# GPT-5.4 mini vs Kimi K2 cost > GPT-5.4 mini's input rate sits a quarter below Kimi K2's and it still costs more on most workloads. The two cross at a ratio of 0.17. URL: https://getculpa.com/gpt-5-4-mini-vs-kimi-k2-instruct-0905-cost Last reviewed: 2026-08-02 ## Answer GPT-5.4 mini lists an input rate a quarter below Kimi K2's and still costs more on most real traffic, because its output rate runs half again higher. The two cross at an output-to-input ratio of 0.17. Culpa, a local-first LLM cost, margin, and forecast ledger, measures your own ratio and prices both models against it. ## Why this happens A rate card has two numbers and a shortlist has one column, and that gap is where this comparison goes wrong. Buyers rank models on the input rate because it's the number that reads like a price, and here the model with the cheaper input rate is the dearer one for almost any workload that generates real output. The spread between a model's two rates decides this. Kimi K2 charges three times its input rate on output. GPT-5.4 mini charges six times. A wide spread is a bet that your work is input-heavy, and most work isn't. ## What this usually looks like - A model was shortlisted on its input rate alone, because that was the column in the sheet. - Nobody has compared the two rates as a spread rather than as two separate prices. - The chosen model looked cheaper on paper and the bill didn't follow. - Output volume has grown since the choice was made and the choice has stayed put. ## Common mistakes - Ranking models on the input rate. Why it hurts: The dearer output rate takes over once your output passes a sixth of your input. Do instead: Price both rates against your own volumes. One column can't rank a two-number product. - Reading a wide input-to-output spread as a discount. Why it hurts: A wide spread only pays off on input-heavy work and charges you for everything else. Do instead: Divide output rate by input rate for each model. The narrower spread is the safer default. - Assuming an open-weight model on a fast host must cost more. Why it hurts: Kimi K2 is cheaper here on any workload above 0.17, which covers most products. Do instead: Compare the two totals at your own ratio and let the arithmetic settle it. ## Self-check - Divide your monthly output tokens by your input tokens for the feature in question. - Compare that number against 0.17. Above it, Kimi K2 is the cheaper of the two. - Divide each model's output rate by its input rate. Kimi K2 sits at 3, GPT-5.4 mini at 6. - Price a full month against both rate cards rather than comparing single columns. - Re-check after any prompt change that shortens input or lengthens output. ## The same two models, one workload either side of 0.17 (illustrative) Kimi K2 Instruct 0905 on Groq at real rates of $1.00 per million input and $3.00 output, against GPT-5.4 mini at $0.75 and $4.50, both effective 2026-07-02 and re-verified on the provider pages 2026-08-02. Volumes are modelled. Crossover: ($1.00 - $0.75) / ($4.50 - $3.00) = 0.17 output tokens per input token Chat workload, 100M input and 40M output, a ratio of 0.40 Kimi K2: (100 x $1.00) + (40 x $3.00) = $100 + $120 = $220 GPT-5.4 mini: (100 x $0.75) + (40 x $4.50) = $75 + $180 = $255 Retrieval workload, 100M input and 10M output, a ratio of 0.10 Kimi K2: (100 x $1.00) + (10 x $3.00) = $100 + $30 = $130 GPT-5.4 mini: (100 x $0.75) + (10 x $4.50) = $75 + $45 = $120 The model with the cheaper input rate wins the retrieval workload by $10 and loses the chat workload by $35. The input column ranked these two backwards for the traffic this product actually runs. ## Cost figures Every figure carries its confidence and its source. No figure on this site is provider-reported. - 0.17 — output-to-input ratio at which these two models cost the same [calculated] Source: ($1.00 - $0.75) divided by ($4.50 - $3.00), using real Groq Kimi K2 Instruct 0905 and OpenAI GPT-5.4 mini rates per million from the price book, effective 2026-07-02 and re-verified on both provider pages 2026-08-02. - $120 to $255 — modelled monthly cost across two workloads and two models, cheapest to dearest [estimated] Source: The four totals in the teardown arithmetic at real rates. A range because the token volumes are modelled. ## FAQ Q: Is GPT-5.4 mini cheaper than Kimi K2? A: Only on input-heavy work. The two cost the same when output reaches 0.17 of input, and above that Kimi K2 wins. Retrieval and classification sit below the line. Chat, drafting and code generation sit well above it. Q: Why does the cheaper input rate lose? A: Because the spread between a model's two rates decides the total once output is more than a small fraction of input. GPT-5.4 mini charges six times its input rate on output while Kimi K2 charges three, so mini's advantage runs out quickly. Q: What ratio do real workloads have? A: It varies by task rather than by product. Retrieval and summarisation sit near 0.05 to 0.15, chat near 0.4, and code generation often passes 1.0. Measure the feature rather than the account, because one product usually holds several of these. ## Sources - Groq pricing: https://groq.com/pricing - OpenAI API pricing: https://developers.openai.com/api/docs/pricing Run the free Cost Leak Scan: https://app.getculpa.com/scan?source=pseo&slug=gpt-5-4-mini-vs-kimi-k2-instruct-0905-cost&cluster=model_compare Machine-readable index of every guide: https://getculpa.com/api/pages Human-readable index of every guide: https://getculpa.com/guides Site overview: https://app.getculpa.com/llms.txt Privacy: Culpa runs on your infrastructure. Your prompts and responses never leave it. Culpa counts calls to run your plan, and it fails open, so if it ever breaks your app keeps running.