How much better is Llama 5 than Llama 4?

Llama 5 is a major step up. It moves to a 600B+ Mixture-of-Experts architecture from Llama 4's dense models, extends context from 256K to 5M tokens, adds recursive self-improvement, and claims frontier-class benchmarks matching GPT-5.4 and Claude Opus 4.6 — which Llama 4 did not.

Should I upgrade from Llama 4 to Llama 5?

Yes, for most use cases. Llama 5 is better on every benchmark and adds native agentic training. The main reason to stay on Llama 4 is hardware constraints — Llama 5's flagship variants need larger GPU clusters. If you can run Llama 5's distilled 70B variant, upgrade.

Is Llama 5 a drop-in replacement for Llama 4?

Mostly. The tokenizer and chat template changed slightly, so you'll need to update prompts. Fine-tunes from Llama 4 do not transfer — you'll need to re-fine-tune on Llama 5. API-level integrations work unchanged if you're using hosted providers.

Quick Answer

Llama 5 vs Llama 4: What Changed in Meta's New Model

Published: April 10, 2026

Llama 5 vs Llama 4: What Actually Changed

Meta released Llama 5 on April 8, 2026, replacing Llama 4 as the flagship open-weight model. Here’s every meaningful difference.

Last verified: April 10, 2026

Quick Comparison

Feature	Llama 4	Llama 5
Released	2025	April 8, 2026
Flagship size	405B dense	600B+ MoE
Architecture	Dense	Mixture-of-Experts
Context window	256K tokens	5M tokens
Training compute	H100 cluster	500K+ Blackwell B200
Modalities	Text, image	Text, image, video, audio
Native agents	Partial	Full
Recursive self-improvement	❌ No	✅ Yes
SWE-bench Verified	~62%	~74%
MMLU-Pro	~80%	~87%
License	Llama Community License	Llama Community License (updated)

Architecture Changes

Llama 4: Dense

Llama 4 used a dense transformer — every parameter activates for every token. 405B parameters meant 405B active every forward pass. Simple to serve, but expensive at scale.

Llama 5: Mixture-of-Experts (MoE)

Llama 5’s flagship has 600B+ total parameters, but only a fraction (likely ~40-80B, similar to DeepSeek V4’s design) activate per token. This makes inference 3-5x cheaper while allowing a much larger total parameter count.

Why it matters: You get more capability for the same inference cost, but serving MoE is more complex (needs expert routing, load balancing).

Context Window: 256K → 5M

Llama 4 capped at 256K tokens (enough for long documents, not for whole codebases). Llama 5 jumps to 5 million tokens — the longest of any frontier model in April 2026.

Practical implications:

Ingest an entire medium-sized repository in one prompt
Process book-length documents without chunking
Long-horizon agent trajectories without memory compression

Recursive Self-Improvement (New)

Llama 5 introduces the ability to refine its own internal logic and generate high-quality synthetic training data. Meta describes this as moving toward “System 2 thinking” — slow, deliberate reasoning.

Llama 4 had no equivalent. This is the biggest architectural innovation of Llama 5 and the main reason Meta claims it matches closed frontier models on hard reasoning benchmarks.

Native Agentic Training

Llama 4 could be fine-tuned for agent workflows, but agentic behavior wasn’t baked in. Llama 5 is trained from the start on tool use, planning, and multi-step execution. You get:

Better out-of-the-box function calling
Stronger multi-step planning without prompt scaffolding
Improved resistance to agent failure modes (loops, hallucinated tool calls)

Multimodal: Text+Image → Text+Image+Video+Audio

Llama 4 handled text and images. Llama 5 adds native video and audio input, matching GPT-5.4 and Gemini 3.1 Pro.

Benchmark Deltas

Benchmark	Llama 4	Llama 5	Delta
MMLU-Pro	~80%	~87%	+7 pts
SWE-bench Verified	~62%	~74%	+12 pts
AIME 2025	~70%	~88%	+18 pts
GPQA Diamond	~72%	~84%	+12 pts
HumanEval	~89%	~94%	+5 pts
LiveCodeBench	~52%	~68%	+16 pts

These are the largest generation-over-generation gains in Llama history — bigger than the Llama 3 → Llama 4 jump.

License Changes

Both use the Llama Community License, but Llama 5 updates a few terms:

Attribution: Still required (“Built with Llama”)
MAU cap: Still 700M for the separate-agreement trigger
Training other models: Llama 5 explicitly allows using outputs to train other models (Llama 4 was ambiguous)
Multimodal outputs: Covered under the same terms

Migration Notes

Tokenizer: Updated for Llama 5 — don’t mix Llama 4 and Llama 5 tokenizers
Chat template: Minor formatting changes; update your prompt wrappers
Fine-tunes: Llama 4 LoRAs and full fine-tunes do NOT transfer to Llama 5
Tool use format: Llama 5 uses a cleaner JSON-based tool schema
API clients: Hosted provider APIs (Together, Fireworks, Groq) accept a drop-in model name change

Should You Upgrade?

✅ Upgrade if:

You have hardware to run Llama 5 (even the 70B distilled variant)
You need longer context or better reasoning
You’re building agents
You want multimodal

⚠️ Stay on Llama 4 if:

Your hardware can’t handle Llama 5
You have production fine-tunes you can’t easily retrain
Your workload is simple enough that Llama 4 is “good enough”

For most teams, upgrade. The benchmark gains alone justify the migration effort.

Last verified: April 10, 2026