1stTop pick
Researched 25d agoClaude Opus 4.7
- Arena
- 1503
- Output (from)
- $25.00 / 1M
Best LLMs for Marketing (2026)
Last refreshed 2026-07-21. Next refresh: weekly.
Top language models for marketing copy, ad creative, email, social posts, and brand-voice content. Ranked by Chatbot Arena human-preference scores with MMLU as a fallback.
Need essays, drafts, or general long-form prose rather than campaign copy? Compare the writing leaderboard for that intent.
Verdict
Use Claude Opus 4.7 for marketing copy today.
GPT-5.5 is the runner-up: 1503 vs 1488 on Arena.
How we rank
Marketing copy keeps a separate use-case layer from general writing. The matrix slot handles campaign-specific picks; the ranked table remains the Arena-then-MMLU fallback.
- Eligibility — General chat models excluding code/embedding SKUs.
- Editorial slot — The page reserves a six-row matrix for ad copy, SEO long-form, brand voice, free, localization, and overall picks before the ranked table.
- Primary ranking — Chatbot Arena, then MMLU, then newer release.
- Variant collapse — We keep one row per model family (`familySlug` + parameter tier). When headline scores tie within ±0.5 pt (±10 Elo on Chatbot Arena), we pick the canonical SKU by lowest tracked input price, then GA over preview or limited access, then newest `release`. A folded sibling within the benchmark noise band can show a "Tied within margin" chip on that score cell.
- Brand caveat — Benchmarks do not measure CTA compliance, offer clarity, localization nuance, or voice-guide fit; keep human review in the loop.
- Writing boundary — Use `/best/writing` for essays, drafts, and general prose; this page is for conversion and content-marketing workflows.
Marketing use-case matrix
Editorial picks for these six marketer workflows are reserved for the upcoming matrix. Until that ships, use the ranked table below as the broad model-quality fallback.
| Use case | Decision this slot will answer | Status |
|---|---|---|
| Ad copy | Which model best turns an offer into short hooks and variants. | Pending editorial pick |
| SEO long-form | Which model best drafts structured articles without losing brief constraints. | Pending editorial pick |
| Brand voice | Which model best follows tone, style, and banned-phrase guidance. | Pending editorial pick |
| Free option | Which free or open-weight route is credible for lightweight marketing drafts. | Pending editorial pick |
| Localization | Which model best adapts copy across languages and local buying context. | Pending editorial pick |
| Overall | Which model is the safest default when the marketing workflow spans formats. | Pending editorial pick |
| # | Model | Input $/1M | Output $/1M | |
|---|---|---|---|---|
| 1 | Claude Opus 4.7 ReasoningVisionTools Arena: 1503 | $5.00 | $25.00 | |
| 2 | Claude Opus 4.6 ReasoningVisionTools Arena: 1501 | $5.00 | $25.00 | |
| 3 | Gemini 3.1 Pro Preview PreviewVisionTools Arena: 1493 | $2.00 | $12.00 | |
| 4 | Muse Spark ReasoningVisionTools Arena: 1491 | — | — | |
| 5 | GPT-5.5 ReasoningVisionTools Arena: 1488 | $5.00 | $30.00 | |
| 6 | Gemini 3 Pro VisionTools Arena: 1486 | $1.25 | $5.00 | |
| 7 | GPT-5.4 ReasoningVisionTools Arena: 1479 | $2.50 | $15.00 | |
| 8 | ERNIE 5.1 Tools Arena: 1476 | $0.59 | $2.65 | |
| 9 | Qwen3.7-Max ReasoningTools Arena: 1475 | $1.25 | $3.75 | |
| 10 | GLM-5.1 ReasoningTools Arena: 1475 | $1.05 | $3.50 | |
| 11 | Gemini 3 Flash PreviewVisionTools Arena: 1467 | $0.50 | $3.00 | |
| 12 | Claude Opus 4.5 ReasoningVisionTools Arena: 1466 | $5.00 | $25.00 | |
| 13 | Grok 4.1 ReasoningVisionTools Arena: 1464 | — | — | |
| 14 | Claude Sonnet 4.6 ReasoningVisionTools Arena: 1459 | $3.00 | $15.00 | |
| 15 | DeepSeek V4 Pro ReasoningTools Arena: 1456 | $0.43 | $0.87 | |
| 16 | DeepSeek V4 Flash ReasoningTools Arena: 1437 | $0.09 | $0.18 | |
| 17 | Gemini 3.1 Flash-Lite VisionTools Arena: 1432 | $0.25 | $1.50 | |
| 18 | o3 ReasoningVisionTools Arena: 1412 | $2.00 | $8.00 | |
| 19 | Gemini 2.5 Pro ReasoningVisionTools Arena: 1398 | $1.25 | $10.00 | |
| 20 | DeepSeek R1 Reasoning Arena: 1372 | $0.10 | $0.30 |
Honorable mentions
Next seats in this ranking. Lines below are from each model's stored description in LLMReference seed data—spot-check the model page before relying on a capability claim.
ERNIE 5.1 is Baidu's fifth-generation flagship language model, officially released May 9, 2026. Achieved via disaggregated fully-asynchronous reinforcement learning and scaled agentic post-training, it delivers leading performance at approximately 6% of the pre-training compute cost of comparable models — with roughly one-third the total parameters and half the active parameters of ERNIE 5.0. ERNIE 5.1 ranks #4 globally and #1 among Chinese models on the LMArena Search leaderboard (score: 1,223), with standout performance in legal reasoning, mathematics (AIME26: 99.6), and business domains. API model ID: ernie-5.1. Context: 128K tokens; max output: 65,536 tokens.
1476
Arena
Alibaba's closed-weight flagship language model, announced at the 2026 Alibaba Cloud Summit (May 20). Scored 56.6 on Artificial Analysis Intelligence Index at launch—highest-ranked Chinese model. 1M-token context with prompt caching (up to 90% discount). Pricing: $2.50/$7.50 per 1M tokens in/out.
1475
Arena
- #6GLM-5.1
Post-training variant of GLM-5 from Z.ai (Zhipu AI) with enhanced agentic coding capabilities. Released April 7, 2026. 754B parameters (40B active) in Mixture of Experts architecture, 200K token context, 128K max output. Supports autonomous plan–execute–test–fix–optimize loops for up to 8 hours without human intervention. Trained entirely on Huawei Ascend hardware (no Nvidia). Key benchmarks: SWE-bench Pro 58.4 (world #1 at release, surpassing GPT-5.4 57.7 and Claude Opus 4.6 57.3), GPQA Diamond 86.2, AIME 2026 95.3, Terminal-Bench 2.0 63.5, MCP-Atlas 71.8, Chatbot Arena Elo 1475 (June 16, 2026, arena.ai). Available via Z.ai API ($1.40/$4.40 per 1M input/output tokens) and open weights on Hugging Face under MIT license.
1475
Arena
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Frequently asked questions
Which LLM is best for marketing copy?
Claude Opus 4.7 is the current LLMReference top pick for marketing copy. The verdict uses the stored category signal Arena: 1503. Output pricing starts at $25.00 per 1M tokens. Review the linked model and provider pages before production use because availability and pricing can change.
How does Claude Opus 4.7 compare to GPT-5.5 for marketing copy?
Claude Opus 4.7 leads GPT-5.5 in the visible shortlist on Arena: 1503 versus 1488. The pricing cards show Claude Opus 4.7: output pricing starts at $25.00 per 1m tokens and GPT-5.5: output pricing starts at $30.00 per 1m tokens.
How does LLMReference rank LLMs for marketing copy?
LLMReference ranks LLMs for marketing copy from stored model, benchmark, freshness, and pricing data. The current methodology summary is: Marketing copy keeps a separate use-case layer from general writing. The matrix slot handles campaign-specific picks; the ranked table remains the Arena-then-MMLU fallback.
How often is this list updated?
The LLM rankings on this page are updated daily as new benchmark scores, provider availability, and pricing data are tracked. The "as of" date at the top of the page shows the most recent refresh.
How do you decide which models appear in the top 3?
The podium picks are driven by the primary benchmark signal for this category (shown in the Methodology section), filtered to non-deprecated models with confirmed API availability. In ties, we prefer the more recently released model.
Are preview or beta models included?
Preview models appear in the "Watch list" section but are not in the main ranked podium unless the category explicitly allows it (e.g., /best/coding and /best/agents, where preview models often lead benchmarks).
Can I compare two specific models head-to-head?
Yes — use the Compare tool at llmreference.com/compare for a side-by-side breakdown of context window, pricing, benchmarks, and provider availability.
Is the pricing data real-time?
Pricing is tracked from provider documentation and updated regularly. It reflects the best available public data, not live API quotes — always verify before billing.