mario0369/llm-cost-same-prompt

Measured per-call LLM cost — same prompt, every model

Vendors publish prices per million tokens. Nobody publishes what one call actually costs, because that depends on how many tokens the model chooses to emit — and on the same question models differ by more than an order of magnitude. One model finishes a JSON extraction in 23 tokens; another writes 300.

This dataset sends a fixed set of prompts to every model at temperature 0, every night, and records the cost computed from the token usage each provider actually reported. Because the question is identical, the difference is the model, not the workload.

Current results (window: 30 days, as of 2026-08-16)

Median spread across tasks: 43.1x. Clean runs in window: 262.

Two-sentence explanation of a concept (short_answer)

Cheapest: deepseek-v4-flash. The most expensive costs 39.4x more for the same question.

model	USD per call	tokens in / out	latency (ms)	runs
deepseek-v4-flash	0.000104	26 / 133	3072	13
gemini-3.5-flash	0.000625	97 / 71	6931	9
GLM5.2	0.000743	34 / 200	6265	12
gpt-5.6-luna	0.000921	93 / 176	4586	6
claude-sonnet-5	0.001003	26 / 116	14127	13
claude-opus-4-8	0.002319	27 / 111	17257	13
Kimi-K3	0.002560	80 / 197	4322	12
gpt-5.5	0.003168	54 / 123	8967	3
claude-fable-5	0.004100	25 / 98	12168	12

Compress a technical passage into three bullets (summarize)

Cheapest: deepseek-v4-flash. The most expensive costs 43.8x more for the same question.

model	USD per call	tokens in / out	latency (ms)	runs
deepseek-v4-flash	0.000197	178 / 189	3893	13
gemini-3.5-flash	0.001092	176 / 122	6575	3
GLM5.2	0.001612	182 / 400	7999	13
claude-sonnet-5	0.001754	231 / 167	19078	12
gpt-5.6-luna	0.001894	242 / 354	7323	6
claude-opus-4-8	0.004432	233 / 175	22452	13
Kimi-K3	0.004843	229 / 358	6396	12
gpt-5.5	0.005964	203 / 215	10169	3
claude-fable-5	0.008622	230 / 170	9442	10

Extract fields, return JSON only (structured_json)

Cheapest: deepseek-v4-flash. The most expensive costs 43.1x more for the same question.

model	USD per call	tokens in / out	latency (ms)	runs
deepseek-v4-flash	0.000047	55 / 39	2270	13
gemini-3.5-flash	0.000302	54 / 33	5496	2
claude-sonnet-5	0.000346	68 / 29	13828	12
gpt-5.6-luna	0.000588	122 / 102	3883	6
claude-opus-4-8	0.000821	71 / 27	14098	12
GLM5.2	0.001089	62 / 290	5389	13
claude-fable-5	0.001550	67 / 25	9138	12
Kimi-K3	0.001590	109 / 111	3075	12
gpt-5.5	0.002024	56 / 75	7042	2

A routing artefact worth knowing about

Some requests arrive on a channel serving a large cached preamble: sending the single word hi comes back reporting ~4,400 prompt tokens, 3,840 of them cached. It is not stable — the same model and the same question sometimes hits it and sometimes does not, so it is a property of routing, not of the model. Those runs are excluded from the comparison above and reported here instead. They are still in probe_runs.csv (cached_prompt_tokens >= 1000), so you can check the exclusion rule yourself.

model	share of runs hitting it	USD when hit	USD when clean
gemini-3.5-flash	53%	0.005985	0.000679
gpt-5.5	72%	0.019670	0.003930
gpt-5.6-luna	28%	0.004142	0.001134

Method

• Identical prompt per task, temperature: 0, capped max_tokens. The prompts are in tasks.js and are deliberately timeless — they never change, so numbers stay comparable across months.
• Cost = tokens reported by the provider x the price actually paid. Responses without a usage field are discarded, not estimated — an estimate inside a "measured cost" dataset is a lie.
• One run per model per task per night; samples accumulate.
• probe_runs.csv contains every run, including failures and preamble hits, so the published averages can be recomputed from scratch.

What this does not tell you

Cost, not quality. Cheapest here says nothing about whether the answer is good. It also reflects these three prompts specifically — your prompts have a different input/output ratio, and that ratio is exactly what drives the difference. Use it as a starting point for bulk work, then measure your own.

Live version, updated nightly

• Human-readable, per-task pages: https://ainetcafe.com/costs
• JSON API, no key, CORS open: https://ainetcafe.com/api/model-costs
• Raw runs as CSV: https://ainetcafe.com/api/model-costs/raw.csv
• Callable from a bare URL by any agent: https://ainetcafe.com/t/model_costs?days=30

Citation

AI NetCafe real model cost dataset. https://ainetcafe.com/costs (CC BY 4.0)