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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTo find out whether a self-hosted LLM is cheaper than Claude for your work, run both systems on the same representative tasks, score them against the same quality bar, and measure performance under your expected serving load. Then compare the cost of each successful task—not just API token rates or local tokens per second.
What a fair benchmark needs to answer
A useful comparison answers five separate questions: Does each system complete the task well enough? What does each accepted result cost? How quickly does a request begin and finish? How much work can the system serve at your target load? What hardware and operating effort does the local option require?
These measures are related but not interchangeable. A fast model that fails more often may cost more per accepted task once retries are included. A high-throughput result measured offline may not describe interactive response times. Set the workload and acceptance bar before running either system.
Build a representative, repeatable task set
Choose tasks that reflect your actual use
Use prompts and supporting inputs drawn from the work you expect to run, with realistic input lengths, output requirements, and tool needs. Keep workload families separate when their success criteria differ: code generation, extraction, summarization, and tool use should not be collapsed into one score if they require different kinds of accuracy.
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Write down what counts as success in advance. For example, a code task might require passing specified tests, while an extraction task might require all required fields to be correct. Define any minimum quality score or pass rate that makes an output usable; otherwise, the results can be judged against a bar chosen after seeing which system performed better.
Keep inputs and conditions comparable
Send identical task prompts and supporting material to both systems. Keep system instructions, requested format, context, and available tools as similar as the interfaces permit. Record differences you cannot eliminate, such as a tool or routing feature available to one system but not the other.
For each run, record the prompt set, sample count, date, model and runtime versions, decoding settings, and evaluation method. For the local system, include the checkpoint, quantization, inference engine and version, hardware and memory, context length, batch size, concurrency, and cache state. For Claude, log the exact model identifier and API route, settings, applicable features, geography, and token usage.
Score quality against a declared acceptance bar
Use task-level pass rates alongside a consistent rubric for qualities that matter to the application, such as correctness, completeness, and clarity. Set the rubric dimensions, weights, and minimum acceptable score before reviewing results. The right weighting depends on the use case: a concise answer may matter for one workflow, while complete coverage or factual correctness may dominate another.
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Where practical, have reviewers score outputs without knowing which system produced them. Record who or what judged each output, whether scoring was blinded, and whether a human reviewed the results. If a model grades its own output, disclose that fact rather than treating the score as neutral.
One published example uses correctness (40%), completeness (35%), and clarity (25%), but those weights are not a universal standard. The tps.sh benchmark page, accessed in 2026, declares 21 prompts across seven coding categories, seven models, and 147 tests on an M2 Max with 32 GB of unified memory. It also describes the results as one run and notes that Claude judged Claude in 62 of the 147 scores, a potential source of bias. Those details describe that benchmark, not expected performance on another task set or machine: tps.sh benchmark.
Measure latency and throughput under realistic serving conditions
Measure the complete request at the serving boundary and state precisely how each metric is timed. Useful measures include:
- Time to first token (TTFT): elapsed time from sending a request until the first streamed output arrives.
- Time per output token (TPOT) or inter-token latency (ITL): the interval or average time associated with generating successive output tokens; state the formula used.
- End-to-end latency: elapsed time from request submission to the completed response.
- Throughput: requests or input/output tokens served over time at a stated request rate and concurrency.
Metric terminology can differ between tools, so report the measurement points or formulas rather than relying on labels alone. The vLLM benchmark documentation defines TTFT as the time from sending a request until its first streamed output and discusses variation in metric terminology.
Run the test at the request rate, concurrency, prompt lengths, output lengths, and acceptable latency relevant to your deployment. Report median latency and tail latency—such as p95 or p99—where your sample size supports it. A high-load offline throughput result by itself does not show whether the system feels responsive in interactive use.
Separate cold requests from cache reuse
Decide whether you are measuring cold prompts, warm prefix-cache reuse, or both. Repeated runs against the same server can reuse prefixes and raise measured throughput. vLLM warns about this effect in its benchmarking guidance. For cold-request measurements, reset or restart the relevant cache, or vary prompts or seeds as appropriate; report warm-cache results separately if cache reuse reflects real production traffic.
Calculate cost per accepted task
Use the same task acceptance bar for both systems, then divide each system’s total cost by the number of tasks that meet it. Include retries, extra turns, searches, rereading, or backtracking that are part of completing the work. A cheaper token can still lead to a higher cost per usable result if it needs more work or succeeds less often.
Count local costs under explicit assumptions
Show the accounting boundary rather than presenting electricity as the whole cost. Depending on your situation, local cost may include amortized hardware, electricity, hosting, cooling, maintenance, and operator time. Report at least two scenarios when relevant: using hardware you already own and deploying new hardware or hosting. State what is included in each scenario and the assumptions used to spread fixed costs across tasks.
Capture Claude’s effective API charges on the test date
Use the official Anthropic API pricing page on the day you benchmark and save the relevant rates. Record the date, exact model identifier, route, and tokens by billing category. The schedule distinguishes normal input and output from prompt-cache writes and reads, and may include feature or routing multipliers. Anthropic documents a 1.1× price multiplier for US-only inference for applicable models. Include only the rates and features that apply to the route you actually tested; model names and prices can change.
Use vendor cost examples as context, not as your forecast
Anthropic’s 2026 cost-and-intelligence guidance reports Claude Fable 5.1 costs of $37.94 to $7.12 per task and Claude Sonnet 5 costs of $3.20 to $1.20 per task in cited DeepResearch Bench II runs with and without caching. It also reports 88.6% task success at $0.54 per solved task for Claude Fable 5.1 at low effort, compared with 77.4% at $0.84 per solved task for Claude Sonnet 5 at default effort on a 478-problem SWE-bench Pro subset. Anthropic says those subset scores are not comparable to the public leaderboard. These are vendor-reported results for stated benchmark configurations, not predictions for another workload: Anthropic cost and intelligence guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare the results without declaring a universal winner
Present the systems side by side using the same task set, acceptance threshold, and workload conditions. Include the setup assumptions with the numbers so readers can see what the comparison actually covers.
| Measure | What to report |
|---|---|
| Quality | Task pass rate and rubric scores at the declared acceptance bar; identify the judge and any human review. |
| Cost | Total spend and cost per accepted task, with local accounting assumptions and Claude billing categories. |
| Responsiveness | TTFT, TPOT or ITL, and end-to-end latency, including median and supported tail percentiles. |
| Serving capacity | Throughput at stated request rate and concurrency, with prompt and output lengths and cache condition. |
| Operational burden | Local hardware, hosting, configuration, maintenance, and operator effort included in the test scenario. |
Choose based on the constraints that matter to the deployment. A local model can be the better fit when it clears the required quality bar and its measured cost and responsiveness work under the target load. Claude can be the better fit when its task success, service characteristics, or reduced operating burden outweigh the measured API cost. If neither option meets the acceptance bar, a lower cost or faster response does not make the result usable.
Tools and published studies can help shape what to measure, but they cannot replace a workload-specific test. NVIDIA’s serving guidance frames cost around achieving acceptable accuracy for the use case: NVIDIA NIM and AIPerf performance guidance. A 2026 arXiv preprint evaluates the RTX 5090 and other consumer GPUs across local inference workloads; it can inform configurations worth testing, but does not establish one best GPU for every workload: arXiv consumer GPU study.
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