DeepSeek vs GPT-6 Luna: Which AI Model Costs Less? (2026)

Short answer, verified October 17, 2026: There is no universally cheapest useful model. At standard short-context, uncached API list rates, GPT-6 Luna and Claude Haiku 5.5 both start at $0.10 per million input and $0.50 per million output tokens for their applicable tiers. DeepSeek V4.1 Flash costs $0.15/$0.60 off-peak and $0.30/$1.20 during its published peak hours. However, the DeepSeek cache-hit input rate can change the ranking, and the cheapest request is not necessarily the cheapest successful task. OpenAI · Anthropic · DeepSeek.

If someone asks, “Is DeepSeek more expensive than Luna?” the correct answer is “Which DeepSeek endpoint, at what time, with how many cache hits and retries?” A model list sorted by input price cannot answer that. Try the interactive comparison chart supplied with this article: set your task's input/output mix, model-call count, cache assumptions and measured task success to see where the ranking flips.

The 2026 price table people keep comparing incorrectly

These are official first-party API list prices, USD per million uncached text tokens on October 17, 2026. They are not subscription-plan allowances or marketplace reseller quotes. Rows are restricted to the named service class, context range, effective date and time conditions. Quality is not represented in this table. More restrictive tiers and different tokenizers make naive comparisons misleading.

Source notes: OpenAI API pricing; Anthropic model pricing; DeepSeek price and UTC schedule; Google Gemini API. Vendor prices can change. In particular, Claude Haiku's higher price tier begins above 100K prompt tokens, OpenAI has separate long-context rates, Gemini 4 Argon's introductory price expires November 30, and DeepSeek's rate varies by UTC time. Recheck before making a procurement decision.

DeepSeek API name and peak schedule: the current Flash model ID is deepseek-flash, serving the DeepSeek-V4.1-Flash-1017 version; the older deepseek-v4-flash alias is still accepted but routes to that version. Pro uses deepseek-v4-pro. DeepSeek peak rates apply from 01:00 to 04:00 UTC and 06:00 to 10:00 UTC, Monday through Friday except Chinese public holidays. DeepSeek documents the IDs and schedule.

The popular phrase “Luna Max” is not an unambiguous first-party API model identifier. This comparison uses the published gpt-6-luna API SKU. A Max subscription plan, app feature, model alias or inference speed tier is not automatically equivalent to an API token rate. Compare invoiceable endpoints, not marketing labels.

The chart that matters: same task, different model bill

Static snapshot for image search; deploy the interactive comparator above for readers to change the token mix.

Define a single task as 20,000 input tokens plus 3,000 output tokens, without caching or retries. That is an example workload, not an average taken from customer logs. Cost per 1,000 identical model calls:

Calculate any row by (20,000 × input_price + 3,000 × output_price) / 1,000,000 × 1,000. This is a price-per-identical-call comparison, not a quality ranking. A reasoning model may generate many more output tokens, including thinking tokens, or require fewer turns to solve the task. The interactive widget lets readers change the token mix and sort the chart without trusting a single canned example.

Counterintuitive result: DeepSeek is not necessarily cheaper than the smallest OpenAI model. On the published uncached price sheet, GPT-6 Luna is cheaper than DeepSeek Flash even off-peak. That says nothing about which one is competent enough for your coding, extraction, or agentic task.

When caching reverses the comparison

The DeepSeek V4.1 Flash off-peak cached-input rate is $0.003 per million cache-hit tokens, versus GPT-6 Luna's $0.01 per million cached input on OpenAI Standard short-context. When nearly all a long prompt is cached, the cheaper input SKU can switch. Meanwhile, output prices remain important. For a sample request with 20,000 cached input tokens, no uncached input, and 1,000 generated tokens:

Important limit: This example assumes the provider actually reports all 20K tokens as cache hits. You cannot assume a cache hit on the first call or on a changed prompt. At larger contexts, compare each vendor's long-context price tier; the widget labels its rate assumptions rather than silently extrapolating.

Why this matters: long-running agents reuse instructions, code snippets and history across many turns. Provider cache accounting is real only if the provider reports an actual hit. Distinguish cache-read tokens from cache writes; Anthropic and OpenAI may charge separately for writes. Also note that Anthropic's newer tokenizer changes the number of tokens for the same text; comparing identical token counts is not necessarily comparing identical work.

A cheaper model can cost more per finished task

Imagine two models working on your own test set. Illustrative measured-like inputs, not published benchmark results:

A looks slightly cheaper if a failed task is worthless but has no cleanup cost. Change the failure cost to a hypothetical $0.05 for reviewing/recovering each failure:

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• A: ($0.004 + 0.60 × $0.05) / 0.40 = $0.085 per success.
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• B: ($0.012 + 0.10 × $0.05) / 0.90 ≈ $0.0189 per success.
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Now B is roughly 4.5 times cheaper in effective economics. This is not an invented benchmark score: it is a sensitivity example with explicit assumptions. Use your own holdout tasks, time estimates and provider usage responses before drawing conclusions.

For a large agent, cost per successful task is a better decision metric than price per million tokens. Count every tool call and failed branch. Your request logs should identify model version, provider, context tier, uncached tokens, cached reads/writes, output including reasoning, retries, final pass/fail, latency and recovery time.

What should you choose?

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• Short extraction or routing: Start by testing GPT-6 Luna, Claude Haiku 5.5 and DeepSeek Flash against a held-out sample. Prefer the lowest passing model, not the cheapest-looking price row.
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• Tool-using workflows and coding: Include GPT-6.1 Sol, Claude Sonnet 5.5, DeepSeek Pro and Gemini 3.8 Flash as candidates. Compare the same harness and prompt budget; stop if a cheaper option breaks tool use or increases cleanup.
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• Difficult investigations: Benchmark at least one frontier model such as Claude Opus 5.5, Gemini 4 Argon or GPT-6 Astra. An expensive model can win on fewer rounds, fewer errors or less human repair; it does not always win.
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• Long context and caching: Recalculate with the actual provider-reported cache reads and context price tier. DeepSeek peak hours, Haiku's >100K pricing and Argon's December increase matter.
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No one should interpret these as independently tested accuracy rankings. We have not run a cross-model quality benchmark for this article. Vendor price statements are verified; recommendations are candidate lists for a controlled trial.

Run a defensible, small comparison yourself

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1. Select 30-50 representative tasks from your product and isolate a private holdout set. Include failures and edge cases, not only happy paths.
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2. Use a frozen agent harness, tools, timeouts, prompts and evaluation rubric; pin provider, model alias and version where possible.
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3. Run every task with the same hard budgets. Capture uncached input, cache reads/writes, all output, retries, latency, errors and final outcome.
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4. Calculate total money paid / verified successes. Include human cleanup on failed attempts and tool costs when they differ.
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5. Repeat borderline results and report uncertainty; a 30-task sample is not a production quality guarantee. Retest whenever vendors change model aliases or rates.
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There is a free UsageBox cost-per-task calculator for entering your own prices and retry assumptions. If you need usage evidence later, UsageBox LLM cost tracking records numeric usage quantities or app-computed costs so that billing and price logic remain independent; UsageBox does not choose the model or maintain your vendor price sheet.

Related reading

This page answers the current cross-provider price-mix comparison, not the older analysis of cheap agentic APIs such as DeepSeek, Qwen and GLM. If you're running autonomous experiment loops, read the companion minimum autoresearch model guide; if you own GPUs, read the companion quality-adjusted self-hosting analysis. For the underlying framework, see cost per task.

Sources and update policy

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• OpenAI - API pricing (GPT-6 Luna, Sol and Astra Standard rate card).
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• Anthropic - Claude model pricing (Haiku, Sonnet and Opus 5.5, tokenizer and prompt tiers).
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• DeepSeek - Models & Pricing (peak hours, cache hits, Flash and Pro versions).
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• Google - Gemini API pricing (3.8 Flash and 4 Argon introductory dates).
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• UsageBox - Cost per Task calculator (bring your own contractual rates).
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Verified October 17, 2026. Always show the effective date and rate assumptions alongside any chart; do not silently mutate historical prices after publishing.