Simplifying model finding for your specific constraints.
Our Story & Mission
Every single month, dozens of foundation text and image models are released by tech giants and open-source communities. As developers, we found ourselves drowning in a sea of models: Should we use GPT-5, Claude Sonnet, or Gemini Flash? Which is actually cheaper under heavy production loads? Is there any model with low price but high reasoning?
We realized that model decision fatigue was slowing down execution times. General comparison sites just dump tables of numbers on you, leaving you to do the manual mental math.
We built whichllmmodel as a tool that puts you in control. By letting you set custom priority weights for coding capability, cost, reasoning accuracy, and context window size, we evaluate the models dynamically to recommend your perfect matches.
Data Collection & Methodology
To maintain absolute transparency, we document exactly how our scores and comparisons are calculated:
Blended CPM Pricing
For LLMs, we calculate a blended Cost Per Million (CPM) tokens assuming a standard 3:1 ratio of input to output tokens (e.g., 3 input tokens for every 1 output token). This reflects average developer request structures.
Reasoning Accuracy
We use the challenging GPQA Diamond benchmark (graduate-level science and logic) to map general reasoning capabilities. This helps identify models that are capable of complex logical planning and advanced problem-solving.
Local Hardware Limits
We evaluate hardware suitability by comparing model parameter sizes and precision quant formats (FP16, Q8_0, Q4_K_M) against your VRAM and System RAM. We leave safety buffers for operating system overhead, and dynamically calculate memory scaling for KV Cache.
Our Pinky Promise: whichllmmodel is 100% independent. We do not accept sponsorship from model providers to manipulate rankings. Our metrics database is updated regularly from official provider pricing tables and independent benchmark databases.
Coding Capabilities & Developer Guide
Understanding coding benchmarks can be confusing, as different models report scores on different datasets. To make this practical for developers, we segment model coding intelligence into four clear task levels based on their evaluated capability:
1. Complex, large-scale coding tasks
SWE-bench ProWhat it means for developers: The model can behave like an autonomous software engineering agent. It understands complete, complex, multi-file codebases and can edit, run, and debug files across a repository to resolve issues.
Primary Use Cases: Autonomous bug-fixing agents, automated repository migration, large-scale refactoring across multiple files, and complex feature development.
Which one should you choose? Choose this level if you are building or using autonomous developer tools (like SWE-agents) that need to make independent modifications directly inside large, professional-grade code repositories.
2. Real-world coding tasks
SWE-bench VerifiedWhat it means for developers: The model can perform standard coding agent tasks. It is highly capable of reading existing code, locating bugs across files, and writing cross-file updates, but is tested on a verified, cleaner subset of SWE-bench issues.
Primary Use Cases: Standard codebase bug fixing, automated PR reviews and corrections, refactoring smaller multi-component systems, and writing unit test suites.
Which one should you choose? Choose this level if you need a coding assistant that can safely read your files and suggest edits across multiple components of a medium-sized project.
3. Algorithmic problem-solving
LiveCodeBenchWhat it means for developers: The model excels at algorithmic reasoning and scripting new logic in a single-file context. It is tested on fresh coding problems from competitions (like LeetCode, AtCoder) to prevent data contamination.
Primary Use Cases: Writing standalone scripts, generating interactive single-file UI layouts or games, writing isolated algorithmic logic, and prototyping new utility files.
Which one should you choose? Choose this level if you want a fast assistant for day-to-day scripting, algorithmic problems, writing standalone scripts, or prototyping frontend designs.
4. Basic code generation
HumanEvalWhat it means for developers: The model can write basic code snippets and complete functions based on docstrings or prompt descriptions. It excels at syntactical completion.
Primary Use Cases: Auto-completing lines of code, generating boilerplate files, writing simple standalone utility functions (e.g. string formatting, data conversion), and basic scripting.
Which one should you choose? Choose this level if you are seeking a copilot-style assistant that helps you write boilerplate code faster and autocomplete standard functions.
💡 Note on Capability Cascades: Developer capabilities cascade downwards. A model evaluated at Complex, large-scale coding tasks is typically highly proficient at all levels below it; a model at Real-world coding tasks is also excellent at solving algorithmic problems and basic code generation, and so on. Higher tiers require larger and more expensive models, whereas lower tiers can be run on smaller, faster, and cheaper options.
Zubair Tahir
Founder & Lead Developer
Hey, I'm Zubair! I'm a software engineer who loves to build. Let me tell you my story: In the past, when building AI apps or projects, it was hard for me to select a model because there are a lot of options out there. I often wondered if there was a model out there with the exact capabilities I needed, but at a lower price. I wished there was a place where I could see all available models and easily find the best reasoning options without overpaying. If you only know two or three options, your decisions might suffer, but if you can search through multiple options, you're much more likely to find the exact model you need. That's why I built this. If you face a similar problem, you can use it, and it would make me happy!