[llm] jan 2026

The LLM Landscape: What's Actually Useful in 2026

Breaking down the models that matter—from reasoning models to coding assistants. A practical guide from someone still learning this space.

AS
Abdulla Sajad
Software Engineer // Learning AI/ML
contents
  1. 01 the llm explosion
  2. 02 coding models
  3. 03 reasoning models
  4. 04 running locally
  5. 05 choosing the right model
  6. 06 prompting tips i learned
  7. 07 the reality check
  8. 08 conclusion

1. The LLM Explosion

I'm relatively new to this space. A year ago, I knew GPT-4 existed and that was about it. Now there's Claude, Gemini, Llama, Mistral, DeepSeek, and new models dropping weekly.

It's overwhelming. So I did what any engineer would do: I spent too much time figuring out what actually works for practical use cases.

This isn't a comprehensive benchmark. It's my experience after months of using these models for actual work—coding, debugging, learning, and building things.

The best model isn't the one with the highest benchmark score. It's the one that solves your problem fastest.

2. Coding Models

This is where I spend most of my time. Here's what I've learned:

Claude 3.5 Sonnet / Claude 4

// best for: complex coding, architecture discussions, debugging

My go-to for serious coding work. It understands context better than anything else I've tried. When I paste in a file and ask for refactoring, it actually gets what I'm trying to do.

Pros: Great context understanding, follows instructions well, less likely to hallucinate APIs.
Cons: Rate limits can be annoying, not available in some regions.

GPT-4o

// best for: general purpose, quick questions, broad knowledge

Still solid. I use it when Claude is rate-limited or when I need broader world knowledge. The API is reliable and well-documented.

Pros: Fast, widely available, good ecosystem (Copilot, etc.).
Cons: Sometimes verbose, can be confident but wrong.

DeepSeek V3 / R1

// best for: cost-effective coding, reasoning tasks

The recent hype is justified. DeepSeek R1's reasoning capabilities are impressive for the price. I've started using it for initial debugging passes before switching to Claude for complex fixes.

Pros: Extremely cheap, good reasoning, open weights available.
Cons: UI/UX of various providers varies, newer so less ecosystem.

3. Reasoning Models

The big trend in late 2025/early 2026: models that "think" before answering. They show their reasoning process, and it actually helps.

When Reasoning Helps

  • Complex debugging where the cause isn't obvious
  • Architecture decisions with tradeoffs
  • Math and logic problems
  • Explaining code behavior step by step

When It Doesn't

  • Quick syntax questions
  • Simple code generation
  • When you already know the answer and just need confirmation

Reasoning models are slower and more expensive. Use them for hard problems, not for "how do I center a div" questions.

4. Running Locally

I've spent time running models locally with Ollama and llama.cpp. Here's my honest take:

What Works Locally

  • Llama 3.2 / 3.3: Actually useful for coding assistance on a good machine
  • Mistral: Good performance per parameter, runs on consumer hardware
  • Phi-4: Microsoft's small model, surprisingly capable for its size

The Tradeoff

Local models are convenient for privacy and no rate limits. But even the best local models can't match Claude or GPT-4 for complex tasks. I use them for:

  • Quick autocomplete-style suggestions
  • Working with sensitive code I can't send to APIs
  • Learning how models work (inspecting weights, trying fine-tuning)

Hardware Reality

You need a GPU with decent VRAM. Running quantized 7B models on CPU works but is slow. For actual productive use, you want at least 16GB VRAM for the better models.

5. Choosing the Right Model

Here's my decision tree:

Task Model Why
Complex coding Claude Sonnet Best context understanding
Quick questions GPT-4o mini Fast, cheap, good enough
Debugging hard bugs DeepSeek R1 Good reasoning, cost-effective
Learning/explaining Claude Clearer explanations
Sensitive code Llama (local) Privacy
IDE autocomplete Copilot/Cursor Integration

6. Prompting Tips I Learned

After making every mistake possible, here's what actually works:

For Coding

  • Context matters: Include relevant files, not just the function you're asking about
  • Be specific about constraints: "Use async/await, handle errors, don't use external libs"
  • Explain the goal: "I need to parse CSVs with inconsistent columns" not "fix this parser"
  • Ask for explanations: "Explain why this approach before implementing"

For Learning

  • Use chain of thought: "Walk through this step by step"
  • Ask for analogies: "Explain like I'm a backend dev who knows Java"
  • Request gaps: "What am I missing?" or "What are the edge cases?"

The model can only work with what you give it. Better context = better output.

7. The Reality Check

Models make mistakes. Here's my mental model for when to trust them:

High Trust

  • Explaining code I've already written
  • Generating boilerplate
  • Suggesting patterns for well-known problems

Medium Trust

  • Writing new functions
  • Debugging suggestions
  • Architecture recommendations

Low Trust

  • Specific library versions/APIs (often outdated)
  • Security recommendations (verify independently)
  • Performance claims (benchmark yourself)

8. Conclusion

The LLM landscape moves fast. By the time you read this, there might be new models that change everything I wrote.

But here's what I think remains true:

  • The best model is the one that solves your problem
  • Understanding prompting is more valuable than chasing the newest model
  • Always verify output—models are confident but not always correct
  • Local models are getting better but cloud models are still ahead

I'm still learning this space. Every week I discover something new—better prompting techniques, new tools, different use cases. The key is staying curious and practical.