Guide · 2026-06-17

Phi-4 14B vs Llama 3.1 8B — Reasoning Benchmark Head-to-Head

Last updated 2026-06-17

We benchmarked Microsoft's Phi-4 14B against Meta's Llama 3.1 8B on math, code, and logic. One model wins reasoning by double digits — the other wins everywhere else.

By Mohamed Meguedmi · 11 min read

Why this matchup matters in 2026

Microsoft released Phi-4 in December 2024 with a single, unambiguous claim: a 14B-parameter dense model can match or beat models 5× its size on reasoning. Meta's Llama 3.1 8B Instruct, released six months earlier, remains the default 8B baseline for almost every open-weights project — it is the model fine-tuners ship against and the one Ollama installs reach for first.

The two models are not in the same weight class, but they are in the same deployment class: both target the single-consumer-GPU tier where you trade a few VRAM gigabytes for a substantial capability shift. The question is whether Phi-4's reasoning premium is worth the larger footprint, the smaller context window, and the thinner ecosystem.

We've run both models through identical benchmark harnesses and identical hardware to find out. The short answer: it depends on the workload, and the gap is bigger than the SERP suggests.

Model specs and architecture

Phi-4 is a dense decoder-only transformer trained on roughly 9.8T tokens, with an unusually heavy emphasis on synthetic data — about 40% of pretraining came from curated synthetic problem-solution pairs targeting math, reasoning, and code. Llama 3.1 8B was trained on 15T tokens of mostly web-scraped natural text, with reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) on the instruct variant.

Spec	Phi-4 14B	Llama 3.1 8B Instruct
Parameters	14.7B	8.03B
Architecture	Dense transformer	Dense transformer (GQA)
Context window	16,384 tokens	131,072 tokens
Vocabulary size	100,352 (tiktoken)	128,256
Training tokens	~9.8T (heavy synthetic)	~15T (mostly natural)
License	MIT	Llama 3.1 Community
Release date	Dec 2024	Jul 2024
Tokenizer	cl100k (OpenAI-style)	Tiktoken-derived Llama 3

Two architectural details matter for daily use. First, Llama 3.1 ships with grouped-query attention (GQA) tuned for a 128k context — Phi-4's 16k ceiling will not stretch politely past that, and rope-scaling attempts degrade quality fast. Second, Phi-4 uses an OpenAI-compatible tokenizer, which produces ~5% fewer tokens than Llama 3 on English text but is meaningfully worse on non-Latin scripts.

Reasoning benchmarks: where Phi-4 earns its keep

We pulled official scores from Microsoft's Phi-4 technical report and Meta's published evaluations, then cross-checked with our own runs using the lm-evaluation-harness at 5-shot for MMLU/GPQA and 0-shot CoT for MATH/HumanEval. Numbers below are taken from BF16 weights — Q4_K_M quantization shaves 1-3 points off most scores symmetrically, so the deltas hold.

Benchmark	What it measures	Phi-4 14B	Llama 3.1 8B	Δ
MMLU (5-shot)	General knowledge, 57 subjects	84.8	73.0	+11.8
GPQA Diamond	Graduate-level science Q&A	56.1	39.7	+16.4
MATH	Competition math	80.4	56.5	+23.9
HumanEval	Python code generation	82.6	72.6	+10.0
MGSM (avg)	Multilingual grade-school math	80.6	68.9	+11.7
DROP	Reading + discrete reasoning	75.5	69.4	+6.1
IFEval	Instruction following	63.0	78.6	-15.6
SimpleQA	Factual recall (lower hallucination = higher)	3.0	16.4	-13.4

Read those last two rows carefully. Phi-4 dominates anything that smells like a structured reasoning problem, but it loses on instruction following and is dramatically worse at factual recall. This is the synthetic-training tradeoff in plain sight: the model learned how to think through math problems brilliantly and how to remember world facts poorly. If your application is "answer trivia about the world," Llama 3.1 8B is still the better pick.

For more on how we structure these comparisons, see our methodology page.

Hardware footprint and inference speed

We measured both models on a single RTX 4070 Ti Super (16 GB VRAM) under Ollama 0.5.x with the default Q4_K_M quantizations. Each run used 2k input tokens and 512 output tokens, averaged over 20 prompts.

Quant	Model	File size	VRAM at idle	Tokens/sec (gen)	Time-to-first-token
Q4_K_M	Llama 3.1 8B	4.9 GB	5.5 GB	112 tok/s	118 ms
Q4_K_M	Phi-4 14B	8.4 GB	9.1 GB	74 tok/s	164 ms
Q8_0	Llama 3.1 8B	8.5 GB	9.2 GB	87 tok/s	132 ms
Q8_0	Phi-4 14B	15.6 GB	15.9 GB	41 tok/s	221 ms
BF16	Llama 3.1 8B	16.1 GB	16.6 GB	spills to RAM	—
BF16	Phi-4 14B	29.4 GB	not feasible	n/a on 16 GB	n/a

Three practical takeaways. First, an 8 GB consumer GPU (RTX 3060 Ti, 4060) handles Llama 3.1 8B Q4_K_M comfortably; Phi-4 14B Q4_K_M needs at least 12 GB to stay fully on-GPU. Second, Phi-4 is roughly 35% slower per token at matched quantization — a real cost for chat applications where latency dominates UX. Third, if you want Q8_0 quality on Phi-4 you need 16 GB minimum, which pushes you into RTX 4060 Ti 16 GB / 4070 Super territory. Use our cost calculator to estimate the electricity and amortized GPU cost for your specific token volume.

Context window: the 16k vs 128k gap

This is the single most underappreciated difference between these models in the SERP. Phi-4's 16,384-token context is fine for chat and short Q&A, but it falls apart for three common modern workloads:

• Long-document RAG: a single 30-page PDF in chunks plus retrieved context plus a system prompt will routinely break 16k. Llama 3.1 8B's 128k context handles it without partitioning.
• Codebase-aware agents: pasting in a directory of source files for refactoring is a non-starter on Phi-4.
• Multi-turn conversations: after 20-30 substantive turns Phi-4 starts truncating the system prompt unless you actively manage the buffer.

Rope-scaling Phi-4 to 32k has been attempted by the community and produces measurably worse outputs past ~20k tokens. For long-context use, treat 16k as a hard ceiling.

Ecosystem, fine-tunes, and licensing

Llama 3.1 8B has a community of thousands of derivatives — abliterated variants, domain fine-tunes (medical, legal, finance), uncensored chat variants, multilingual extensions, and tool-use specialists. Phi-4's ecosystem is, by comparison, sparse: as of mid-2026 there are perhaps a few dozen serious community fine-tunes, mostly focused on reasoning specialization rather than domain adaptation.

Licensing is a tilt in Phi-4's favor. The MIT license is genuinely permissive — you can ship Phi-4 in commercial closed-source products with zero acceptable-use restrictions. Llama 3.1's Community License is permissive in practice but has the famous 700M-monthly-active-users clause and an acceptable use policy that legal teams at large enterprises sometimes flag.

For the full catalog of models we benchmark, see the model catalog. We also publish the underlying scores via the BestLLMfor public API (CC BY 4.0) and an open-source MCP server, so you can pull the same numbers used in this article into your own dashboards or agents.

How to run each model locally with Ollama

Both models are one command away on a modern Linux, macOS, or Windows machine with sufficient VRAM.

# Install Ollama (Linux/macOS one-liner)
curl -fsSL https://ollama.com/install.sh | sh

# Pull Llama 3.1 8B Instruct (Q4_K_M, ~4.9 GB)
ollama pull llama3.1:8b-instruct-q4_K_M

# Pull Phi-4 14B (Q4_K_M, ~8.4 GB)
ollama pull phi4:14b-q4_K_M

# Quick reasoning test
ollama run phi4:14b-q4_K_M "A bat and ball cost $1.10 total. The bat costs $1 more than the ball. How much does the ball cost? Think step by step."
ollama run llama3.1:8b-instruct-q4_K_M "A bat and ball cost $1.10 total. The bat costs $1 more than the ball. How much does the ball cost? Think step by step."

On a fresh install, Phi-4 reliably answers "5 cents" with correct algebraic working. Llama 3.1 8B answers correctly more often than the chat-tuned 7B generation but still slips into the intuitive "10 cents" trap in roughly 1 in 5 runs at default temperature. That's the reasoning gap in a single prompt.

Pricing and cost-per-million-tokens on hosted endpoints

If you're not running locally, both models are widely available on hosted inference platforms. Pricing as of mid-2026:

Provider	Model	Input ($/1M tok)	Output ($/1M tok)
Together AI	Llama 3.1 8B Instruct Turbo	$0.18	$0.18
Fireworks	Llama 3.1 8B Instruct	$0.20	$0.20
DeepInfra	Llama 3.1 8B Instruct	$0.04	$0.04
DeepInfra	Phi-4 14B	$0.07	$0.14
Azure AI Foundry	Phi-4 14B	$0.125	$0.50

Llama 3.1 8B is roughly 2-4× cheaper than Phi-4 across providers, driven both by the smaller parameter count and by intense provider competition. For high-throughput, low-margin workloads — chat front ends, classification, RAG retrieval reranking — that price gap usually dominates the reasoning gap.

Verdict: which one should you actually run?

Your workload	Pick	Why
Math tutoring, science Q&A, STEM assistant	Phi-4 14B	+24 pts on MATH, +16 on GPQA — not close.
Coding assistant (Python, short snippets)	Phi-4 14B	+10 pts HumanEval, better single-shot accuracy.
Long-document RAG, codebase agents	Llama 3.1 8B	128k context vs 16k. Non-negotiable.
Multi-turn chatbots, customer support	Llama 3.1 8B	Better IFEval, lower latency, cheaper to serve.
Trivia, factual recall, world knowledge	Llama 3.1 8B	Phi-4 hallucinates badly (SimpleQA 3.0 vs 16.4).
Multilingual (non-English) generation	Llama 3.1 8B	Better tokenizer + broader natural training mix.
Fine-tuning for a custom domain	Llama 3.1 8B	Larger ecosystem, more LoRA examples, faster iteration.
Commercial product with strict license review	Phi-4 14B	MIT license, no AUP friction.
8 GB VRAM GPU (RTX 3060 Ti, 4060)	Llama 3.1 8B	Phi-4 Q4 spills past 8 GB.

If we had to pick one model to keep on a 16 GB GPU for general daily use across mixed workloads, we'd still pick Llama 3.1 8B Instruct — its context window, latency, and ecosystem make it the better default. Phi-4 14B earns a permanent slot too, but as a specialist you reach for when reasoning quality is the bottleneck. For more head-to-head matchups, see our best local LLMs guide, and learn more about how the team picks targets on the about page.