Emma’s model picker is at the top of every chat. Switch models mid-conversation if a task changes. Every model listed here is selectable on the current plan — Emma doesn’t silently downgrade.
The decision framework
Model selection in 2026 is no longer “pick the best model.” The community consensus — across Reddit’s r/LocalLLaMA, r/ChatGPT, and r/ArtificialIntelligence, and confirmed by independent benchmarks — is that multi-model routing beats single-model commitment. The right question is: what am I asking the model to do right now? Three variables matter:- Quality ceiling — does this task need frontier reasoning, or is “good enough” actually good enough?
- Speed — is this interactive (you’re waiting) or batch (it runs overnight)?
- Cost — one image at 0.19 each is a budget decision.
Recommended models by task
Ad creative and image generation
Recommended:gemini-3.5-flash (Gemini 3.5 Flash Image)
The best ROI for speed and quality in ad creative production. Independent testing shows Gemini Flash models produce legible text, correct diagrams, and brand-accurate product images at roughly 0.19 and 60+ seconds (Meowapps benchmark, June 2026). For Emma’s B2B Meta Ads work plan, which generates 20+ creatives per campaign, this difference compounds: a full creative arsenal costs under 4+ on GPT Image.
The Reddit community on r/ArtificialIntelligence has reached consensus that Gemini image models (the “Nano Banana” family) offer the best overall value for production workflows — fast generation, strong text rendering, and the lowest cost per image among frontier models (AI Tool Discovery, Reddit roundup, July 2026).
When to upgrade: GPT Image 2 (gpt-5.4 or higher with image generation) produces the most detailed and illustrative output when absolute visual quality matters more than speed or cost — hero images for a homepage, a single high-stakes creative. Use it selectively, not as the default.
Marketing copy, ad copy, and content writing
Recommended:anthropic.claude-sonnet-4-6 (Claude Sonnet 4.6)
Claude models consistently produce the most natural-sounding prose and follow brand voice instructions more reliably than alternatives. The EQ-Bench Creative Writing leaderboard (August 2026) confirms Claude as the current benchmark for tone, nuance, and structural variety in marketing content (EVY, August 2026). BenchLM rates Claude’s instruction-following (Arena IF: 1500) as the highest among frontier models (BenchLM, July 2026) — which is exactly what matters when Emma is writing in your founder’s voice from Brand Guidelines.
For Emma’s content workflows — LinkedIn posts, ad copy, email sequences, landing page text — Claude Sonnet 4.6 hits the sweet spot of quality and cost (15 per million tokens). It follows character limits, respects format constraints, and produces copy that requires less human editing than alternatives.
When to upgrade: anthropic.claude-opus-4-8 (Claude Opus) for the highest-stakes content — investor-facing materials, long-form thought leadership, or anything where the first draft needs to be very close to publishable. Opus is the default on several of Emma’s premium work plans (B2B Meta Ads Creative Production, Meta Pre-Launch Gate) for this reason.
Handwritten text extraction and document OCR
Recommended:anthropic.claude-sonnet-4-6 or gpt-5.4
Handwriting OCR is a task where frontier vision models have decisively overtaken traditional OCR engines. The 2026 benchmarks tell a clear story: frontier vision-language models achieve sub-2% character error rate on modern handwriting, compared to 9%+ for dedicated handwriting engines (Journal of Documentation, 2025; CodeSOTA benchmark, 2026).
Both Claude Sonnet 4.6 and GPT-4o-class models work well. Claude excels on dense, structured documents — contracts, multi-table pages, technical reports — where it needs to interpret as well as transcribe. GPT-4o leads slightly on degraded scans and messy handwriting (91.2% accuracy on handwritten forms in production testing), using contextual clues to decipher characters (TokenMix, April 2026; Convly, July 2026).
The practical recommendation: use whichever model you’re already running for the surrounding workflow. If you’re processing event registration forms in a CRM pipeline, Claude is already loaded. If you’re extracting data from scanned receipts in a standalone task, GPT handles the worst handwriting marginally better.
What doesn’t work: Gemini Flash models — their vision capabilities are excellent for general image understanding, but fine-grained text extraction on degraded or handwritten documents is not their strength. This is a task where you need frontier-tier vision, not the fast-and-cheap tier.
Research, competitor analysis, and long-document processing
Recommended:gemini-3.1-pro-preview (Gemini 3.1 Pro)
Gemini Pro’s massive context window is the primary advantage here. As of mid-2026, Gemini 3.1 Pro supports up to 2.5 million tokens — the largest among commercial models (tech-insider.org, July 2026). It can process an entire competitor’s website, a 400-page contract, or a full quarter of CRM engagement history in a single call without chunking — and chunking typically reduces extraction accuracy by 3-7% (TokenMix, April 2026).
Gemini also leads on factual grounding — scoring 93.2% on the FACTS Grounding benchmark vs. Claude Opus 4.6 at 91.4% (AIMagicX, April 2026) — and integrates live search during generation, which reduces hallucination on recent facts. For Emma’s Competitor Intelligence Report and GTM Discovery work plans, these advantages compound: more complete input data, fewer fabricated details, lower cost per token (12 per million vs. Claude Sonnet’s 15).
Independent needle-in-haystack testing confirms Gemini 3.1 Pro maintains over 90% recall at 1M tokens, with Claude Opus 4.6 holding similar accuracy to about 750K (tech-insider.org, June 2026).
When Claude is better: For extraction accuracy on complex nested structures (legal contracts, financial statements with cross-references), Claude leads at 97.6% vs. Gemini’s 95.2% (TokenMix, April 2026). Claude also produces more precise reasoning over retrieved content — Opus 4.8 leads Gemini on every reasoning benchmark, including SWE-Bench Pro (69.2% vs. 54.2%) and GDPval knowledge work (ELO 1890 vs. 1314) (Contra Collective, May 2026). If you need to analyze what you found rather than just find it, Claude is the stronger choice. The practical split: Gemini for broad research and long-document triage, Claude for deep analysis and precision extraction on what Gemini surfaces.
Lead enrichment and CRM workflows
Recommended:anthropic.claude-sonnet-4-6 at minimum; anthropic.claude-opus-4-6-v1 or higher strongly preferred
Lead enrichment is one of Emma’s most demanding tasks. It chains web search, LinkedIn profile parsing, ICP scoring, and personalized outreach drafting across dozens of leads in a single run. Cheap models produce shallow enrichment — missing firmographic detail, generic ICP scores, and template-sounding outreach that contacts ignore.
The rule: the more advanced the model, the better the enrichment quality. At the very least, use Claude Sonnet 4.6. For large enrichment runs (50+ leads, event guest lists, scheduled enrichment), Opus-class models produce meaningfully better results — deeper company research, more accurate ICP scoring, and outreach drafts that read like they were written by someone who actually read the prospect’s LinkedIn.
For the Field Event Luma Engagement work plan and batch CRM enrichment, set the model to anthropic.claude-opus-4-6-v1 or anthropic.claude-opus-4-8. The per-lead cost increase is small relative to the value of correctly qualifying and personalizing outreach to high-intent event leads.
Coding, technical implementation, and skill development
Recommended:anthropic.claude-sonnet-4-6 (Claude Sonnet 4.6)
The Reddit developer community has reached a clear hierarchy for coding: Claude > GPT > Gemini (foxafox.com Reddit roundup, May 2026). Claude Sonnet 4.6 leads SWE-bench Verified, catches subtle bugs, respects existing codebases, and handles multi-file refactoring better than alternatives (vexp.dev, June 2026). For Emma’s internal skill development and landing page code editing, Claude is the default for a reason.
For cost-sensitive batch coding: openrouter/deepseek/deepseek-v4-pro delivers 85-90% of frontier model performance at a fraction of the cost. Good for high-volume routine tasks where you’re running many similar operations.
General-purpose and mixed-task conversations
Recommended:anthropic.claude-sonnet-4-6 or gpt-5.4
For conversations that jump between tasks — “update my Market Strategy, then draft a LinkedIn post, then check my CRM leads” — you want a model that handles the range without being bad at any one thing. Claude Sonnet 4.6 and GPT-5.4 are both strong generalists. Claude tends to produce better writing; GPT tends to be slightly faster on mixed-modal tasks.
Quick-reference table
Cost tiers at a glance
Emma’s models fall into three cost tiers. Knowing which tier you’re in helps you budget:Sources
The recommendations above draw from both Emma’s internal benchmarking and the broader practitioner community:- Image generation: Meowapps independent testing (June 2026) measured Gemini Flash at ~0.19 in 60+ seconds. AI Tool Discovery’s Reddit roundup (July 2026) reports r/ArtificialIntelligence consensus on Gemini image models as best value for production. ZDNet rated Gemini 3 Pro Image at 93% overall for image generation quality.
- Writing quality: EQ-Bench Creative Writing leaderboard (August 2026, via EVY) places Claude Opus 5 first at Elo 2105. BenchLM (July 2026) rates Claude’s Arena IF (instruction-following) at 1500, the highest among frontier models. BuildMVPFast (July 2026) names Claude Sonnet 5 as the best raw writing model for natural tone and low-edit drafts.
- Handwriting OCR: CodeSOTA benchmark (2026) measures frontier VLMs at 1.2-1.5% CER, beating the GPT-4o 1.69% baseline from March 2025 (arXiv 2503.15195). TokenMix production testing (April 2026) reports GPT-4o Vision at 91.2% on handwritten forms, Claude at 97.6% on complex structured extraction. AIMultiple’s 2026 handwriting benchmark reports Gemini 2.5 Pro at 93% accuracy, behind GPT-5 (95%).
- Research and long context: Gemini 3.1 Pro verified at 2.5M token context window (tech-insider.org, July 2026). AIMagicX (April 2026) measures Gemini 3.1 Pro at 93.2% on FACTS Grounding vs. Claude Opus 4.6 at 91.4%. Independent needle-in-haystack testing shows Gemini at 90%+ recall at 1M tokens, Claude Opus similar accuracy to ~750K (tech-insider.org, June 2026). TokenMix (April 2026) finds chunking reduces extraction accuracy by 3-7%. For reasoning over retrieved content, Contra Collective (May 2026) reports Claude Opus 4.8 leading Gemini on SWE-Bench Pro (69.2% vs 54.2%) and GDPval knowledge work (ELO 1890 vs 1314).
- Coding: foxafox.com Reddit roundup (May 2026) reports the community hierarchy as Claude > GPT > Gemini. vexp.dev (June 2026) measures Claude Opus 4.6 at 72.1% on SWE-bench Verified.
- Multi-model routing: The 2026 practitioner consensus — visible across Reddit, Medium, and independent benchmarks — is that no single model wins everything. Teams routing Claude for coding and writing, Gemini for research and batch work, and GPT for mixed-modal tasks consistently outperform single-vendor setups.
How to change models
In the Customer Portal:- Per conversation: Click the model name at the top of any chat to open the picker. Your selection persists for that conversation.
- For scheduled chats: Set the model when creating or editing a scheduled chat. Budget models like
gemini-3.1-flash-liteare fine for lightweight tasks; use Opus for lead enrichment and complex workflows. - For work plans: Some work plans specify a default model (e.g., Meta Pre-Launch Gate defaults to Claude Opus). You can override this at the start of the conversation if you prefer a different model.
Next steps
- Concepts — skill, work plan, scheduled chat, template.
- Agent Mode — the chat interface where you select models.
- Settings — manage connected accounts and API configuration.
- Coming soon: automatic model routing. The recommendations on this page are manual today — you pick the model per conversation. The next step is for Emma to route automatically to the best-performing model for each task type, so you get optimal quality and cost without switching the picker yourself. Think of it as the multi-model strategy above, applied by default.