Every few months, a new model leapfrogs the current leader on benchmarks. GPT-4 was the obvious choice — until Claude 3 Opus arrived. Then Gemini 1.5 Pro changed the calculus for long-context tasks. Now there are open-weight models that match frontier performance at a fraction of the cost.
Teams that locked in on one provider are now doing painful migrations. Teams that built model-agnostic workflows are just swapping a config value.
Why "just pick one" is the wrong strategy
The instinct to standardise on a single model is understandable — fewer keys to manage, simpler billing, one set of docs. But it creates three real problems:
- Task mismatch: Claude is better at long-document analysis. GPT-4o is better at structured JSON output. Gemini 1.5 Pro handles 1M-token contexts. No single model wins every task.
- Vendor lock-in: If your chosen provider has an outage, raises prices, or deprecates a model, you have no fallback.
- Cost inefficiency: Using GPT-4 for tasks that GPT-3.5 or Claude Haiku handles equally well costs 10–20x more per token for no quality gain.
The model comparison workflow
The most useful thing you can do before committing to a model for a specific use case is run the same prompt against multiple models and compare outputs side-by-side. This sounds obvious, but most teams never do it systematically.
A good comparison workflow looks like this:
- Define a set of 10–20 representative prompts for your use case
- Run each prompt against your candidate models simultaneously
- Score outputs on the dimensions that matter: accuracy, format compliance, latency, cost per call
- Pick the model that wins on your specific criteria — not on general benchmarks
- Revisit the comparison every quarter as models update
The key word is simultaneously. Running models one at a time introduces recency bias — you remember the last output most clearly. Side-by-side comparison forces honest evaluation.
A practical model selection guide for common tasks
Based on what we see teams using in production:
- Code generation and review: GPT-4o or Claude 3.5 Sonnet. Both are strong; Claude tends to produce cleaner explanations alongside the code.
- Long document summarisation (>50 pages): Gemini 1.5 Pro or Claude 3 Opus. The 1M-token context window is a genuine differentiator here.
- Structured data extraction (JSON/CSV output): GPT-4o with function calling. The structured output mode is the most reliable.
- Customer-facing chat: Claude 3.5 Sonnet. Consistently rated highest for tone and helpfulness in user studies.
- High-volume, cost-sensitive tasks: Claude Haiku or GPT-3.5 Turbo. 10–20x cheaper than frontier models with acceptable quality for simple tasks.
- On-premise or air-gapped: Llama 3 or Mistral via a self-hosted inference server.
Managing multiple provider keys without chaos
The operational overhead of multi-model setups is real. You end up with keys from Anthropic, OpenAI, Google, and possibly AWS Bedrock — each with different auth patterns, rate limits, and billing cycles.
The right abstraction is a single internal API that your team calls, which routes to the appropriate provider based on the model requested. Your engineers write code against one interface. The routing, key management, and quota enforcement happen in one place.
This also means you can swap the underlying model without changing application code — just update the routing config.
Cost comparison: what you're actually paying per task
Token pricing is hard to reason about in the abstract. Here's a concrete example: summarising a 10-page document (roughly 8,000 tokens input, 500 tokens output):
- GPT-4o: ~$0.046 per summary
- Claude 3.5 Sonnet: ~$0.027 per summary
- Claude Haiku: ~$0.003 per summary
- Gemini 1.5 Flash: ~$0.002 per summary
If you're running 10,000 summaries a month, the difference between GPT-4o and Gemini Flash is $440/month. For the same quality output on a straightforward summarisation task, that's hard to justify.
One of our customers cut their monthly AI spend by 60% in two weeks — not by using AI less, but by routing low-complexity tasks to cheaper models while keeping frontier models for tasks that actually needed them.
Setting this up with Lab Maneuver
Lab Maneuver's model playground lets your team run any prompt against multiple models simultaneously and compare outputs in a split-screen view. You connect your own provider keys, set per-model token quotas, and your team gets a single interface for all models — no key management, no per-model onboarding.
The usage dashboard breaks down spend by model, so you can see exactly which models are being used for what — and make data-driven decisions about where to route traffic.
Ready to stop guessing which model to use?
Run your prompts against Claude, GPT-4, and Gemini side-by-side. Set up in under an hour.
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