.8 27B

Ollama · Q4_K_M

$ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M

All ways to run Qwen3.8 27B →

Specifications

Specifications of Phi 4 and Qwen3.8 27B
Spec	Phi 4	Qwen3.8 27B
Publisher	Microsoft	Alibaba
Parameters	14.7B	27.8B
Architecture	Phi3ForCausalLM	Qwen3_5ForConditionalGeneration
Context	16K tokens	256K tokens
Modalities	Chat, Math, Code	Vision, Function-calling
License	MIT	Apache 2.0
Released	Dec 2024	Aug 2026

Frequently Asked Questions

Which is better, Phi 4 or Qwen3.8 27B?

Qwen3.8 27B leads llmrun Score 121.5 to 83.8; both fit 24 GB at Q4_K_M; Phi 4 is 1.8× faster on RTX 4090 (~69 vs ~38 tok/s). Choose Phi 4 for 1.8× faster on RTX 4090 and lighter on memory; choose Qwen3.8 27B for higher llmrun Score and longer context window.

Which needs less VRAM, Phi 4 or Qwen3.8 27B?

At Q4_K_M, Phi 4 requires 9.5 GB while Qwen3.8 27B needs 17.4 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 38 tok/s for Qwen3.8 27B (Q4_K_M), so Phi 4 is faster. Estimates, ±20%.

Can I run Phi 4 or Qwen3.8 27B on a 16 GB GPU?

Phi 4 fits at Q4_K_M; Qwen3.8 27B fits at Q3_K_L.

Which has a longer context window, Phi 4 or Qwen3.8 27B?

Phi 4 supports 16,384 tokens and Qwen3.8 27B supports 262,144 tokens.