Text Models Landscape: From GPT to the Open-Source Wave

Large language models (LLMs) power ChatGPT, Cursor, support bots—almost every AI app. This article maps the text model ecosystem in 2026.

What Text Models Do

text in → model → text out

Capabilities:
  generate   articles, code, email
  understand summarize, classify, sentiment, translate
  reason     math, logic, multi-step analysis
  tools      function calling → APIs, commands
  persona    follow system prompts

Core mechanism: next-token prediction (see fundamentals #02). Everything else follows.

Mainstream Models (2026 Reference)

Model	Vendor	Context	Strengths	Price (input/1M tokens)
GPT-4o	OpenAI	128K	general + tools	~$2.5
Claude 3.5 Sonnet	Anthropic	200K	code, long text, safety	~$3
Gemini 2.0 Flash	Google	1M	huge context, multimodal	~$0.1
DeepSeek V3	DeepSeek	128K	cost, Chinese, code	~$0.27
Llama 3.3 70B	Meta	128K	top open weights, self-host	free (self-host)
Qwen 2.5 72B	Alibaba	128K	Chinese, open, many sizes	free (self-host)

Pricing shifts fast—verify vendor pages when choosing.

Closed vs Open

Closed (API)

Pros: strongest models, zero ops, continuous updates
Cons: data residency, vendor lock-in, linear cost at scale

Examples: GPT-4o, Claude, Gemini
Best for: fast MVP, strongest capability, moderate privacy needs

Open (Self-Host)

Pros: data stays local, predictable cost at volume, fine-tunable
Cons: GPU ops, slightly behind frontier closed models

Examples: Llama 3, Qwen, DeepSeek weights, Mistral
Best for: regulated industries, high volume, customization

Real Choices

Cursor: multi-model (GPT-4o, Claude, custom)
Notion AI: OpenAI early, added cheaper/open models later
Bank support (CN): Qwen on-prem, data never leaves network

Real Capability Boundaries

Genuinely strong	Looks strong but isn’t
Code generation/explanation	Exact arithmetic (use calculator tool)
Summarization, translation	Live facts (training cutoff)
Structured JSON output	100% factual accuracy
Major languages	low-resource languages/dialects
Complex instructions	very long chains without error

Model Selection Tree

Need top general capability?
  yes → GPT-4o or Claude 3.5 Sonnet
  no ↓
Need context > 200K?
  yes → Gemini 2.0 Flash
  no ↓
Must keep data on-prem?
  yes → Qwen 2.5 / DeepSeek V3 self-host
  no ↓
Cost-sensitive, high volume?
  yes → DeepSeek V3 API
  no ↓
Chinese-optimized?
  yes → DeepSeek V3 or Qwen
  no → Llama 3.3 or GPT-4o-mini

What’s Next

Reasoning models (o1, R1): more compute at inference → better math/logic
Agent-native models: built-in planning and tool use
On-device small models: 3B–8B on phone/PC
MoE (DeepSeek V3, Mixtral): scale capacity, control inference cost

References

Summary

Text models are the brain of AI apps. 2026 = closed frontier + open catch-up + reasoning models rising. No universal winner—match scenario, budget, and data constraints.