Llama 4 (Meta)
B Tier · 7.9/10
Meta's open-weights family -- Scout (10M context), Maverick (multimodal 400B MoE). NOTE: Meta's frontier work moved to the proprietary Muse Spark line in April 2026; Llama remains downloadable and supported but is effectively in maintenance mode
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
- STRATEGIC SHIFT (2026-04-08): Meta's frontier development has moved OFF the Llama line. Meta Superintelligence Labs launched **Muse Spark** -- Meta's first proprietary, closed-source frontier model (see the muse-spark page) -- and tier-1 coverage frames it as the Llama successor ('Goodbye Llama,' per VentureBeat). Llama has NOT been formally discontinued: existing models stay downloadable and supported, and Bloomberg-sourced reports say open derivatives of future models are under consideration. But community consensus is Llama-4-era weights are now in maintenance mode -- 'Llama 5' claims circulating on low-quality aggregators are fabricated; no vendor announcement exists. If you're betting a product on continued frontier open-weight releases from Meta, that bet now looks shaky -- Qwen, DeepSeek, and GLM are the active open-weight frontierSource: VentureBeat (Muse Spark launch), The New Stack, understandingai.org · 2026-04-08
- 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 still the compatibility king of open weights -- biggest ecosystem, longest context (Scout's 10M), genuine multimodality (Maverick). But two things changed in 2026: the open-weight frontier moved to Qwen, DeepSeek, and GLM on performance per dollar, and in April Meta itself moved on -- its frontier development now lives in the proprietary Muse Spark line, leaving Llama in de facto maintenance mode. Existing weights aren't going anywhere, and for maximum tooling compatibility Llama 4 remains a safe pick today. Just don't build a roadmap that assumes a frontier-class open Llama 5 -- there is currently no vendor signal one is coming.
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)
Explore more Llama 4 (Meta) rankings
Deeper leaderboards, benchmarks, task-specific tier lists, and status/pricing pages for Llama 4 (Meta).
The Tier List Tuesday
Weekly newsletter: tier movers, new entrants, and the VS of the week. Built from our daily AI-tool sweeps. No spam, unsubscribe anytime.
Alternatives to Llama 4 (Meta)
Mistral AI
European AI lab with open and commercial models -- Le Chat is now **Vibe** (May 28 2026): one agent across Work Mode + Code Mode with a VS Code extension and CLI, powered by Mistral Medium 3.5 (128B dense, 256k context, 77.6% SWE-Bench Verified). Earlier 2026 line: Small 4 (119B MoE Apache 2.0), Medium 3, Voxtral TTS
DeepSeek
DeepSeek V4 shipped 2026-04-24: V4-Pro (1.6T/49B active MoE) + V4-Flash (284B/13B active), 1M native context, Hybrid Attention Architecture, open-source on HF. Trails only Gemini 3.1 Pro on world knowledge
Gemma 4 (Google)
Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices
DiffusionGemma (Google)
Google DeepMind's experimental open-weights TEXT-DIFFUSION model (June 10, 2026) -- 26B MoE (3.8B active), Apache 2.0, generates 256-token blocks in parallel with bidirectional attention for up to 4x faster output (1,000+ tok/s on H100). Trades some quality vs Gemma 4 for raw speed
Qwen (Alibaba)
Alibaba's open-weights + API family -- Qwen 3.7 Max flagship GA (May 20 2026: SWE-Bench Pro 60.6%, Terminal-Bench 69.7%, GPQA 92.4%, $2.50/$7.50 per 1M with 50% promo until 6/22), Qwen3.7-Plus multimodal API (Jun 2), Qwen3.6-27B dense Apache 2.0 (beats the 397B MoE on coding from one consumer GPU)
GLM / Z.ai (Zhipu AI)
Zhipu AI's open-weights flagship -- GLM-5.2 (launched 2026-06-13) is a ~753B-parameter MoE with a 1M-token context and the new IndexShare sparse-attention architecture (~2.9x lower per-token FLOPs at 1M context), MIT licensed. Vendor benchmarks put SWE-Bench Pro at 62.1 (up from GLM-5.1's 58.4) and it tops the Artificial Analysis open-weights Intelligence Index; VentureBeat reports it beats GPT-5.5 on several long-horizon coding benchmarks at roughly 1/6 the cost. Drop-in for Claude Code / Cline / OpenCode. Still trained outside the Nvidia stack on Huawei Ascend silicon
Kimi K2.6 (Moonshot)
Moonshot's 1T-parameter MoE open-weights flagship -- Kimi K2.6 (GA 2026-04-20) is #1 open-weights on Artificial Analysis Intelligence Index v4.0 (score 54, ranked #4 overall). Native video input, 256K context, Modified MIT license
Nemotron (Nvidia)
Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware
MiniMax M3
MiniMax's coding/agent flagship -- M3 (June 1 2026): 1M-token context, MSA sparse attention (>15x decoding speedup at long context), SWE-Bench Pro 59.0%, Terminal-Bench 66.0%. OPEN WEIGHTS LIVE on HuggingFace since June 12 (~428B total / ~23B active, native multimodal, minimax-community license)
Falcon (TII)
UAE's Technology Innovation Institute open-weights family -- Falcon 3 optimized for efficient sub-10B deployment on consumer hardware
gpt-oss (OpenAI)
OpenAI's FIRST open-weight models -- gpt-oss-120b (single 80GB GPU, near parity with o4-mini on reasoning) and gpt-oss-20b (runs on 16GB edge devices). Apache 2.0. Launched 2025-08-05. gpt-oss-safeguard ships in 2026 as the safety-tuned variant
IBM Granite 4.0
IBM's enterprise-focused open-weight family -- Granite 4.0 hybrid Mamba-2 + transformer architecture (70-80% memory reduction vs pure transformer), 3B to 32B sizes, Apache 2.0. First open model family to secure ISO 42001 certification. Nano 350M runs on CPU with 8-16GB RAM. 3B Vision variant landed 2026-04-01
Arcee Trinity-Large-Thinking
Arcee AI's US-made open-weight frontier reasoning model -- launched 2026-04-01. 398B total params, ~13B active. Sparse MoE (256 experts, 4 active = 1.56% routing). Apache 2.0, trained from scratch. #2 on PinchBench trailing only Claude 3.5 Opus. ~96% cheaper than Opus-4.6 on agentic tasks
Olmo 3 (AI2)
Allen Institute for AI's fully-open frontier reasoning models -- Olmo 3 family (2025-11-20) includes 7B and 32B sizes, four variants (Base, Think, Instruct, RLZero). Apache 2.0 with fully open data + checkpoints + training logs. Olmo 3-Think 32B matches Qwen3-32B-Thinking at 6x fewer training tokens
AI21 Jamba2
AI21 Labs' hybrid SSM-Transformer (Mamba-style) open-weight family -- Jamba2 launched 2026-01-08. Two sizes: 3B dense (runs on phones / laptops) and Jamba2 Mini MoE (12B active / 52B total). Apache 2.0, 256K context, mid-trained on 500B tokens
StepFun Step 3.7 Flash
StepFun's (China) agent-focused open-weight family -- Step 3.7 Flash (May 28 2026): 198B sparse MoE vision-language model, ~11B active, 256K context, Apache 2.0, ~400 tok/s, SWE-Bench Pro 56.3. Supersedes Step 3.5 Flash (Feb 2026) as the flagship
Cohere Command A
Cohere's enterprise-multilingual flagship -- 111B params, 256K context, runs on 2x H100. 23 languages. CC-BY-NC 4.0 on weights (research / non-commercial), commercial requires Cohere enterprise contract. Follow-ups: Command A Reasoning + Command A Vision