Head-to-head

Llama 4 (Scout / Maverick) logovsClaude Opus 4.8 logo

Llama 4 (Scout / Maverick) vs Claude Opus 4.8: which AI model wins in 2026?

Llama 4 (Scout / Maverick) ($0.60/1M out (Maverick, hosted)) and Claude Opus 4.8 ($25/1M out) are two of the most-used AI models in 2026. Across 3 community votes, Claude Opus 4.8 leads with 57% approval.

Quick verdict

On Reasoning, pick Claude Opus 4.8: the arena rates it 4.5/5 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 $25/1M out for Claude Opus 4.8.

Line-by-line comparison

From
$0.60/1M out (Maverick, hosted)No first-party paid Meta API; typical third-party hosted rates shown for Maverick (Scout from ~$0.10/$0.30 per 1M on DeepInfra, $0.11/$0.34 on Groq). Hosted Scout context is capped well below the 10M nominal spec (e.g. ~320K on DeepInfra).
$25/1M outStandard tier $5/$25 per 1M tokens (unchanged from Opus 4.7); fast mode research preview at $10/$50 (vs $30/$150 on Opus 4.7's deprecated fast tier); batch API 50% off at $2.50/$12.50; no long-context surcharge up to 1M tokens; prompt cache reads at $0.50/1M.
Provider
Meta
Anthropic
Context window
10M tokens (Scout) / 1M (Maverick)
1M tokens (128K max output)
Input price
$0.15/1M in (Maverick, hosted)
$5/1M in
Output price
$0.60/1M out (Maverick, hosted)
$25/1M out
Modalities
text, vision (image input)
text, vision (image input up to 2576px, text output)
Open weights
Yes
No
Crowd score
50%(0)
57%(3)
Arena ratings (1-5)
Reasoning
2.5
4.5
Coding
2.0
5.0
Writing
2.5
5.0
Speed
4.5
3.0
Value
3.5
3.5

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

Claude Opus 4.8

  • SWE-Bench Pro 69.2% (vs 64.3% for Opus 4.7) and beats prior Opus models on CursorBench at every effort level; strong real-world reports on large refactors and multi-file bug hunts
  • About 4x less likely than Opus 4.7 to let flaws in its own generated code pass unflagged; big jump on math reasoning (USAMO 2026: 96.7% vs 69.3%)
  • 1M-token context and 128K output at unchanged $5/$25 pricing, with no long-context premium; batch API at 50% off ($2.50/$12.50)
  • Fast mode (research preview) delivers up to 2.5x output speed at $10/$50, 3x cheaper than Opus 4.7's fast tier ($30/$150)
  • Unique API features for agents: mid-conversation system messages that preserve the prompt cache, and Dynamic Workflows spawning parallel subagents in Claude Code
  • 84% on Online-Mind2Web browser automation and record score on Legal Agent Benchmark (first model past 10% all-pass); strong enterprise knowledge work (Box reports 87% vs 77% internally)
  • Turn-by-turn regressions reported: missed obvious instructions in planning docs, answering a narrow slice of the goal, and worse one-shot simple UI generation than 4.7
  • Writing style criticized by heavy users: excessive hedging, over-cautious editing that 'cuts anything bold or funny' (Steve Yegge), and pushback loops even against well-evidenced theses
  • Language-mixing quirk: users report random Chinese, Cyrillic, or Greek insertions in long research threads
  • Visible quality degradation past ~200K tokens in hands-on use despite the advertised 1M window
  • Vending-Bench regression: fell for scam suppliers about 30x more than 4.7 and negotiates worse (a side effect of stricter honesty alignment)

Cast your verdict

One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.

Llama 4 (Scout / Maverick)$0.60/1M out (Maverick, hosted)
50%crowd score · 0
Claude Opus 4.8$25/1M out
57%crowd score · 3

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 Claude Opus 4.8

A drop-in upgrade for Opus 4.7 users: identical API surface and $5/$25 pricing with real gains on long-horizon agentic coding, code review, and enterprise analysis. Choose it if you run Claude Code, multi-file migrations, security audits, or agent pipelines that inspect, act, and verify over many steps. Skip it for quick one-shot UI snippets or prompts tightly tuned to 4.7 behavior, where users report regressions, and pick Sonnet 5 ($3/$15, intro $2/$10 through Aug 2026) if cost matters more than ceiling capability. Writers sensitive to hedging and over-cautious editing may find its style frustrating.

What the crowd says

On Llama 4 (Scout / Maverick)

No verdicts yet. Be the first to speak.

On Claude Opus 4.8

Judge Dreadful

Writing took a hit. It hedges everything and edits any bold or funny line out of my drafts. Also caught it answering a narrow slice of my planning doc and calling it done.

Champion of Vibes

Threw USAMO-level math at it for a lark and it just grinds through. 96.7 vs 69 for 4.7 tracks with what I see. Same $5/$25, 1M context, no excuse not to switch.

Glorius Maximus

Upgraded from 4.7 for a monorepo refactor and the difference is real. It actually flags its own sketchy code instead of shipping it. Multi-file bug hunts feel way less babysat.

Frequently asked questions

Is Llama 4 (Scout / Maverick) better than Claude Opus 4.8?

The crowd currently sides with Claude Opus 4.8: 57% recommend it, versus 50% for Llama 4 (Scout / Maverick) (3 votes). On Reasoning, Claude Opus 4.8 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 Claude Opus 4.8?

Llama 4 (Scout / Maverick) is cheaper: it starts at $0.60/1M out (Maverick, hosted), while Claude Opus 4.8 starts at $25/1M out.

How much do Llama 4 (Scout / Maverick) and Claude Opus 4.8 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. Claude Opus 4.8: $5/1M in per 1M input tokens, $25/1M out per 1M output tokens.