The Tool Desk
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To query multiple large language models (LLMs) with one prompt, use a comparison tool that sends identical instructions to several models, displays their answers together, and records quality and operating data. OpenRouter is the simplest browser-based starting point; LiteLLM is useful when you need synchronized settings and telemetry; Google LLM Comparator and WhichLLMs suit repeatable evaluation; MCP tools such as Evvl bring comparisons into coding assistants. Do not choose a permanent “best” model from one impressive answer. Test representative tasks, inspect the raw outputs, and weigh correctness, instruction following, safety, latency, token use, cost and consistency.
What does an LLM comparison tool do?
An LLM comparison tool submits the same task to two or more models and places the responses in a common view. That removes the friction of switching browser tabs or rewriting prompts, so you can compare differences in reasoning, factual coverage, tone, formatting and refusal behavior on the exact work you care about.
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Most tools are evaluators rather than truth machines. OpenRouter warns that responses are AI-generated and can be inaccurate, so every answer needs review before you rely on it. A comparison is useful when it exposes trade-offs; it is not proof that one model is universally superior.
Which tools can query several LLMs side by side?
The right option depends on whether you want a quick browser test, measurable API experiments, blind judging or an evaluation workflow inside another application.
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| Tool | How it compares models | Documented scope or limit | Useful evaluation features | Billing or sharing details |
|---|---|---|---|---|
| OpenRouter Chat playground | Select one or more models, send one message and read the answers side by side. | 500+ AI models in the interface; the page names OpenAI, Google and Anthropic among its providers. | Fast browser-based qualitative comparison. | Responses are AI-generated and may be inaccurate; review outputs before relying on them. |
| LiteLLM Model Compare Playground | Runs synchronized prompts and settings against configured models. | Up to three models at once in the documented UI; available in v1.80.0-stable and later. | Guardrail tests, time to first token, input/output/reasoning tokens, total latency and cost when tracking is enabled. | Designed for gateway and self-hosted workflows; model charges depend on the providers you configure. |
| Google LLM Comparator | Analyzes side-by-side evaluation results in a web app or companion Python library. | Built for scalable analysis rather than a fixed browser model count. | Slice results, summarize themes behind policy-compliance differences, and inspect output patterns with built-in or user-defined comparison functions. | Useful when you need repeatable analysis of an evaluation set. |
| Evvl | Runs comparisons through OpenRouter from MCP clients. | 200+ models through its documented OpenRouter workflow. | Works with Claude Code, Codex, Copilot, Cursor and other MCP clients; produces share links. | The service is free, while OpenRouter bills model usage; share links expire after seven days. |
| Try That LLM | Displays responses from many models for the same prompt. | 100+ models advertised on its product page. | Shows tone, reasoning, cost and quality differences; optional AI judges; can automatically test newly released models. | Useful for prompt regression checks and release monitoring. |
| Krater | Supports manual or blind comparisons so you can judge answers without seeing model names first. | 500+ models advertised. | Blind testing reduces brand expectations; the service recommends two to four models for readable practical comparisons. | Each selected model is billed at its normal token cost. |
| WhichLLMs | Open-source application for prompt datasets and reusable evaluations. | Live model discovery rather than a fixed catalog count. | Judged scoring, latency and API-cost metadata, reusable answers and JSON export. | Its README says generation and judging use an OpenRouter key. |
How do I run a fair multi-LLM comparison?
Start with a real decision, not a collection of random prompts. The following process produces evidence you can repeat after changing a model or prompt.
- Define the task and acceptance criteria. Write down what a successful answer must contain, what errors are unacceptable and how you will handle safety-sensitive content. For a coding task, criteria might include passing tests, compatibility with a specified version and adherence to an API contract. For writing, specify audience, length, structure and prohibited claims.
- Choose a representative prompt set. Include several examples from the workload rather than one showcase question. A practical minimum can include a coding task, a factual-synthesis task and a constrained-writing task; add edge cases and failure-prone examples that your users actually encounter.
- Freeze the inputs. Use the same user prompt, system instructions, context documents, temperature and output limits for every model wherever the tool permits. LiteLLM provides a setting-synchronization control for this purpose. Record the model identifier and settings so a later run is comparable.
- Run enough trials to expose variation. A single generation can be unusually good or bad. Repeat prompts when the service allows it, especially for tasks affected by sampling. Keep the prompt text and supplied context unchanged between trials.
- Inspect the raw answers. Read for correctness, omissions, unsupported assertions, instruction following, tone, formatting and safety behavior. Do not accept an aggregate score without examining examples that produced it.
- Capture operational metrics. Record time to first token, total latency, input tokens, output tokens, reasoning tokens when exposed, per-prompt cost and projected cost at your expected volume. LiteLLM explicitly exposes these measurements when tracking is enabled.
- Score against your criteria and make a decision. Separate hard failures from preferences. A model that is slightly less eloquent but consistently correct may be a better default than one that occasionally produces a brilliant answer and occasionally violates a requirement.
- Repeat after changes. Re-run the set after a prompt edit, model upgrade, context change or safety-policy change. Treat the evaluation as a regression test, not a one-time contest.
What should I measure besides answer quality?
Quality is task-specific, and operating characteristics determine whether a model is practical in production. Track each dimension separately instead of hiding everything in one score.
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Check factual claims against an authoritative reference, verify calculations and run generated code through your normal tests. Mark omissions explicitly: an answer can be fluent yet fail because it left out a required constraint or edge case.
Instruction following and format
Test exact requirements such as valid JSON, a word limit, a prescribed heading structure or a ban on certain content. Count malformed outputs as failures when downstream software cannot consume them.
Safety and policy compliance
Include benign requests that should be answered and sensitive requests that should be refused or redirected. Google’s evaluation workflow is designed to help identify where policy-compliance behavior differs and to summarize themes behind those differences.
Latency and responsiveness
Time to first token affects perceived responsiveness, while total latency affects throughput. Measure both under the same context length and output limit; a model that appears fast on a short prompt may be slower on your real workload.
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Separate input, output and reasoning-token usage when the tool reports them. Calculate cost per successful task, not just cost per request: retries, human correction and failed outputs can make a nominally cheap model more expensive overall.
Consistency
Run multiple examples from each task category and look for variance. A model that wins one prompt but fails a neighboring case is a risky default, even if its average score looks attractive.
Guardrail and parameter behavior
Check whether safety settings, temperature, tool permissions and output limits are actually honored. LiteLLM’s comparison playground includes guardrail testing and synchronized settings, which helps reveal configuration differences before deployment.
Should I use a browser playground, an API evaluator or an MCP tool?
Use a browser playground for a quick qualitative decision
OpenRouter’s chat playground is the shortest path when you want to paste a prompt, select several models and inspect answers immediately. Its catalog covers 500+ models, so it is useful for exploratory comparisons. Because the interface is optimized for reading responses, exportable, repeatable scoring may require a separate process.
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Use LiteLLM for synchronized settings and telemetry
Choose LiteLLM when configuration parity and operational data matter. The documented Model Compare Playground compares up to three configured models, synchronizes settings, runs guardrail tests and can show first-token time, token counts, total latency and cost. The limit applies to that UI and is documented for v1.80.0-stable and above.
Use Google LLM Comparator for analysis at evaluation-set scale
Google’s web application and Python library are aimed at analyzing many side-by-side results rather than manually browsing one prompt at a time. Slicing lets you find the task categories where models diverge, while built-in or user-defined comparison functions help inspect output patterns and policy differences.
Use Evvl when comparisons belong inside an MCP client
Evvl connects MCP clients such as Claude Code, Codex, Copilot and Cursor to comparisons through OpenRouter. It reaches 200+ models, lets you share a result, and keeps those share links for seven days. The service itself is free, but the model calls remain subject to OpenRouter usage billing.
Use Try That LLM for prompt and release regression checks
Try That LLM advertises comparisons across 100+ models and adds views for tone, reasoning, cost and quality. Optional AI judges can help triage a large prompt set, and automatic testing of newly released models is useful when you want to detect changes without manually rebuilding every comparison.
Use Krater for blind human judgments
Krater supports manual and blind modes across a catalog advertised at 500+ models. Blind mode hides model identity while you score the response, reducing the risk that a familiar provider name changes your judgment. Each selected model incurs its normal token cost, so two to four models is a practical range for readable, affordable tests.
Use WhichLLMs for an open-source, exportable workflow
WhichLLMs supports live model discovery, prompt datasets, judged scoring, latency and API-cost metadata, reusable answers and JSON exports. Its README says both generation and judging use an OpenRouter key, making it suitable when you want a local or self-managed evaluation process built around that gateway.
How many models should I compare at once?
Compare enough candidates to cover the decision, but not so many that reviewers stop reading the answers. LiteLLM’s documented interface caps a run at three models, while Krater says practical comparisons usually involve two to four. A larger catalog, such as OpenRouter’s 500+ models, describes what is available to select; it does not mean you should run every model on every prompt.
A staged approach is more efficient: screen a small group on the full prompt set, then run deeper latency, cost and safety checks on the finalists. Keep a known baseline in every run so a new candidate is compared with a model you already understand.
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Record the price of each model call under the provider’s normal token billing and include retries or judge calls in the total. Krater states that selected models are billed at their normal token cost, and Evvl notes that OpenRouter bills usage even though Evvl is free as a service. WhichLLMs can expose API-cost metadata when configured with an OpenRouter key.
Project cost from your own workload: multiply the measured input and output usage by expected request volume, then add the cost of failed generations, human review and any automated judging. A lower per-token price is not a saving if the model’s omissions force repeated calls or extensive correction.
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Why can two models produce different answers to the same prompt?
Models differ in training, system behavior, safety policies, context handling and sampling, so identical instructions can produce different conclusions, formats and refusal decisions. Provider-side updates can also change behavior over time. That is why a comparison should record the exact model identifier and date of each run.
Do not infer general capability from one category. Krater’s warning is apt: “A model that wins your haiku test might lose your code review.” Your evaluation set should mirror the mix of tasks for which you are choosing a default.
How can I avoid biased or misleading comparisons?
- Keep prompts identical. Do not give one model extra context, a longer output limit or a more favorable system instruction.
- Hide model names when possible. Blind mode, as offered by Krater, reduces expectation effects during human scoring.
- Separate judging from generation. If an AI judge scores responses, preserve the raw answers and inspect disagreements; a judge can share the same blind spots as the models it evaluates.
- Use objective checks. Run code, validate JSON, compare factual claims with references and test safety cases instead of relying only on prose quality.
- Report uncertainty. Show how many prompts and trials produced the result, and distinguish a small preference from a decisive failure-rate difference.
- Re-test after changes. Prompt edits, model revisions and provider policy updates can reverse a previous ranking.
What are common failure modes and fixes?
The outputs are not actually comparable
Symptom: one model received a different context window, temperature or output limit. Fix: copy the complete system and user messages, synchronize supported settings and log any parameter that a provider does not expose.
A fluent answer receives the highest score despite factual errors
Symptom: reviewers reward tone while missing unsupported claims. Fix: add reference-backed factual checks, require citations or evidence where appropriate, and score correctness separately from style.
The average score hides a dangerous edge case
Symptom: a model performs well on routine prompts but fails a sensitive or high-impact case. Fix: report hard-failure rates and keep safety or policy tests as a separate gate rather than folding them into an overall average.
The comparison becomes too expensive or unreadable
Symptom: dozens of models generate long answers that nobody reviews. Fix: screen with two to four candidates, shorten exploratory outputs, and reserve full-length runs for finalists.
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Results cannot be reproduced
Symptom: a later run uses a changed prompt or an unrecorded model revision. Fix: store prompts, system instructions, model IDs, settings, timestamps, raw outputs and scoring rules. WhichLLMs’ reusable answers and JSON exports can help preserve an evaluation record.
A shared result exposes sensitive information
Symptom: a share link contains private prompts or customer data. Fix: remove confidential material before sharing, use synthetic test records, and treat temporary links such as Evvl’s seven-day shares as public during their lifetime.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I choose a default model after testing?
Choose the model that best meets your weighted requirements, not the one with the most impressive isolated response. A simple decision sheet can rank hard gates first and preferences second.
| Decision stage | Question to answer | Evidence to use |
|---|---|---|
| Hard gates | Does the model meet safety, factual, format and compatibility requirements? | Failure counts from representative and edge-case prompts. |
| Quality | Which finalist is most correct, complete and faithful to instructions? | Human review plus objective checks and judge explanations. |
| Operations | Can it meet responsiveness and throughput targets? | Time to first token, total latency and token usage under matched settings. |
| Economics | What will successful tasks cost at expected volume? | Provider token billing, retry rate, judge cost and correction effort. |
| Stability | Does performance hold across prompt categories and repeated runs? | Per-category scores, variance and regression results over time. |
Keep the runner-up documented. A fallback model reduces disruption if the primary model changes behavior, becomes unavailable or no longer satisfies a cost or latency target.
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What is the practical answer to “which LLM is best?”
There is no evidence here for a universal winner. The best model is the one that passes your acceptance tests at an acceptable cost and speed for your workload. Side-by-side tools make that decision faster, but only a representative, repeatable evaluation can make it credible.
Frequently asked questions
Can I compare models without writing code?
Yes. OpenRouter’s chat playground, Try That LLM and Krater provide browser-oriented workflows for entering a prompt and reading multiple responses. Code becomes useful when you need repeatable datasets, automated checks, exports or scheduled regression runs.
Does comparing more models make the result more reliable?
Not automatically. More candidates increase token cost and review burden, and a large list can encourage shallow scoring. A smaller, carefully selected group tested on realistic prompts usually produces a clearer decision.
Why should I keep the raw responses if I already have scores?
Scores compress away the details needed to diagnose a failure. Raw answers let you verify whether a low score came from a factual error, a formatting defect, a safety refusal or an inconsistent judge, and they provide examples for improving prompts.
How often should an LLM comparison be repeated?
Repeat it whenever you change the prompt, context, model version, safety configuration or provider. For a stable production workload, schedule periodic regression runs so a silent provider update does not change behavior unnoticed.
Frequently Asked Questions
Can I compare models without writing code?
Yes. OpenRouter’s chat playground, Try That LLM and Krater provide browser-oriented workflows for entering a prompt and reading multiple responses. Code becomes useful when you need repeatable datasets, automated checks, exports or scheduled regression runs.
Does comparing more models make the result more reliable?
Not automatically. More candidates increase token cost and review burden, and a large list can encourage shallow scoring. A smaller, carefully selected group tested on realistic prompts usually produces a clearer decision.
Why should I keep the raw responses if I already have scores?
Scores compress away the details needed to diagnose a failure. Raw answers let you verify whether a low score came from a factual error, a formatting defect, a safety refusal or an inconsistent judge, and they provide examples for improving prompts.
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How often should an LLM comparison be repeated?
Repeat it whenever you change the prompt, context, model version, safety configuration or provider. For a stable production workload, schedule periodic regression runs so a silent provider update does not change behavior unnoticed.
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