Meta released Llama 4 (Scout, Maverick, Behemoth) in late 2025 with mixture-of-experts architecture and 10M-token context. Llama 3 and 3.3 are still in heavy production at thousands of companies. Whether to upgrade depends on whether your workload actually needs what 4 brings — here is the honest analysis.
What changed in 2026
- MoE architecture. Llama 4 Maverick is 400B total / 17B active. Llama 4 Scout is 109B total / 17B active. Inference cost is closer to a 17B dense model, quality is closer to a 70B+.
- Context up to 10M tokens (Scout). A real differentiator for codebase, video transcript, and document corpora work.
- Native multimodal at the base layer. Image inputs are first-class on Maverick and Behemoth.
Quality: the benchmark gap
| Benchmark |
Llama 3.3 70B |
Llama 4 Scout |
Llama 4 Maverick |
| MMLU |
86.0 |
87.5 |
89.0 |
| HumanEval |
80.5 |
84.0 |
86.5 |
| MATH |
70.2 |
75.0 |
78.5 |
| GPQA |
47.0 |
53.0 |
56.0 |
| Long context recall (1M) |
n/a |
92% |
94% |
In practice: Maverick beats 3.3 70B on every workload we've measured, with the largest gaps on math, coding, and long context. Scout is closer to a side-grade for short-context work, but unlocks new workloads at long context.
Inference cost in production
On AWS Bedrock or Fireworks, Llama 4 Maverick runs roughly $0.85/M input and $2.50/M output — competitive with Mistral Large 3 and meaningfully cheaper than GPT-5/Opus. Scout is cheaper still at $0.50/M input. Self-hosted, you need a single 8xH100 node for Maverick at moderate batch — capex amortizes below $1/M tokens for steady workloads above 5M tokens/day.
Fine-tuning
LoRA and QLoRA on Llama 4 work as expected. Full SFT on Maverick requires multi-node training and is non-trivial — most teams will fine-tune Scout instead. Hugging Face TRL, Axolotl, and Unsloth all have first-class Llama 4 support as of Q1 2026. We fine-tuned Scout on 80K customer support tickets and saw a 12-point lift in accuracy vs base, similar to the lift seen on Llama 3.3.
When to upgrade
Upgrade now if: you need long context (>200K), your workload is reasoning- or math-heavy, you're already running on hosted inference (cost change is small), or you're starting a new project.
Hold on Llama 3.3 if: you have a stable fine-tune in production, your latency budget is tight (Llama 4 TTFT is 50-100ms longer in some configurations), or your prompts are short and the quality gap is small for your specific task.
Ecosystem in 2026
Hugging Face has 12K+ Llama 4 fine-tunes published. AWS, Azure, GCP, Together, Fireworks, and Replicate all serve Llama 4 with managed APIs. The model is on Bedrock with VPC isolation, which closes a major gap for regulated industries that previously had to self-host.
Behemoth — should you care?
Llama 4 Behemoth (2T total params) is positioned as a "teacher" for distillation, not a directly servable model in most production setups. Unless you're a research lab or a hyperscaler, you'll see Behemoth's gains indirectly through distilled variants like the next Llama 4 Scout-Pro.
FAQ
Does Llama 4 work with vLLM?
Yes. vLLM 0.7+ supports Llama 4 Scout and Maverick with full PagedAttention. Throughput is comparable to Llama 3.3 70B at similar active parameters.
Is the license still permissive?
Yes — Llama 4 community license, similar restrictions to Llama 3 (>700M MAU restriction still applies; not open-source under OSI definition but commercially usable for nearly all teams).
What about Llama 4 Scout's 10M context — is it usable?
Yes for batched offline workloads. For interactive use, latency at 10M is challenging — most teams cap at 1M-2M for online use cases.
Where to go next
For related guides see DeepSeek R2 review, Local LLM setup guide for 2026, and Small language models for the edge.