LLM Comparison: Any Two Models, Side by Side

Why compare models on RankLLMs

What the engine puts side by side

RankLLMs Overall Score

The 0-100 composite blending every capability pillar into one comparable number.

Reasoning

GPQA Diamond and MATH-500 - formal logic, science, and competition mathematics.

Coding

SWE-bench Verified: resolving real GitHub issues end to end, the closest proxy for engineering work.

Agentic tool use

OSWorld desktop tasks and multi-step tool invocation - autonomy under real friction.

Code Arena Elo

Blind head-to-head coding battles rated by human voters - the community verdict.

Inference speed

Steady-state tokens per second on standard streaming endpoints.

API price per 1M tokens

Blended cost at a 3:1 input-to-output ratio, sourced from official pricing pages.

Context window

Maximum input per session - how much codebase or document fits in one call.

License

Proprietary API terms versus open weights you can self-host.

Global rank

Where each model sits on the live leaderboard right now.

Who runs comparisons here

1. 01

   Engineers picking a production API

   You have a workload and a budget. Compare coding accuracy against price per million tokens and pick the model that solves your tasks for the least money.

2. 02

   Teams watching inference spend

   A migration is a cost decision before it is a capability decision. See exactly what a switch saves - or costs - before touching a line of routing code.

3. 03

   Researchers and analysts

   Track how the frontier moves: reasoning vs coding gaps, open-weights vs proprietary closures, and where speed still lags capability.

4. 04

   Builders choosing open weights

   Compare licenses, context windows, and self-hostable performance against the APIs you would replace.

The engine gives you numbers; these deep dives give you decisions. Human-written matchups with real workloads, cost-per-solved-task math, and a named winner per use case.

Latest Comparison

September 4, 2026

Qwen 2.5 7B vs Llama 3.1 8B: Full Benchmark Comparison and Verdict

Qwen2.5-7B-Instruct vs Llama 3.1 8B Instruct on official benchmarks: HumanEval 84.8 vs 72.6, MATH 75.5 vs 51.9, GSM8K 91.6 vs 84.5. Full comparison, VRAM requirements, and which one to still pick in 2026.

8 min readRead comparison

1. 02Muse Spark 1.3 vs Gemini 3.8 Flash: Which Model Is Better for Coding and Agents?Sep 3 • 11 min read→
2. 03Why GLM-5.3 Can Beat Bigger Models: My Take on GLM-5.3 vs Kimi K3 vs Qwen3.8-MaxSep 3 • 10 min read→
3. 04GLM-5.3 Flash vs Muse Spark 1.2 Contributor: The Better Value for Command CodeSep 2 • 9 min read→
4. 05DeepSeek V4 Pro 0813 vs GLM-5.3: Frontier Agent Benchmark & Architecture ComparisonAug 17 • 6 min read→
5. 06ZCode vs DeepSeek Harness: Agentic IDE vs Modular Open-Source FrameworkAug 17 • 7 min read→
6. 07DeepSeek V4 Flash vs GPT-5.6 Luna: Benchmarks & CostAug 10 • 16 min read→
7. 08Muse Code vs Claude Code: Which AI Coding Agent Is Best?Aug 10 • 19 min read→

How to compare AI models (and what the numbers mean)

Most LLM comparisons fail for the same reason: they compare leaderboard positions instead of workloads. A model that wins coding benchmarks can lose your specific coding task, because benchmarks measure different things. Work through four questions in order. First, what is the task - issue resolution, live terminal work, research synthesis, or document analysis? Each maps to a different benchmark (SWE-bench for the first, Terminal Bench for the second, BrowseComp for the third). Second, what is the accuracy gap worth in money? A four-point SWE-bench difference sounds large until you price it: in our best coding LLM analysis, the most expensive model costs nine times more per solved task than a model scoring 3.5 points lower.

Third, are the numbers from the same date and the same benchmark version? A 49% score from 2024 and a 49% score from 2026 are not the same achievement, and SWE-bench Verified versus SWE-bench Pro scores are never interchangeable (see our SWE-bench Pro guide). Fourth, is the evaluation harness disclosed? Agentic benchmarks depend on scaffolding, step budgets, and tool access - a score without those details is marketing, not measurement.

What a trustworthy comparison includes

• Dated benchmark versions. Every score names its benchmark, version, and snapshot date. Undated scores are the number one red flag in AI model comparison content.
• Stated cost assumptions. Cost-per-task math is only honest when the token workload is written down. Ours assumes ~12K input and ~3K output tokens per agentic request; adjust for your workload.
• A named winner for a named job. "Both are great" is not a verdict. A real comparison says which model fits which workload, and why.
• Losses acknowledged. The winning model's weaknesses appear in the text, not in a footnote.
• Update discipline. When the market moves, the page changes and the change is dated - see the methodology for how scores are verified and revised.

LLM comparison FAQ

1. 01

   What is the best way to compare LLMs?

   Match benchmarks to your workload, then normalize by cost. Coding tasks map to SWE-bench and Terminal Bench, research tasks to BrowseComp, desktop automation to OSWorld. Divide the token price by benchmark accuracy to get cost per solved task - our coding comparison walks through the full calculation.

2. 02

   How do I compare two specific models myself?

   Use the engine at the top of this page: select any two models and every benchmark pillar, speed measurement, context window, and price appears side by side from the same verified dataset. The URL updates so you can share the exact matchup.

3. 03

   Which LLM comparison should I trust?

   Ones that pass the checklist above: dated benchmark versions, disclosed cost assumptions, a specific verdict, acknowledged losses, and visible update history. Distrust any comparison without dates - in a market where flagship models ship every three to five months, an undated comparison is indistinguishable from a wrong one.