Llama 4 (Meta)
B Tier · 7.9/10
Meta's open-weights flagship family -- Scout (10M context), Maverick (multimodal 400B MoE), Behemoth in preview
Score Breakdown
Benchmark Scores
Benchmarks for Llama 4 Maverick (17B/400B MoE)
| Benchmark | Description | Score | |
|---|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 80.5% | |
| GPQA Diamond | Graduate-level science questions | 69.8% | |
| HumanEval | Python code generation | 88% | |
| MMMU (multimodal) | 73.4% |
Last updated: 2026-04-13
Personality & Tone
The open-weight workhorse
Tone: Plain, helpful, and neutral. Meta's instruction-tuned Llama 4 reads like a sanitized ChatGPT -- useful for general tasks but without a strong persona of its own.
Quirks: The 'real' personality depends on the checkpoint you run. Base Llama 4 is bland by design; the interesting behaviors come from community fine-tunes (Nous, Hermes, Dolphin, etc.) that give it different voices and refusal patterns.
The Good and the Bad
What we like
- +Llama 4 Scout has a 10M token context window -- longest shipping open-weight model, ideal for RAG
- +Llama 4 Maverick is natively multimodal (early-fusion) and hit Elo 1417 on LMArena experimental
- +Permissive-enough license for most commercial use (700M MAU clause rarely binds)
- +Biggest open-weights ecosystem by far -- Ollama, LM Studio, vLLM, llama.cpp, thousands of fine-tunes
- +Meta invests heavily -- Behemoth (~2T) is in preview as the teacher model
What could be better
- −Llama 4 initial launch underdelivered on vibes vs. benchmark numbers per r/LocalLLaMA consensus
- −Community License is not Apache/MIT -- the 700M MAU clause and attribution requirement rule out some commercial use
- −Requires serious hardware to run the flagship sizes -- Maverick full-precision needs 4× H100
- −DeepSeek V3.2 and Kimi K2.5 have surpassed Llama on many benchmarks at similar or lower cost
Pricing
Self-hosted (Free)
- ✓Llama 4 Community License
- ✓Unlimited use
- ✓Zero data sharing
- ✓700M MAU clause + attribution required
Cloud API (Together.ai, Fireworks, Groq)
- ✓Scout: $3 in / $7.50 out
- ✓Maverick: $8 in / $20 out
- ✓No hardware needed
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Llama 4 Scout (109B MoE, 17B active, 10M context)Full 10M context is practically unreachable on consumer hardware due to KV-cache size | 2× RTX 4090 48 GB total (Q4 quantization) | 2× A100 80 GB FP16 |
| Llama 4 Maverick (400B MoE, multimodal) | 128 GB unified RAM Mac Studio M3 Ultra (Q3) | 4× H100 80 GB or 2× H200 FP8 |
| Llama 3.3 70B (dense, still popular) | 1× RTX 3090/4090 24 GB (Q4) | 1× H100 80 GB FP16 |
Known Issues
- Llama 4 Maverick scored Elo 1417 on a special 'experimental chat' variant on LMArena -- the released weights feel weaker than that number impliesSource: Reddit r/LocalLLaMA, LMArena notes · 2026-04
- Quantized versions of Scout at 10M context use enormous KV-cache memory -- full 10M is practically unreachable on consumer hardwareSource: Hugging Face discussions · 2026-03
Best for
Developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (Scout), or multimodal (Maverick). Safe default choice given the ecosystem.
Not for
Teams chasing the absolute frontier on benchmarks -- DeepSeek V3.2 and Kimi K2.5 score higher. Also not ideal if you need true MIT/Apache licensing (use Qwen, GLM, or MiniMax instead).
Our Verdict
Llama 4 is the safe open-weights default in 2026. It has the biggest ecosystem, the longest context (Scout's 10M), and genuine multimodality (Maverick). But the frontier has moved -- DeepSeek V3.2 and Kimi K2.5 are stronger per-dollar, and the Llama 4 Community License is less permissive than Apache 2.0 alternatives from Alibaba and Z.ai. If you're building on open weights and want maximum compatibility, Llama 4 is still the right pick. If you want best-in-class performance per dollar, look at DeepSeek or Qwen.
Sources
- Meta Llama official site (accessed 2026-04-13)
- Meta AI blog: Llama 4 (accessed 2026-04-13)
- Together.ai pricing (accessed 2026-04-13)
- LMArena leaderboard (accessed 2026-04-13)
- Reddit r/LocalLLaMA (accessed 2026-04-13)
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