Head-to-head
Llama 4 (Scout / Maverick) vs GPT-5.2: which AI model wins in 2026?
Llama 4 (Scout / Maverick) ($0.60/1M out (Maverick, hosted)) and GPT-5.2 ($14/1M out) are two of the most-used AI models in 2026. Across 2 community votes, Llama 4 (Scout / Maverick) leads with 50% approval.
Quick verdict
On Reasoning, pick GPT-5.2: the arena rates it 4/5 against 2.5/5 for Llama 4 (Scout / Maverick). On budget, Llama 4 (Scout / Maverick) wins: it starts at $0.60/1M out (Maverick, hosted) versus $14/1M out for GPT-5.2.
Line-by-line comparison
Strengths and weaknesses
Llama 4 (Scout / Maverick)
- MoE efficiency: only 17B active parameters per token (Scout 109B/16 experts, Maverick 400B/128 experts), giving near GPT-4o-class chat at a fraction of the compute
- Very cheap hosted inference: Maverick from about $0.15/1M in and $0.60/1M out; Scout from about $0.10/$0.30 on DeepInfra or $0.11/$0.34 on Groq
- Native early-fusion multimodality (text plus images, tested up to 8 images) in an open-weight model
- Largest nominal context of any open-weight model at release: 10M tokens on Scout, 1M on Maverick
- Scout fits on a single H100 GPU with Int4 quantization; pretrained on 200 languages
- High throughput: 17B active params reach 500+ tokens/s on fast providers (Groq lists Scout at 594 TPS)
- Benchmark trust damaged: Meta submitted an unreleased chat-optimized Maverick variant to LMArena (ELO 1417), which devs called misleading since public weights score lower
- Long-context claims collapse in practice: Scout scored ~15.6% at 128K on Fiction.LiveBench vs 90.6% for Gemini 2.5 Pro, and hosted providers cap Scout far below 10M (e.g. ~320K on DeepInfra)
- Coding widely panned on r/LocalLlama and HN, losing to the similarly-priced DeepSeek V3 on real dev tasks
- Not OSI open source: license excludes EU-domiciled companies, requires a special license above 700M MAU, and mandates Llama branding
- 109B/400B total params are too big for consumer GPUs, and the lineage stalled: no reasoning variant shipped and Behemoth was never released
GPT-5.2
- 80.0% SWE-bench Verified and 55.6% SWE-Bench Pro at launch, near parity with Claude Opus 4.5 (80.9%)
- GDPval: ties or beats human professionals in 70.9% of comparisons, nearly double GPT-5.1's 38.8%
- Strong science and math: 92.4% GPQA Diamond (Thinking, 93.2% Pro) and 40.3% FrontierMath, state of the art at release
- 400K context with near-perfect MRCR v2 long-context retrieval up to 256K tokens
- 30% fewer hallucinations than GPT-5.1 (error rate on real ChatGPT queries down from 8.8% to 6.2%)
- Cheaper than its successors: $1.75/$14 per 1M vs $2.50/$15 (GPT-5.4) and $5/$30 (GPT-5.5)
- Speed is the top community complaint: extended thinking reported as low as ~4 tokens/s in ChatGPT, and Pro can think for a very long time and still fail
- Headline benchmark scores were obtained at xhigh reasoning effort, which consumes far more tokens and time than default settings
- Widely criticized personality regression vs GPT-5.1: Reddit users called it 'too corporate, too safe' and 'a step backwards' for chat and writing
- Coding lags Anthropic's line in head-to-head Elo comparisons (a later Opus 4.7 analysis cited a 144 Elo gap); no audio modality, no fine-tuning
- Already superseded as of mid-2026: OpenAI recommends GPT-5.5 and GPT-5.2 no longer appears on the main API pricing page
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
The arena’s verdict on Llama 4 (Scout / Maverick)
Pick Llama 4 if you need a cheap, fast, self-hostable multimodal model for high-volume chat, extraction, or multilingual workloads outside the EU; Maverick lands near GPT-4o quality at roughly a tenth of the price. Avoid it for coding, hard reasoning, or genuine million-token retrieval, where DeepSeek, Qwen 3, and Gemini clearly outperform it. Versus Llama 3.3 70B it adds native vision and a longer window, but many developers found the older dense model or Qwen more reliable for pure text quality, and the 10M context is mostly a paper number.
The arena’s verdict on GPT-5.2
Pick GPT-5.2 over GPT-5.1 for heavy reasoning, long-context, or agentic work: it nearly doubles GPT-5.1's GDPval win rate, cuts hallucinations 30%, and handles 400K contexts reliably. In mid-2026 it is mainly a value play, priced at $1.75/$14 versus $5/$30 for GPT-5.5 while staying competent on most professional tasks. Avoid it for latency-sensitive chat and creative writing, where users found it slow and flatter than GPT-5.1. Teams that want OpenAI's current frontier should pay up for GPT-5.5 instead.
What the crowd says
On Llama 4 (Scout / Maverick)
No verdicts yet. Be the first to speak.
On GPT-5.2
“The thinking speed is brutal, I clocked something like 4 tok/s in ChatGPT on extended thinking. Pro will grind for ages and still whiff. Great scores, painful to actually use.”
“GDPval numbers are wild, it ties or beats human pros in 71% of comparisons. For science and math work (92.4 GPQA Diamond) it earned a spot in my stack.”
Keep comparing
Frequently asked questions
Is Llama 4 (Scout / Maverick) better than GPT-5.2?
On Reasoning, GPT-5.2 rates higher (4/5 vs 2.5/5). The right pick depends on your use case. The line-by-line comparison on this page breaks down pricing, key specs and arena ratings.
Which is cheaper, Llama 4 (Scout / Maverick) or GPT-5.2?
Llama 4 (Scout / Maverick) is cheaper: it starts at $0.60/1M out (Maverick, hosted), while GPT-5.2 starts at $14/1M out.
How much do Llama 4 (Scout / Maverick) and GPT-5.2 cost per 1M tokens?
Llama 4 (Scout / Maverick): $0.15/1M in (Maverick, hosted) per 1M input tokens, $0.60/1M out (Maverick, hosted) per 1M output tokens. GPT-5.2: $1.75/1M in per 1M input tokens, $14/1M out per 1M output tokens.