Head-to-head
Llama 4 (Scout / Maverick) vs DeepSeek-V4: which AI model wins in 2026?
Llama 4 (Scout / Maverick) ($0.60/1M out (Maverick, hosted)) and DeepSeek-V4 ($0.87/1M out) are two of the most-used AI models in 2026. Across 3 community votes, DeepSeek-V4 leads with 57% approval.
Quick verdict
On Reasoning, pick DeepSeek-V4: the arena rates it 4.5/5 against 2.5/5 for Llama 4 (Scout / Maverick). Both start at the same price: $0.60/1M out (Maverick, hosted).
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
DeepSeek-V4
- 1M-token context window (8x the 128K of V3.2) with up to 384K output tokens, standard on the official API
- Aggressive pricing: $0.435/$0.87 per 1M tokens (V4-Pro), roughly 28.7x cheaper per output token than Claude Opus 4.8; cache-hit input drops to $0.003625/1M (over 99% discount)
- MIT-licensed open weights for both V4-Pro and V4-Flash on Hugging Face: commercial use, fine-tuning and redistribution allowed
- Open-source SOTA on agentic coding: 80.6 on SWE-bench Verified (Think Max config), tied with Gemini 3.1 Pro, plus Codeforces rating 3206 (~rank 23 vs humans)
- Ranks #3 of 93 on the Artificial Analysis Intelligence Index (score 44), well above the 25 average
- Sparse-attention stack cuts 1M-context inference to 27% of V3.2's FLOPs and 10% of its KV cache
- Intermittent malformed tool calls: function calls sometimes emitted as plain text in content instead of the tool_calls field (GitHub issue deepseek-ai #1244)
- Thinking mode breaks long multi-turn tool-call chains with 400 errors in agent frameworks (OpenClaw issue #72044, fix still incomplete)
- Developers report it fabricating nonexistent APIs in custom codebases and acting on hallucinated user input in agent loops
- Very verbose (180M eval output tokens vs 95M median) and mid-pack speed at 54.6 tok/s (#39/93), which erodes the low per-token price in practice
- Text only (no vision or audio) and still a preview: the official release planned for mid-July 2026 adds peak-hour pricing that doubles listed API rates during Beijing business hours
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 DeepSeek-V4
Choose DeepSeek-V4 if you want near-frontier reasoning and agentic coding at 3x to nearly 30x below Claude Opus or GPT-5.5 pricing, or if MIT-licensed weights for self-hosting and fine-tuning matter to you. It is a decisive upgrade over V3.2: 8x longer context, far cheaper long-context inference and stronger coding, and the legacy deepseek-chat/reasoner endpoints are deprecated on July 24, 2026 anyway. Avoid it for production agents that depend on rock-solid multi-turn tool calling, where users still report malformed tool calls and fabricated APIs, and for any vision or audio work since it is text only. Latency-sensitive apps should also test first, as its verbosity and mid-pack 54.6 tok/s output speed offset some of the cost advantage, and budget for the peak-hour price doubling arriving with the official mid-July release.
What the crowd says
On Llama 4 (Scout / Maverick)
No verdicts yet. Be the first to speak.
On DeepSeek-V4
“Tool calling is flaky. Function calls sometimes land as plain text instead of the tool_calls field, and thinking mode 400s on long multi-turn chains. Not agent-ready yet.”
“MIT license on both Pro and Flash weights is the real story. Fine-tune, redistribute, ship commercially, no lawyer needed. Plus 384K output tokens for long-doc generation.”
“MIT weights, 1M context, and output tokens roughly 29x cheaper than Opus 4.8. Cache hits make input basically free. Moved my bulk pipelines over and the bill collapsed.”
Keep comparing
Frequently asked questions
Is Llama 4 (Scout / Maverick) better than DeepSeek-V4?
The crowd currently sides with DeepSeek-V4: 57% recommend it, versus 50% for Llama 4 (Scout / Maverick) (3 votes). On Reasoning, DeepSeek-V4 rates higher (4.5/5 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 DeepSeek-V4?
They cost the same to start: both begin at $0.60/1M out (Maverick, hosted).
How much do Llama 4 (Scout / Maverick) and DeepSeek-V4 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. DeepSeek-V4: $0.435/1M in (cache hit $0.003625) per 1M input tokens, $0.87/1M out per 1M output tokens.