Phi-4 14B vs Mistral Nemo 12B Instruct
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Phi-4 14B | Mistral Nemo 12B Instruct |
|---|---|---|
| Parameters | 14B | 12B |
| Author | Microsoft | Mistral AI |
| License | MIT | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 9 GB | 7 GB |
| VRAM at Q5 | 11 GB | 9 GB |
| VRAM at Q8 | 16 GB | 13 GB |
| VRAM at FP16 | 28 GB | 24 GB |
| Use cases | chat, general, reasoning | chat, general, multilingual, fr |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About Phi-4 14B
Microsoft's Phi-4 14B, trained on ultra-curated synthetic data with a heavy STEM bias. The 14B reasoning leader at the end of 2024. Strengths: Top-tier 14B reasoning at release, MIT license, Strong math, science, and code performance, Tight, well-formatted outputs.
About Mistral Nemo 12B Instruct
Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage. Strengths: 128k context window, Strong European multilingual performance, Apache 2.0 license, Efficient Tekken tokenizer reduces token counts.
How they compare
Phi-4 14B comes from Microsoft and Mistral Nemo 12B Instruct from Mistral AI, they belong to the Phi and Mistral families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 14B vs 12B parameters, Phi-4 14B is the larger of the two. At Q4, Mistral Nemo 12B Instruct fits in about 7 GB of VRAM versus 9 GB for the other — a 2 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Phi-4 14B takes MMLU with 84.8 against 68 — a decisive 16.8-point margin. For workloads weighted toward that benchmark, Phi-4 14B is the stronger default.
On a typical mid-range GPU, Mistral Nemo 12B Instruct pushes roughly 25 tokens/sec versus 20, so it is the more responsive choice for interactive or high-volume use. For long-context work, Mistral Nemo 12B Instruct offers the bigger window (125k vs 16k tokens).
Memory, quantization & throughput
Across quantization levels, Phi-4 14B requires Q4 ≈ 9 GB, Q5 ≈ 11 GB, Q8 ≈ 16 GB, FP16 ≈ 28 GB, while Mistral Nemo 12B Instruct requires Q4 ≈ 7 GB, Q5 ≈ 9 GB, Q8 ≈ 13 GB, FP16 ≈ 24 GB. In practice Phi-4 14B needs a 12 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Phi-4 14B needs roughly 16 GB of system RAM to run on CPU and Mistral Nemo 12B Instruct about 16 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 20 tokens/sec from Phi-4 14B and 25 from Mistral Nemo 12B Instruct, scaling up to 55 and 70 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Phi-4 14B or Mistral Nemo 12B Instruct to the card you actually own:
- On a 8 GB GPU: Phi-4 14B does not fit; Mistral Nemo 12B Instruct runs at Q4 (7 GB).
- On a 12 GB GPU: Phi-4 14B runs at Q5 (11 GB); Mistral Nemo 12B Instruct runs at Q5 (9 GB).
- On a 16 GB GPU: Phi-4 14B runs at Q8 (16 GB); Mistral Nemo 12B Instruct runs at Q8 (13 GB).
- On a 24 GB GPU: Phi-4 14B runs at Q8 (16 GB); Mistral Nemo 12B Instruct runs at FP16 (24 GB).
Benchmark scores
Reported benchmarks for Phi-4 14B: MMLU 84.8, MATH 80.4, HumanEval 82.6.
Reported benchmarks for Mistral Nemo 12B Instruct: MMLU 68, HellaSwag 83.5, Winogrande 76.8.
Bottom line: which should you pick?
- Pick Mistral Nemo 12B Instruct for long-context work (up to 125k tokens).
- Pick Mistral Nemo 12B Instruct for lower VRAM and faster inference; pick Phi-4 14B for maximum headline quality.
- Pick Phi-4 14B if MMLU performance is your priority (84.8 vs 68).
- Pick Phi-4 14B if your workload is reasoning.
- Pick Mistral Nemo 12B Instruct if your workload is fr, multilingual.
Which GPU should you buy to run Phi-4 14B?
To run Phi-4 14B locally at Q4, you need ~9 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Frequently asked questions
What is the difference between Phi-4 14B and Mistral Nemo 12B Instruct?
The headline differences: Phi-4 14B is a 14B model and Mistral Nemo 12B Instruct is 12B; their context windows differ (16k vs 125k tokens); they ship under different licenses (MIT vs Apache 2.0). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Phi-4 14B and Mistral Nemo 12B Instruct run on a 24 GB GPU?
At a Q4 quantization, Phi-4 14B needs about 9 GB of VRAM and fits comfortably on a 24 GB GPU; Mistral Nemo 12B Instruct needs about 7 GB and fits comfortably on a 24 GB GPU. Mistral Nemo 12B Instruct is the lighter option for tight VRAM budgets.
Is Phi-4 14B or Mistral Nemo 12B Instruct more capable?
On MMLU, Phi-4 14B scores higher (84.8 vs 68), a 16.8-point advantage on this benchmark.
Which is faster, Phi-4 14B or Mistral Nemo 12B Instruct?
Mistral Nemo 12B Instruct is the smaller model (12B vs 14B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Phi-4 14B and Mistral Nemo 12B Instruct use?
Phi-4 14B is licensed under MIT and Mistral Nemo 12B Instruct under Apache 2.0.
Which has the longer context window, Phi-4 14B or Mistral Nemo 12B Instruct?
Mistral Nemo 12B Instruct has the larger context window (125k vs 16k tokens), so it handles longer documents and codebases in a single prompt.