Real selection criteria
The right criteria, in order of importance: (1) Reliability on your specific case — what works in benchmark may not in your case. (2) Latency — different requirements for real-time chat vs batch processing. (3) Cost at scale — what works for prototype may be unsustainable at 1M queries/day. (4) Compliance — data residency, certifications. (5) Capabilities — multimodality, function calling, long context.
By use case
Customer service chatbot: Claude 3.5 Haiku or GPT-4o-mini. Speed + low cost + good for instruction following.
Complex code: Claude 3.5 Sonnet (best on SWE-bench) or GPT-5.5 Codex.
Long document analysis: Gemini 1.5 Pro (1M tokens) or Claude 3.5 Sonnet (200K tokens).
Real-time voice: GPT-4o (320ms TTFB).
Multimodality (text+image+video): Gemini 2.0 (native multimodality).
Math/science reasoning: OpenAI o1 or GPT-5.5 thinking mode.
Real costs
per million tokens
per million tokens
GPT-4o/Claude Sonnet
Multi-model strategy
The companies that win in 2026 don't pick one model — they orchestrate several. Classifier: small/cheap model decides which model handles the case. Specialist: the right model for each subtask. Verifier: another model verifies critical answers.
How VuraOS handles it
VuraOS uses 3-4 models in parallel: Haiku for classification, Sonnet for complex responses, GPT-4o for voice, Claude Opus for cases requiring max reasoning. The orchestrator chooses per case.
Conclusion
There's no "best model" in absolute terms. There's the right model for each specific case. Mature companies build orchestration that uses different models depending on use case and route. Mono-model approaches optimize the wrong variable.