Start with GPT-6 Luna and Gemini 3.8 Flash for cost-sensitive work; evaluate Gemini 4 Argon, GPT-6.1 Sol, and Astra as separate escalation candidates. This refresh replaces the earlier GPT-5.6 and Gemini 3.7 shortlist, rather than treating legacy models as today’s default.
The chart, calculator, and price table above use maintained pricing data. They compare base token charges, not guaranteed costs per successful task. See OpenAI pricing and Google AI pricing for the wider catalogs.
Which model should you choose?
| Workload | First candidate | Decision rule |
|---|---|---|
| High-volume extraction and classification | GPT-6 Luna | Keep it if structure and accuracy pass your acceptance checks |
| Cost-sensitive Google-centered apps | Gemini 3.8 Flash | Compare quality and integration effort against Luna |
| Complex Google reasoning and agents | Gemini 4 Argon | Evaluate paid API access and both introductory and standard-rate budgets |
| Complex OpenAI coding and tools | GPT-6.1 Sol | Escalate only when it reduces failures or reviewer time |
| High-value specialist tasks | GPT-6 Astra | Require a measurable gain over cheaper candidates |
| Pricing data: How we verify → | ||
Choose Gemini when its output wins on your actual documents, your existing Google deployment reduces migration work, or a second provider improves resilience. Argon now has a stable paid Gemini API endpoint; it is not a free-tier model, and project quotas and eligibility still apply.
Choose GPT-6 when Luna passes at the lowest token cost or your OpenAI prompts, tools, and deployment controls materially reduce implementation risk. Do not assume Astra is the economical default simply because it is a premium model.
The feature comparison is operational, not a claimed benchmark ranking:
| Decision factor | Gemini shortlist | OpenAI shortlist |
|---|---|---|
| Budget starting point | Gemini 3.8 Flash | GPT-6 Luna |
| Reasoning escalation | Gemini 4 Argon | GPT-6.1 Sol, then evaluated Astra |
| Important billing distinction | Argon introductory deadline and implicit-only caching | Long-context tiers and cache-write charges |
| Production gate | Project access, quotas, and passing task evaluations | Account access, quotas, and passing task evaluations |
For the Google launch details and a data-generated current-versus-scheduled table, read Gemini 4 Argon pricing and access. For another provider control, see GPT-6 Luna vs Claude Sonnet.
Compare the whole bill
Run two calculator scenarios
In the inline calculator, enter 10 million input tokens and 2 million output tokens per month for an extraction-heavy service. Then try 2 million input and 10 million output for a generation-heavy service. The changing totals show why output-heavy applications need a different budget from retrieval-heavy ones.
These are workload examples, not quality-equivalent forecasts. Models can tokenize the same documents differently and consume different amounts of reasoning output. The full token calculator supports a broader comparison; check its supported settings before treating a result as an invoice estimate.
Reprice Google traffic across the introductory deadlines
Google’s official pricing catalog publishes Argon’s introductory rates through November 30, 2026, and higher standard rates starting December 1. The chart above uses current rates, not the future schedule; a pilot that looks affordable now still needs a steady-state budget.
The same official catalog lists Gemini 3.8 Flash’s current rates through December 31, 2026, with higher standard rates starting January 1, 2027. The current Flash estimate is not a forecast of next year’s bill; consult Google’s dated schedule for that period.
Argon supports implicit caching, not explicit caching. Its output billing includes thinking tokens, and grounding can add separate charges. Do not budget every repeated prompt as a guaranteed cache hit or treat a Google AI subscription as API credit.
Separate context capacity from billing tiers
OpenAI’s official API pricing distinguishes base, long-context, batch, and other service rates. For the shortlisted GPT-6 routes, prompts above 272,000 input tokens cross the documented long-context threshold. A large context window does not mean every request uses the headline price.
The base chart and inline calculator do not include every cache-write, tool, or service-tier charge. Separate long prompts from ordinary requests, log cache writes as well as hits, and include retries and fallbacks. The context-window guide explains this distinction.
Buy accepted results, not cheap attempts
Score the same representative tasks for accuracy, valid structure, tool success, latency, and reviewer time. Divide the complete bill by accepted results, including failed attempts in the numerator. Neither a lower token rate nor a provider benchmark proves which route wins your application.
Managed open-model control: Add one alternative so the evaluation is not limited to two providers. Check Novita’s current model catalog for a suitable hosted control; this is not a Gemini or GPT endpoint.
Affiliate disclosure: we may earn a commission through the sponsored link, at no extra cost to you. Official Google and OpenAI pricing sources checked October 18, 2026; pricing tables remain data-driven. No new independent model benchmark is claimed.
FAQ
Is Gemini cheaper than GPT-6?
Not across every tier or workload. Luna has the lowest base token rates in this shortlist, but compare the live totals for models that pass your tasks.
Is Gemini 4 Argon available through the API?
Google documents a stable paid Gemini API endpoint, subject to project eligibility and quotas. It is not available on the Free tier.
Will this Argon estimate apply in December?
The current chart and calculator use introductory rates. Use the linked Argon scheduled-rate table to budget traffic from December 1, 2026.
Should I switch providers for lower token prices?
Only after accounting for accepted-output quality, retries, tool charges, migration work, and latency. Keep the existing provider when those costs exceed the verified saving.