Building an AI-powered feature in 2026 means picking an LLM provider. The three paths — OpenAI, Anthropic, and open source — each win on a specific axis. Here's the framework.
OpenAI
Best for: general-purpose applications, multi-modal (vision, voice), fastest time-to-market.
Strengths:
- Broadest model lineup: GPT-4o, GPT-4o-mini, o1, o1-mini
- Strong vision + audio capabilities
- Massive developer ecosystem, best documentation
- Assistants API abstracts a lot of complexity
Weaknesses:
- Occasional inconsistency in output style
- Pricing on top-tier models isn't cheap
- Model deprecation cycle is short (last year's model isn't always available)
Sweet spot: consumer applications, multi-modal features, teams that want the fastest ramp-up.
Anthropic (Claude)
Best for: long-context tasks, instruction-following, safety-sensitive applications, coding.
Strengths:
- Best-in-class long-context handling (200K+ tokens usable)
- Follows instructions carefully — outputs are more predictable
- Strongest coding model in independent benchmarks
- Prompt caching materially reduces cost for repeated context
Weaknesses:
- Fewer multi-modal options than OpenAI
- Smaller ecosystem of third-party tools
Sweet spot: document processing, complex agents, coding assistants, business applications where instruction-following consistency matters more than raw capability.
Open source (Llama, Mistral, Qwen, DeepSeek)
Best for: cost-sensitive high-volume use cases, on-premise requirements, deep customisation.
Strengths:
- Massively cheaper at high volume (via Together, Groq, Ollama, Fireworks)
- Full control: can fine-tune, host on-premise, no data sent to third parties
- Multiple strong models compete for you (Llama 3.3, Mistral Large 2, Qwen 2.5, DeepSeek)
Weaknesses:
- Lower ceiling than frontier proprietary models on hard tasks
- Requires more engineering investment (hosting, monitoring, updates)
- Model quality lags proprietary by roughly 6–12 months
Sweet spot: high-volume classification, on-premise required, cost-conscious internal tools.
The framework
Q1: How much do you care about hitting the frontier of capability?
- A lot (novel problems, cutting-edge reasoning) → OpenAI or Anthropic
- Just needs to work well → open source often sufficient
Q2: What's your volume?
- Under 1M tokens/month → any of the three
- 1M–100M/month → OpenAI or Anthropic (open source starts to save money here)
- Over 100M/month → open source almost always wins on cost
Q3: Data sensitivity?
- Regulated data (health, legal, financial with client PII) → often open source on-premise
- Standard business data → any of the three (with proper contract terms)
Q4: Team engineering capacity?
- Small team, want to ship fast → OpenAI or Anthropic API
- Bigger team, willing to invest in ops → open source becomes plausible
Cost comparison (rough, mid-2026)
For 1M input + 500K output tokens:
- OpenAI GPT-4o: ~$5.00
- OpenAI GPT-4o-mini: ~$0.30
- Anthropic Claude Opus 4.7: ~$18.00
- Anthropic Claude Sonnet 4.6: ~$4.00
- Anthropic Claude Haiku 4.5: ~$1.20
- Llama 3.3 (via Together): ~$0.90
- DeepSeek V3: ~$0.30
Note: prompt caching (available on both OpenAI and Anthropic) cuts input costs 50-90% for repeated context. This makes the frontier models much more competitive at scale.
Our recommendation
For most business applications, we default to Anthropic Claude Sonnet with prompt caching enabled. Excellent instruction-following, strong long-context handling, competitive cost. Good balance across every axis.
For simple classification or high-volume tasks, we drop to Claude Haiku or an open-source model.
For coding-heavy features or complex agents, Claude Opus (higher cost, materially better quality).
For multi-modal features (image, voice, video), OpenAI is often the pragmatic choice.
Rarely: fully open source, on-premise, with a fine-tuned model. Justified when the volume + data sensitivity math clearly favours it.
What we help with
We integrate LLMs into production products for clients — chat features, document processing, semantic search, RAG systems, agents. Book a consultation and we'll walk through what fits your specific use case.