The LLM landscape: choosing a model

Start from the job, not the leaderboard

New models arrive every few weeks, and rankings change faster than you can build. The durable skill is a method for choosing, not a memorized list. We give you both: criteria and tiers that last, and a dated snapshot that will age.

A note on neutrality

Vendors in this module are listed alphabetically, and we don't rank them. Third-party trackers disagree with each other, and any ranking would be stale within weeks. Use official documentation for current specifications and prices.

Criteria that matter for assistants

Criterion	What to ask
Quality on your tasks	Does it pass your evaluation set: your policies, tools and tone?
Tool-calling reliability	Does it choose the right tool and fill arguments correctly?
Instruction following	Does it obey scope and refusal rules over many turns?
Grounding	Does it stay within retrieved sources and say "I don't know"?
Latency	Time to first token and speed afterward. Critical for voice.
Cost	Price per resolved conversation, with caching and routing included.
Context window and long-context quality	Not just the size limit, but whether it uses long context well.
Languages	Quality in each language you serve, tested, not assumed.
Data handling	Retention, training use, regional processing, certifications.
Stability	Rate limits, uptime, deprecation policy, version pinning.

Try it: a model-tier chooser

This small tool applies the reasoning above. It suggests a tier of model for your situation. It doesn't name a product, because the right product depends on your tests.

Typical task

Latency need

Data sensitivity

Volume

Tiers and routing

Most providers offer a family of models at different sizes. Think in tiers:

Tier	Strengths	Use for
Flagship / frontier	Best reasoning, tool use and long tasks	Hard turns, planning, agents, evaluation judging
Mid-tier	Strong all-rounder at a lower price	A sensible default for many assistants
Small / fast	Very low cost and latency	Classification, routing, simple answers, voice
Open-weight	You host it, you control data and cost	Privacy-sensitive or very high volume
On-device	Runs locally, works offline	Edge and privacy cases

A router sends each turn to the cheapest tier that can handle it, and escalates when confidence is low.

User messageSmall model: classify and score difficultyEasy: small model answersHard: flagship model answersValidate and respond

A simple routing design. The classifier is cheap, so the savings accumulate.

Measure the router itself: how often it escalates, and how often an easy-labeled turn fails. Many teams also add prompt caching for the system prompt and shared context to cut cost and latency, where the provider supports it.

Proprietary or open-weight?

	Proprietary API	Open-weight, self-hosted
Time to start	Minutes	Days to weeks
Peak quality	Usually highest	Closing the gap, varies by task
Data control	By contract	Full, inside your boundary
Ops burden	Provider's	Yours: GPUs, scaling, updates
Cost shape	Pay per use	Fixed infrastructure plus engineering
Licensing	Terms of service	Read each model's license carefully

Many teams use both: a proprietary model for the hardest turns and an open-weight model for high-volume, sensitive or simple ones.

Cost: think per resolved conversation

text

cost per resolved conversation =
  (model cost + retrieval cost + tool and infrastructure cost + human escalation cost)
  / resolved conversations

A cheap model that needs three retries, or escalates twice as often, may cost more than a pricier model that solves the problem first time. Include humans in the equation, because escalations are the most expensive turn of all.

Snapshot: the families you will meet (October 27, 2026)

This table will age

It was compiled on October 27, 2026 from vendor release notes and independent trackers. Names, versions and prices change often, and sources disagree. Confirm everything in the official docs linked below the table.

Vendor	Families you will meet	Often chosen for
Anthropic	Claude Opus 5.5, Sonnet 5.5 and Haiku 5.5, plus a higher tier called Fable 5.1	Tool use, long agentic tasks, careful instruction following; tiers from fast to flagship
Google	Gemini 4 Argon (released October 13, 2026), alongside Flash tiers such as Gemini 3.8 Flash	Multimodal work, long context, Google Cloud integration
OpenAI	The GPT-6 family, with several variants and 6.1 releases through October 2026	Broad ecosystem, general assistants, coding
Open-weight	DeepSeek V4 family, Kimi K3, Qwen 3.x, GLM-5.2, Gemma 4	Self-hosting, data control, cost at scale

Independent trackers in October 2026 rank these differently and often disagree on prices for the same model. That is the lesson: use them to build a shortlist, then decide with your own evaluation set.

Operate for change

Pin versions. Avoid "latest" aliases in production.
Keep a fallback. Test a second model from a different provider so an outage or deprecation is a configuration change.
Track deprecations. Subscribe to provider notices and calendar the retirement dates.
Gate every change. Run your evaluation set (module 14) before swapping a model or editing a prompt.
Log the model version with every conversation so you can explain behavior later.
Re-tune prompts per model. A new model usually needs a new prompt pass (module 5).

In short

What you will learn