Question 1

Which LLM is best for coding in 2026?

Accepted Answer

Claude Sonnet 4 and GPT-4o lead for coding tasks in 2026, with Claude excelling at complex multi-file refactoring and GPT-4o at quick completions. For open-source, DeepSeek Coder V3 and Llama 4 are strong alternatives. The best choice depends on your use case, budget, and latency requirements.

Question 2

What is the most accurate LLM?

Accepted Answer

As of mid-2026, Claude Opus 4 and GPT-5 score highest on reasoning benchmarks (GPQA, MATH, HumanEval). For factual accuracy, Claude models tend to hallucinate less. For domain-specific accuracy, fine-tuned models or RAG-augmented systems often outperform general-purpose LLMs.

Question 3

Are open-source LLMs as good as proprietary ones?

Accepted Answer

Open-source LLMs like Llama 4, Mistral Large, and DeepSeek V3 have closed much of the gap with proprietary models. For many production tasks they perform comparably at lower cost. However, frontier proprietary models (Claude Opus, GPT-5) still lead on complex reasoning, long-context tasks, and instruction following.

Question 4

How do I choose the right LLM for my project?

Accepted Answer

Consider: (1) Task type - coding, writing, analysis, or conversation, (2) Context window needs - 8K to 1M+ tokens, (3) Latency requirements - streaming vs batch, (4) Cost budget - per-token pricing varies 100x between models, (5) Privacy - self-hosted open-source vs cloud APIs. Start with a frontier model, then optimize for cost.

Question 5

What is the difference between GPT-4, Claude, and Gemini?

Accepted Answer

GPT-4o (OpenAI) excels at general tasks and multimodal input. Claude (Anthropic) leads in long-context reasoning, safety, and coding. Gemini (Google) offers the largest context window (2M tokens) and strong multimodal capabilities. Each has different pricing, API design, and strengths for different use cases.

#						Context
1st	Claude 4 Opus Anthropic · ~400B reasoningresearch	91.2%	96.2%	98.1%	$15 in $75 out	500K
2nd	Gemini 3 Ultra Google · ~1T (MoE) multimodallong-context	90.8%	91.5%	97.2%	$5 in $20 out	2M
3rd	o1 Pro OpenAI · Unknown reasoningmath	90.3%	92.4%	99.1%	$60 in $240 out	200K
4	GPT-4o OpenAI · ~200B multimodalvision	88.7%	90.2%	95.8%	$2.5 in $10 out	128K
5	Claude 3.5 Sonnet Anthropic · ~70B codinganalysis	88.3%	92%	96.4%	$3 in $15 out	200K
6	DeepSeek V3 DeepSeek · 671B (MoE, 37B active) open-sourcecoding	87.5%	89.6%	94.8%	$0.27 in $1.1 out	128K
7	Llama 4 Maverick Meta · 400B (MoE, 17B active) open-sourcegeneral	87.5%	85.5%	93.7%	Free	1M
8	Grok 3 xAI · ~314B reasoningreal-time	87.5%	88.9%	94.8%	$3 in $15 out	131K
9	Qwen 2.5 72B Alibaba · 72B open-sourcemultilingual	86%	86.7%	95.2%	Free	128K
10	Llama 4 Scout Meta · 109B (MoE, 17B active) open-sourcelong-context	84.8%	78.2%	90.5%	Free	10M
11	Phi-4 Microsoft · 14B open-sourcelightweight	84.8%	82.6%	91.5%	Free	16K
12	Mistral Large 3 Mistral · ~123B open-sourceenterprise	84%	84.2%	91.3%	$2 in $6 out	128K
13	Gemini 2.0 Flash Google · ~8B fastbudget	81.2%	78.4%	89.3%	$0.1 in $0.4 out	1M

Benchmark Leaderboard

ML System Design Interview Workshop

You Just Compared LLMs.
Now Learn to Actually Use the Best One.

ML System Design Interview Workshop

Benchmark Leaderboard

ML System Design Interview Workshop

You Just Compared LLMs.Now Learn to Actually Use the Best One.

ML System Design Interview Workshop

You Just Compared LLMs.
Now Learn to Actually Use the Best One.