Mistral Nemo Instruct 2407 vs Qwen3.8 27B

Qwen3.8 27B leads llmrun Score 121.5 to 65.4; both fit 24 GB at Q4_K_M; Mistral Nemo Instruct 2407 is 2.1× faster on RTX 4090 (~81 vs ~38 tok/s).

Model A · Mistral AI

Mistral Nemo Instruct 2407

65.4llmrun Score

12.2B · 128K context · Apache 2.0 · Jul 2024

ChatFunctions

Model B · Alibaba

Qwen3.8 27B

121.5llmrun Score

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

VisionFunctions

Which should you choose?

Choose Mistral Nemo Instruct 2407 if you want…

2.1× faster on RTX 4090
~81 vs ~38 tok/s
Lighter on memory
8.1 vs 17.4 GB at Q4_K_M
Also does chat
Qwen3.8 27B doesn't list it

Choose Qwen3.8 27B if you want…

Higher llmrun Score
121.5 vs 65.4
Longer context window
256K vs 128K tokens
Also does vision
Mistral Nemo Instruct 2407 doesn't list it
Newer release
Aug 2026 vs Jul 2024

On your hardware

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

MemoryMistral Nemo Instruct 2407Qwen3.8 27B

8 GB

RTX 4060 · 8 GB tier

Mistral Nemo Instruct 2407

IQ2_M · ~37 tok/s

7.8 GB · up to 16Kfits

Qwen3.8 27B

Doesn't fit

smallest quant needs 8.8 GB

12 GB

RTX 3060 12GB · 12 GB tier

Mistral Nemo Instruct 2407

Q4_K_M · ~29 tok/s

11 GB · up to 16Kmore headroom

Qwen3.8 27B

IQ3_XXS · ~20 tok/s

12 GB · up to 16K

16 GB

RTX 5060 Ti 16GB · 16 GB tier

Mistral Nemo Instruct 2407

Q4_K_M · ~36 tok/s

11 GB · up to 32Kmore headroom

Qwen3.8 27B

Q3_K_L · ~19 tok/s

15.4 GB · up to 16K

24 GBreference

RTX 4090 · 24 GB tier

Mistral Nemo Instruct 2407

Q4_K_M · ~81 tok/s

11 GB · up to 64Kfaster

Qwen3.8 27B

Q4_K_M · ~38 tok/s

17.9 GB · up to 64K

32 GB

RTX 5090 · 32 GB tier

Mistral Nemo Instruct 2407

Q4_K_M · ~144 tok/s

11 GB · up to 64Kfaster

Qwen3.8 27B

Q4_K_M · ~67 tok/s

17.9 GB · up to 256K

48 GB

RTX A6000 · 48 GB tier

Mistral Nemo Instruct 2407

Q4_K_M · ~62 tok/s

11 GB · up to 128Kfaster

Qwen3.8 27B

Q4_K_M · ~29 tok/s

17.9 GB · up to 256K

Mac 32 GB

Mac Mini M4 · Macs

Mistral Nemo Instruct 2407

Q4_K_M · ~10 tok/s

11 GB · up to 64Kfaster

Qwen3.8 27B

Q4_K_M · ~5 tok/s

17.9 GB · up to 128K

Mac 64 GB

Mac Studio M4 Max · Macs

Mistral Nemo Instruct 2407

Q4_K_M · ~47 tok/s

11 GB · up to 128Kfaster

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256K

Mac 128 GB

Mac Studio M4 Max · Macs

Mistral Nemo Instruct 2407

Q4_K_M · ~47 tok/s

11 GB · up to 128Kfaster

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

Mistral Nemo Instruct 2407 Qwen3.8 27B

Overall

Mistral Nemo Instruct 2407

65.458–73

Qwen3.8 27B

121.5119–124

Coding

Mistral Nemo Instruct 2407

no data

Qwen3.8 27B

112.1105–119

Math

Mistral Nemo Instruct 2407

no data

Qwen3.8 27B

117.5111–125

Science

Mistral Nemo Instruct 2407

no data

Qwen3.8 27B

112.093–131

Reasoning

Mistral Nemo Instruct 2407

no data

Qwen3.8 27B

124.1121–127

Mistral Nemo Instruct 2407 · #84 of 92 open models · #27 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: Mistral Nemo Instruct 2407 leads 0, Qwen3.8 27B leads 1 · Benchmarks imported Oct 2026

BoardMistral Nemo Instruct 2407Qwen3.8 27B

DTBench

Mistral Nemo Instruct 2407

48.6%#63/68 open

Lab-reportedEpoch AI · Jul 2024

Qwen3.8 27B

88.0%#9/68 open

Lab-reportedEpoch AI · Aug 2026

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

Only Mistral Nemo Instruct 2407 (4)

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.

QuantMistral Nemo Instruct 2407Qwen3.8 27B

Q2_K

Mistral Nemo Instruct 2407

5.9 GB4.8 GB file

32K 12.2 GB · 128K 32.3 GB

Qwen3.8 27B

12.6 GB10.8 GB file

32K 13.9 GB · 128K 19.3 GB

Q3_K_S

Mistral Nemo Instruct 2407

6.1 GB5.5 GB file

32K 12.4 GB · 128K 32.5 GB

Qwen3.8 27B

12.9 GB12.7 GB file

32K 14.2 GB · 128K 19.6 GB

Q3_K_M

Mistral Nemo Instruct 2407

6.7 GB6.1 GB file

32K 13.0 GB · 128K 33.1 GB

Qwen3.8 27B

14.3 GB13.4 GB file

32K 15.6 GB · 128K 21.0 GB

Q4_0

Mistral Nemo Instruct 2407

6.8 GB7.1 GB file

32K 13.1 GB · 128K 33.3 GB

Qwen3.8 27B

14.6 GB~13.9 GB file

32K 16.0 GB · 128K 21.4 GB

Q4_K_M

Mistral Nemo Instruct 2407

8.1 GB7.5 GB file

32K 14.4 GB · 128K 34.5 GB

Qwen3.8 27B

17.4 GB16.5 GB file

32K 18.8 GB · 128K 24.1 GB

Q5_K_M

Mistral Nemo Instruct 2407

9.5 GB8.7 GB file

32K 15.7 GB · 128K 35.9 GB

Qwen3.8 27B

20.5 GB19.8 GB file

32K 21.9 GB · 128K 27.3 GB

Q6_K

Mistral Nemo Instruct 2407

10.8 GB10.1 GB file

32K 17.1 GB · 128K 37.2 GB

Qwen3.8 27B

23.7 GB22.0 GB file

32K 25.0 GB · 128K 30.4 GB

Q8_0

Mistral Nemo Instruct 2407

13.0 GB13.0 GB file

32K 19.3 GB · 128K 39.4 GB

Qwen3.8 27B

28.5 GB29.0 GB file

32K 29.9 GB · 128K 35.2 GB

18 more quants

IQ2_XXS

Mistral Nemo Instruct 2407

4.1 GB3.6 GB file

32K 10.4 GB · 128K 30.5 GB

Qwen3.8 27B

8.4 GB7.3 GB file

32K 9.7 GB · 128K 15.1 GB

IQ2_XS

Mistral Nemo Instruct 2407

4.4 GB3.9 GB file

32K 10.7 GB · 128K 30.8 GB

Qwen3.8 27B

9.1 GB9.1 GB file

32K 10.4 GB · 128K 15.8 GB

IQ2_S

Mistral Nemo Instruct 2407

4.6 GB4.1 GB file

32K 10.8 GB · 128K 31.0 GB

Qwen3.8 27B

9.4 GB8.4 GB file

32K 10.8 GB · 128K 16.1 GB

IQ2_M

Mistral Nemo Instruct 2407

4.9 GB4.4 GB file

32K 11.1 GB · 128K 31.3 GB

Qwen3.8 27B

10.1 GB10.5 GB file

32K 11.5 GB · 128K 16.8 GB

IQ3_XXS

Mistral Nemo Instruct 2407

5.5 GB4.9 GB file

32K 11.8 GB · 128K 31.9 GB

Qwen3.8 27B

11.5 GB10.9 GB file

32K 12.9 GB · 128K 18.2 GB

IQ3_XS

Mistral Nemo Instruct 2407

5.8 GB5.3 GB file

32K 12.1 GB · 128K 32.2 GB

Qwen3.8 27B

12.2 GB10.4 GB file

32K 13.6 GB · 128K 18.9 GB

IQ3_S

Mistral Nemo Instruct 2407

5.9 GB5.6 GB file

32K 12.2 GB · 128K 32.3 GB

Qwen3.8 27B

12.6 GB12.0 GB file

32K 13.9 GB · 128K 19.3 GB

IQ3_M

Mistral Nemo Instruct 2407

6.2 GB5.7 GB file

32K 12.5 GB · 128K 32.7 GB

Qwen3.8 27B

13.3 GB14.9 GB file

32K 14.6 GB · 128K 20.0 GB

Q3_K_L

Mistral Nemo Instruct 2407

7.0 GB6.6 GB file

32K 13.3 GB · 128K 33.4 GB

Qwen3.8 27B

15.0 GB14.1 GB file

32K 16.3 GB · 128K 21.7 GB

IQ4_XS

Mistral Nemo Instruct 2407

7.3 GB6.7 GB file

32K 13.6 GB · 128K 33.7 GB

Qwen3.8 27B

15.7 GB14.3 GB file

32K 17.0 GB · 128K 22.4 GB

IQ4_NL

Mistral Nemo Instruct 2407

not listed

Qwen3.8 27B

16.4 GB17.4 GB file

32K 17.7 GB · 128K 23.1 GB

Q4_1

Mistral Nemo Instruct 2407

not listed

Qwen3.8 27B

16.4 GB17.5 GB file

32K 17.7 GB · 128K 23.1 GB

Q4_K_S

Mistral Nemo Instruct 2407

7.6 GB7.1 GB file

32K 13.9 GB · 128K 34.0 GB

Qwen3.8 27B

16.4 GB15.4 GB file

32K 17.7 GB · 128K 23.1 GB

Q4_K_L

Mistral Nemo Instruct 2407

8.2 GB8.0 GB file

32K 14.5 GB · 128K 34.6 GB

Qwen3.8 27B

17.8 GB18.8 GB file

32K 19.1 GB · 128K 24.5 GB

Q5_0

Mistral Nemo Instruct 2407

8.4 GB8.5 GB file

32K 14.7 GB · 128K 34.8 GB

Qwen3.8 27B

not listed

Q5_K_S

Mistral Nemo Instruct 2407

9.1 GB8.5 GB file

32K 15.4 GB · 128K 35.6 GB

Qwen3.8 27B

19.9 GB18.7 GB file

32K 21.2 GB · 128K 26.6 GB

Q5_K_L

Mistral Nemo Instruct 2407

9.6 GB9.1 GB file

32K 15.9 GB · 128K 36.0 GB

Qwen3.8 27B

20.9 GB21.5 GB file

32K 22.2 GB · 128K 27.6 GB

BF16

Mistral Nemo Instruct 2407

25.2 GB~24.5 GB file

32K 31.5 GB · 128K 51.6 GB

Qwen3.8 27B

56.3 GB54.7 GB file

32K 57.7 GB · 128K 63.0 GB

How to run each

Mistral Nemo Instruct 2407

Ollama · Q4_K_M

$ollama run hf.co/MaziyarPanahi/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M

All ways to run Mistral Nemo Instruct 2407 →

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 Mistral Nemo Instruct 2407 and Qwen3.8 27B
Spec	Mistral Nemo Instruct 2407	Qwen3.8 27B
Publisher	Mistral AI	Alibaba
Parameters	12.2B	27.8B
Architecture	MistralForCausalLM	Qwen3_5ForConditionalGeneration
Context	128K tokens	256K tokens
Modalities	Chat, Function-calling	Vision, Function-calling
License	Apache 2.0	Apache 2.0
Released	Jul 2024	Aug 2026

Frequently Asked Questions

Which is better, Mistral Nemo Instruct 2407 or Qwen3.8 27B?

Qwen3.8 27B leads llmrun Score 121.5 to 65.4; both fit 24 GB at Q4_K_M; Mistral Nemo Instruct 2407 is 2.1× faster on RTX 4090 (~81 vs ~38 tok/s). Choose Mistral Nemo Instruct 2407 for 2.1× faster on RTX 4090 and lighter on memory; choose Qwen3.8 27B for higher llmrun Score and longer context window.

Which needs less VRAM, Mistral Nemo Instruct 2407 or Qwen3.8 27B?

At Q4_K_M, Mistral Nemo Instruct 2407 requires 8.1 GB while Qwen3.8 27B needs 17.4 GB, making Mistral Nemo Instruct 2407 the easier model to run on your local setup.

Which is faster on RTX 4090?

Estimated decode speed on RTX 4090 is about 81 tok/s for Mistral Nemo Instruct 2407 (Q4_K_M) and 38 tok/s for Qwen3.8 27B (Q4_K_M), so Mistral Nemo Instruct 2407 is faster. Estimates, ±20%.

Can I run Mistral Nemo Instruct 2407 or Qwen3.8 27B on a 16 GB GPU?

Mistral Nemo Instruct 2407 fits at Q4_K_M; Qwen3.8 27B fits at Q3_K_L.

Which has a longer context window, Mistral Nemo Instruct 2407 or Qwen3.8 27B?

Mistral Nemo Instruct 2407 supports 131,072 tokens and Qwen3.8 27B supports 262,144 tokens.