{"slug": "gemini-3-5-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model", "title": "Gemini 3.5 Flash vs Claude Haiku vs GPT-4o mini: Picking a Small Model", "summary": "The article compares three small, fast LLMs—Gemini 3.5 Flash, Claude Haiku, and GPT-4o mini—for routine tasks like classification and code routing, emphasizing that cheap, consistent models are preferable over frontier models for such \"boring\" work. The author shares informal evaluation results and recommends using an adapter pattern to keep business logic provider-agnostic, enabling easy A/B testing and migration between models. Key caveats include skepticism toward marketing benchmarks and the importance of verifying model IDs and features in official documentation before deployment.", "body_md": "The Gemini 3.5 Flash announcement made the rounds on Hacker News this week, and I've been getting pinged by teammates asking whether to migrate our internal tools off Claude Haiku or GPT-4o mini. After spending a weekend running informal evals on classification and code-routing tasks, I have some takes.\n\nUpfront caveat: I'm hedging on specific Gemini 3.5 Flash feature claims because marketing benchmarks and actual API behavior rarely match. Check the [official Gemini docs](https://ai.google.dev/) before you bet your architecture on any number — including the ones I quote.\n\n## Why migrate small models at all?\n\nMost teams I work with use small, fast models for the boring stuff:\n\n- Classification (is this ticket billing, bug, or feature-request?)\n- Summarization (TL;DR a long Slack thread)\n- Code routing in agent setups\n- Lightweight extraction (pull dates and amounts out of an invoice)\n\nYou don't need a frontier model for any of these. You need something cheap, fast, and consistent. Frontier models are actually *worse* for tool routing in my experience — they overthink and burn latency on tasks that should be reflexive.\n\nSo when a new small model lands, it's worth 30 minutes of evaluation. Not a migration. Just a check.\n\n## The contenders\n\nThree models I'd consider for a new project today:\n\n-\n**Gemini 3.5 Flash**— Google's latest fast model. Reportedly faster and cheaper than the 2.5 generation, with a very long context window. I'll caveat the specifics below. -\n**Claude Haiku 4.5**— Anthropic's small model. I've used Haiku in production for two years; it's my default for anything that needs structured outputs or tool calling. -\n**GPT-4o mini**— OpenAI's small model. Still solid, still has the deepest ecosystem support.\n\nI'm leaving out Llama and Mistral on purpose. Self-hosted open models deserve their own post.\n\n## Side-by-side: calling the APIs\n\nHere's what each looks like in practice. All three have official SDKs, and the shape is similar but not identical.\n\n### Gemini 3.5 Flash\n\n``` python\n# pip install google-generativeai\nimport google.generativeai as genai\n\ngenai.configure(api_key=\"YOUR_KEY\")\n\n# verify the exact model ID in the official docs before shipping\nmodel = genai.GenerativeModel(\"gemini-3.5-flash\")\n\nresponse = model.generate_content(\n    \"Classify this ticket: 'Cannot log in after password reset.'\",\n    generation_config={\"temperature\": 0.2}  # low temp for classification\n)\nprint(response.text)\n```\n\n### Claude Haiku 4.5\n\n``` python\n# pip install anthropic\nfrom anthropic import Anthropic\n\nclient = Anthropic()  # reads ANTHROPIC_API_KEY from env\n\nmsg = client.messages.create(\n    model=\"claude-haiku-4-5\",\n    max_tokens=200,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Classify this ticket: 'Cannot log in after password reset.'\"\n    }]\n)\nprint(msg.content[0].text)\n```\n\n### GPT-4o mini\n\n``` python\n# pip install openai\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nresp = client.chat.completions.create(\n    model=\"gpt-4o-mini\",\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Classify this ticket: 'Cannot log in after password reset.'\"\n    }],\n    temperature=0.2\n)\nprint(resp.choices[0].message.content)\n```\n\nThree different shapes for the same operation. If you've been around long enough you know the drill — wrap it in your own interface and stop caring about the cosmetic differences.\n\n## Migrating: a thin adapter\n\nI migrated one of our pipelines from GPT-4o mini to Gemini Flash last quarter (the 2.5 generation, not the new one). The pattern that saved me:\n\n```\n# adapter.py — keeps business logic provider-agnostic\nclass LLMClient:\n    def __init__(self, provider: str):\n        self.provider = provider\n        if provider == \"gemini\":\n            import google.generativeai as genai\n            self.model = genai.GenerativeModel(\"gemini-3.5-flash\")\n        elif provider == \"claude\":\n            from anthropic import Anthropic\n            self.client = Anthropic()\n        elif provider == \"openai\":\n            from openai import OpenAI\n            self.client = OpenAI()\n        else:\n            raise ValueError(f\"Unknown provider: {provider}\")\n\n    def classify(self, prompt: str) -> str:\n        # one method, three implementations — callers stay clean\n        if self.provider == \"gemini\":\n            return self.model.generate_content(prompt).text\n        if self.provider == \"claude\":\n            msg = self.client.messages.create(\n                model=\"claude-haiku-4-5\",\n                max_tokens=200,\n                messages=[{\"role\": \"user\", \"content\": prompt}]\n            )\n            return msg.content[0].text\n        # openai\n        resp = self.client.chat.completions.create(\n            model=\"gpt-4o-mini\",\n            messages=[{\"role\": \"user\", \"content\": prompt}]\n        )\n        return resp.choices[0].message.content\n```\n\nBoring? Yes. But this adapter saved me a week when we needed to A/B test the new Gemini model. Flip a config flag, replay the same prompts, compare outputs. Zero business logic changed.\n\n## Tradeoffs I'm willing to commit to\n\nThings I feel confident about from actually running these in production:\n\n-\n**Claude Haiku** is the most predictable for structured outputs and tool calling. If your downstream code parses JSON, start here. -\n**GPT-4o mini** has the deepest ecosystem — every framework, every tutorial, every Stack Overflow answer. If your team is junior, the docs alone are worth it. -\n**Gemini Flash** has historically had the largest context window of the three. Useful when you need to dump entire codebases or long docs into a prompt.\n\nThings I'm explicitly hedging on for 3.5 Flash:\n\n- The benchmark numbers in Google's announcement look great. Benchmarks always look great. Run your own evals.\n- I haven't tested its tool-calling reliability against Haiku yet. That's next month's project.\n- Pricing on the official page is the only number I trust. Check it before architecting around cost assumptions.\n\n## Which one should you pick?\n\nHonest answer: it depends on what you're already running.\n\n-\n**Already on Haiku and it works?** Don't migrate. The savings won't pay for the engineering time. -\n**Building something new with massive context needs?** Start with Gemini 3.5 Flash. Long-context is its lane. -\n**Need bulletproof tool calling for agents?** Claude Haiku, every time. -\n**Just shipping a side project and want zero friction?** GPT-4o mini.\n\nThe dirty secret of LLM migrations is that model choice matters way less than your prompts and your eval suite. Spend the time on evals first. Then pick whichever model wins *your* benchmark — not whichever one trended on Hacker News last week.", "url": "https://wpnews.pro/news/gemini-3-5-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model", "canonical_source": "https://dev.to/alanwest/gemini-35-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model-52n4", "published_at": "2026-05-20 13:47:05+00:00", "updated_at": "2026-05-20 14:07:08.679526+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "developer-tools", "products"], "entities": ["Gemini 3.5 Flash", "Claude Haiku", "GPT-4o mini", "Google", "Hacker News", "Llama", "Mistral"], "alternates": {"html": "https://wpnews.pro/news/gemini-3-5-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model", "markdown": "https://wpnews.pro/news/gemini-3-5-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model.md", "text": "https://wpnews.pro/news/gemini-3-5-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model.txt", "jsonld": "https://wpnews.pro/news/gemini-3-5-flash-vs-claude-haiku-vs-gpt-4o-mini-picking-a-small-model.jsonld"}}