There Are 47 AI Models. I Have Credits for 12 of Them. I Still Don't Know Which One to Use.

The Current Model Landscape Is Absurd

Last week I spent 18 minutes staring at my IDE trying to decide which AI model to use for a straightforward refactoring task. Not because I didn't know how to refactor the code - because I had 47 possible model configurations to choose from.

Four Claude models. Three GPT-5.6 variants. Five Gemini options. Eight different open-source models running locally through Ollama. And that's before you factor in the reality that most of these support multiple inference modes, context caching strategies, and reasoning levels.

My org gives me a fixed monthly credit allocation across three providers. Blow through it too fast and I'm stuck with free-tier rate limits for the rest of the month. Use the wrong model and I waste credits on tasks that didn't need them. Use too cheap a model and I spend two hours debugging hallucinated code. The cost of the wrong decision compounds.

This isn't a complaint about having too many tools. It's an observation that the AI model landscape fragmented faster than the decision frameworks developers use to navigate it.

What You're Actually Choosing Between (Mid-2026)

Here's what the model landscape actually looks like right now, based on official documentation and verified pricing.

Claude (Anthropic)

Four tiers, all with 1M token context windows except Haiku (200K). Note: Fable 5 is Anthropic's most expensive model (2x Opus pricing) - reserved for the most demanding workloads.

* Sonnet 5 introductory pricing through August 31, 2026. Standard pricing thereafter: $3/$15 per MTok.

Claude supports effort levels (low, medium, high, xhigh, max) that trade speed and cost for quality. Opus 4.8 defaults to high effort; Sonnet 5 defaults to high on API.

OpenAI (GPT-5.6 Series)

Three variants, all with 1.05M context windows:

Google (Gemini)

Multiple tiers with varying context windows:

Gemini offers 50% batch discounts and context caching at ~10% of input cost.

Open-Source (Self-Hosted or API)

The open-source landscape has matured significantly. Here are the most credible options as of mid-2026:

• DeepSeek-R1 (671B total, 37B active per token) - Family of open reasoning models with performance approaching proprietary models. Available in sizes from 1.5B to 671B. MIT licence for code, commercial-use licence for weights. Outperforms many closed-source models on code and maths benchmarks.
• Llama 4 Scout & Maverick (17B) - Meta's latest natively multimodal models with mixture-of-experts architecture. 10M token context window on Scout variant. Requires licence acceptance.
• Llama 3.3 (70B) - Strong instruction-tuned model with 693K downloads on Hugging Face. Text-only, multilingual, trained on ~15 trillion tokens.
• Qwen 2.5-Coder (0.5B to 32B) - Code-specific models with improvements in generation and reasoning. Supports 128K token context.
• Gemma 3 (270M to 27B) - Google's efficient models that run on single GPU. Vision support on larger variants.
• Mistral (7B) - Proven workhorse for local deployment. 31M+ downloads on Ollama.

A Task-Based Decision Model

The insight that made this manageable: classify the task, not the model.

Instead of memorising which model excels at what, I built a task taxonomy with preset model choices. Here's what actually works:

Task Type 1: Mechanical

Examples: Boilerplate generation, test case scaffolding, JSON formatting, regex patterns, docstring writing.

Model choice: Cheapest available - Gemini 3.1 Flash-Lite ($0.25/$1.50 per MTok), Haiku 4.5 ($1/$5), or open-source Gemma 3 4B locally.

Why: These tasks have objective right answers. Speed and cost matter more than reasoning depth. Flash-Lite generates 200 test stubs in under 2 seconds for under a penny. There's no reason to burn Opus credits here.

Task Type 2: Ambiguous

Examples: API design decisions, naming debates, architectural trade-offs, explaining unfamiliar code, writing documentation.

Model choice: Mid-tier balanced models - Claude Sonnet 5 ($3/$15), GPT-5.6 Terra ($2.50/$15), or Gemini 3.5 Flash ($1.50/$9).

Why: These need judgement, not just correctness. The model must understand context, weigh trade-offs, and explain reasoning. Cheap models confidently pick one option without explaining why. Expensive models overthink it. Mid-tier hits the sweet spot.

Task Type 3: Complex Reasoning

Examples: Tracing subtle bugs across multiple files, understanding why a complex algorithm fails on edge cases, reviewing architecture for hidden issues, refactoring tangled legacy code.

Model choice: Top-tier with high effort - Claude Opus 4.8 at high effort ($5/$25), GPT-5.6 Sol ($5/$30), or Gemini 3.1 Pro ($2-4/$12-18). For open-source: DeepSeek-R1 70B. Reserve Fable 5 ($10/$50) only for the most critical, highest-stakes work - it's 2x Opus cost.

Why: This is where cheap models confidently hallucinate solutions that compile but break in production. The cost of a wrong answer here - wasted debugging time, shipped bugs - exceeds the API cost by orders of magnitude. Use an expensive model, but Opus 4.8 is usually sufficient. Fable 5's premium is justified only for multi-hour agent workflows or mission-critical debugging where absolute maximum capability matters more than cost.

Task Type 4: Long Context

Examples: Summarising lengthy transcripts, finding patterns across 20+ files, analysing full git diffs, processing documentation sites.

Model choice: Long-context specialists - Llama 4 Scout (10M tokens, open-source), Claude Opus/Sonnet (1M tokens), Gemini 2.5 Pro (1M+ with extended context), or GPT-5.6 (1.05M).

Why: Context window size matters here more than reasoning capability. For truly massive inputs (multi-million tokens), open-source Llama 4 Scout is the only option. For 1M token range, Claude and Gemini are reliable. Pricing gets complex at scale - context caching can cut costs 90%.

Making Your Budget Last the Month

The framework above assumes unlimited credits. Real organisations give you caps. Here's how to stretch them:

Track by Task Type, Not Calendar Day

Log every API call by task type (mechanical, ambiguous, complex, long-context). At week's end, check the distribution. If 60% of spend is on mechanical tasks, you're overspending. Those should be 10-15% max. Shift them to cheaper models or open-source.

Front-Load Expensive Models Early

Don't ration Opus evenly across 30 days. Use it liberally in week one when complex problems are fresh and your budget is full. By week three, you've already debugged the gnarly stuff. Mechanical and ambiguous tasks dominate. Cheaper models suffice.

Leverage Batch APIs and Caching

Gemini's batch API gives 50% discounts. Context caching (available on Claude and Gemini) charges ~10% of input cost for cached content. If you're repeatedly processing the same large codebase, cache it. A 100K token codebase cached costs $30 once on Sonnet, then $0.30 per subsequent query instead of $300.

Open-Source as Overflow

When API credits run low, shift to open-source. DeepSeek-R1 7B runs locally on a decent GPU and handles most coding tasks competently. Llama 3.3 70B via Ollama is free once you've paid for the hardware. Use API models for the first 80% of your budget, then fall back to local for the final stretch.

The Mistakes That Burn Budgets

Mistake 1: Defaulting to One Model

Using Sonnet for everything because it's "good enough" wastes money on tasks that Flash-Lite could handle. Diversify. Log your usage for a week. You'll find 40% of your Sonnet calls could've been Haiku with identical output.

Mistake 2: Penny-Pinching on Critical Tasks

The inverse mistake: using Flash-Lite to debug a race condition because "it's just a quick question." Cheap models give confident, plausible, wrong answers on hard problems. The API call saves $0.15. The two hours of misdirected debugging costs $100 in opportunity cost.

Mistake 3: Ignoring Open-Source

DeepSeek-R1 outperforms GPT-4o on many benchmarks. Llama 4 Scout handles 10M tokens. Yet most developers never try them because "setup is hard." For recurring tasks - code review, test generation, documentation - the upfront setup cost amortises fast.

Mistake 4: Not Logging

You'll forget which model worked for which task. I keep a plaintext log: date, task, model, effort, cost, outcome. Takes 10 seconds per call. After a month, patterns emerge. Your log becomes your personal benchmark suite.

The 30-Second Decision Tree

When you're about to make an API call:

• Is there an objectively correct answer? (Boilerplate, tests, formatting) → Use cheapest: Flash-Lite, Haiku, or Gemma 3 locally.
• Does it require judgement or trade-off evaluation? (API design, naming, docs) → Use mid-tier: Sonnet 5, Terra, or 3.5 Flash.
• Are you stuck on a genuinely hard problem? (Deep bug, algorithmic edge case, architectural flaw) → Use top-tier: Opus 4.8, Sol, or DeepSeek-R1 70B.
• Is the input massive? (Multi-file diffs, long transcripts) → Use long-context: Llama 4 Scout (10M), Claude/Gemini/GPT (1M+).
• Does it touch money, auth, or user data? → Use Opus 4.8 at max effort, no exceptions. Bugs here are catastrophic.

This handles 90% of decisions. For the remaining 10%, consult your log.

Why Open-Source Models Deserve Serious Consideration

The gap between open-source and proprietary models has collapsed. DeepSeek-R1 (fully open, MIT licence) beats GPT-4o on code and maths. Llama 4 handles more context than any API model. Here's why that matters:

Cost at Scale

For high-volume workloads, open-source is dramatically cheaper. Running DeepSeek-R1 70B locally costs GPU depreciation + electricity. No per-token charges. If you're generating thousands of test cases daily, the hardware pays for itself in weeks.

Privacy and Control

API models send your data to third-party servers. For legal code, unreleased features, or proprietary algorithms, that's a non-starter. Open-source models run in your VPC. You control the data.

Customisation

You can fine-tune open-source models on your codebase. A Llama 3.3 70B fine-tuned on your org's patterns understands your naming conventions, architecture decisions, and code style. API models don't.

No Vendor Lock-In

Anthropic changes Opus pricing or deprecates a model? You're stuck migrating. Open-source models are yours forever. Download the weights, host them yourself, and you're immune to vendor decisions.

This Will Get Worse Before It Gets Better

The model count is accelerating. In six months, there will be 60+ options. The framework above scales because it's task-based, not model-based. New models slot into existing task categories. When GPT-6 or Claude Opus 5 drops, you don't relearn your workflow - you test it on your four task types and see where it fits.

The real solution is automated routing. A tool that ingests your prompt, classifies the task, checks your budget, and routes to the optimal model. That tool doesn't exist yet in a production-grade form. When it does, frameworks like this become obsolete.

Until then: classify tasks, preset models, log outcomes, iterate monthly.

Implementing This

If you take one thing from this, make it this: spend 30 minutes classifying your typical tasks, then preset a model for each category.

Write it down. Stick it on your wall or in a gist. For the next month, use those presets religiously and log every call with outcome. At month-end, look for patterns. Tasks where the cheap model was fine. Tasks where you should've used the expensive one. Adjust presets accordingly. Repeat monthly.

Try at least one open-source model this week. Install Ollama, pull DeepSeek-R1 7B, and give it three real tasks from your work. You'll be surprised how capable it is - and how freeing it feels to have no per-token cost.

The goal isn't perfect model selection. The goal is eliminating decision paralysis so you spend your time coding instead of comparison-shopping AI models at 2am.