LLM Comparison: Choose a Model for Your Actual Work
Benchmark comparisons tell you how models perform on standardized tests. This guide shows how to compare LLMs on the tasks you actually do — writing, research, analysis and coding — and how to keep your evaluation decisions portable across tools.
Updated 5 min read
A comparison of LLMs (large language models) means comparing the AI models that power tools like ChatGPT, Claude and Gemini. The most important comparison is not on published benchmarks — it is on the tasks you actually do. A model that scores well on a coding test may still produce poor output for the specific kind of code your project needs.
Keeping your evaluation results in a place that survives the comparison itself is useful. Tools like fenbs let you record the decision criteria, test results and final choice, so the reasoning is available when you revisit the decision later.
Why benchmarks are a starting point, not a conclusion
Published benchmarks like MMLU, HumanEval and GPQA measure specific, standardized tasks. They are useful for understanding a model's ceiling on known problem types. They are less useful for predicting performance on your actual prompts, your domain vocabulary, and your expected output format.
Models are also evaluated on the benchmarks used to promote them. A model that tops a published ranking may have been fine-tuned on problems similar to that benchmark. Run a personal comparison on your own tasks to get a result that is relevant to your work.
The main models to compare in 2026
| Model family | Provider | Notable strengths | Where to check current specs |
|---|---|---|---|
| GPT-4o, o3, o4-mini | OpenAI | Broad capability; o-series strong for reasoning; large tool ecosystem. | OpenAI's model page at openai.com |
| Claude (Sonnet, Opus, Haiku) | Anthropic | Long context; careful reasoning; strong for editing and analysis. | Anthropic's model page at anthropic.com |
| Gemini (Pro, Ultra, Flash) | Large context window; Google Workspace integration; multimodal. | Google's Gemini page | |
| Mistral (Large, Nemo, etc.) | Mistral AI | European-hosted; strong multilingual; open-weight versions available. | Mistral's model page at mistral.ai |
| Llama (Meta) | Meta (open weight) | Free to self-host; large community; no per-token API cost. | Meta's Llama page at llama.meta.com |
| Gemma, Phi (small models) | Google / Microsoft | Efficient; good for constrained environments; can run on-device. | Vendor model pages |
| fenbs | fenbs.ai | Keeps evaluation decisions, test results and project context across AI apps. | Not a model; pairs with the models above. |
How to run a useful comparison
Choose three to five representative tasks from your actual work. Use the same prompt wording in each model without adjusting it for the model's preferences. Evaluate the output on a consistent set of criteria: accuracy, format, length, tone, and how much editing it needs.
- Pick a short writing task, a research question, and a coding or analysis task from your backlog.
- Run each task in each model with identical prompts.
- Rate the outputs on your criteria before looking at which model produced which answer.
- Record the result with the prompt, the output, and your rating. This gives you evidence for the decision rather than impressions.
- Factor in cost, latency and the integration points you need (MCP, API, IDE plugin).
Comparing by task type
Writing and editing
Claude is frequently preferred for long-form writing and detailed editing. GPT-4o is strong for versatile writing tasks. Both handle most professional writing well; the difference shows on long documents where consistency of voice matters. Try a few paragraphs of your actual document type.
Research and analysis
Gemini integrates with Google Search and can cite current sources. Perplexity is specialized for cited-source research. For analysis of documents you provide, Claude and GPT-4o both handle long inputs well. The quality of analysis depends heavily on how the question is framed.
Coding
OpenAI's o-series models score well on coding benchmarks, particularly for multi-step problems. Claude is strong at reasoning through code bugs and explaining complex logic. For in-editor coding assistance, purpose-built tools like GitHub Copilot, Cursor and Claude Code are usually more useful than a general-purpose chat model.
Structured output and tool use
If your use case requires the model to call tools, produce JSON or follow a strict schema, test this specifically. Models vary in how reliably they follow structured output instructions. The open-source LLMs guide covers models you can fine-tune for specific formats.
Context window size
The context window is the maximum amount of text a model can use in a single session — including the conversation, instructions and documents. Larger is not always better; a model with a large window can still lose track of instructions buried in the middle. Test with a realistic document length, not just the maximum the vendor lists.
Gemini Ultra and Claude have among the largest context windows in commercial models. GPT-4o's context window is large enough for most professional tasks. Check the current model page for exact limits, as these change with model updates.
Cost and access
Costs vary significantly between models and between the API and consumer apps. For consumer apps, most providers have a free tier and paid plans. For APIs, the pricing depends on input and output tokens. Check the current pricing page for each provider; these change regularly.
Try a new model without losing your project context
When you decide to try a different model, connect it to fenbs and ask it to read the project context. The tasks, decisions and Where we left off from previous sessions are there, regardless of which model you used before. If the new model is not the right fit, switch back without rebuilding the context.
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How to do this with fenbs
- Create a free account and add the evaluation as a project.
- Write the evaluation criteria in the project context: what you are testing, the prompts you will use, and the scoring approach.
- Run the comparison and record results in tasks — one task per model or one task per test scenario.
- Record the final model choice in Decisions and rules: which model, for which tasks, and why.
- Connect your chosen model from /integrations and ask it to read the context before working.