The LLM market has consolidated around three major players in 2026. Claude 4, GPT-5 and Gemini 2.5: each excels in different domains. Here is our field comparison based on hundreds of hours of testing on real use cases.
Our comparison method
We tested the three models on 8 task categories: long document analysis, code generation, complex reasoning, structured data extraction, writing, multi-turn conversations, classification and summarisation.
Claude 4 (Anthropic): the reasoning champion
Claude 4 stands out for complex reasoning, nuanced analysis and strict instruction following. Its 200K token context without degradation is unmatched.
GPT-5 (OpenAI): best for code
GPT-5 maintains dominance on code generation and technical tasks. The OpenAI API ecosystem remains the most mature.
Our field recommendation: Claude 4 Sonnet for agents and analysis, GPT-4o for code and APIs, Gemini 2.5 Flash for high-volume tasks where cost is critical.
Gemini 2.5 (Google): the value champion
Gemini 2.5 Flash offers remarkable performance at 5-10x lower cost than premium models. Best economic choice for classification, summarisation and large-volume extraction.
Our decision guide
Complex AI agents: Claude 4 Sonnet. Code generation: GPT-4o or Claude 4. Long documents: Claude 4. High volume low cost: Gemini 2.5 Flash. General use: Claude 4 Sonnet.
With care,
Excellent article, this matches exactly what we're seeing with our enterprise clients. The section on inference costs is especially valuable. It's a topic most articles gloss over but it's make-or-break at scale.
Thanks James! Inference cost optimization is often deprioritized during prototyping but becomes critical in production. Feel free to book a session if you'd like to go deeper on this.
Sharing this with my whole team. The distinction between an impressive demo and robust production is exactly the debate we're having internally right now. The human checkpoint advice is immediately actionable.
Great article. I'd push back slightly on the 18-day deployment estimate, in our experience with enterprise security and GDPR requirements, 4–6 weeks is more realistic for a first production agent.
Completely fair point David. The 18 days refers to a scoped first agent in a test environment. For full enterprise production with security constraints, your estimate is accurate.