Pricing Comparison: OpenAI vs Google Gemini pricing - a comparison for AI product builders

OpenAI and Google run the two most-watched rate cards in AI token pricing. Both repriced six times in the last 12 months, which makes this comparison a moving target. Here is where the numbers stand as of July 13, 2026.

OpenAI vs Google at a glance

Takeaway: Gemini is cheaper at every tier today. Neither price list survives a quarter unchanged.

Model overview

OpenAI's current lineup is the gpt-5.6 family, launched July 9, 2026. gpt-5.6-sol is the flagship at $5 / $30, gpt-5.6-terra the mid tier at $2.50 / $15, and gpt-5.6-luna the value pick within the family at $1 / $6. The gpt-5.5 and gpt-5.4 generations stay on the card, with gpt-5.5-pro as the premium reasoning option and gpt-5.4-nano as the cheapest model at $0.20 / $1.25.

Google's flagship is Gemini 3.1 Pro, in preview at $2 / $12 for prompts up to 200k. Gemini 3.5 Flash, launched May 19, 2026, is the newest speed tier at $1.50 / $9. Gemini 3.1 Flash-Lite at $0.125 / $0.75 is the value pick, and Gemini 2.5 Pro remains on the card as a legacy model being displaced.

Key features

Pricing snapshot

Prices verified July 13, 2026. Providers reprice often, see the update timeline below.

OpenAI API pricing

Standard tier, per 1M tokens, prompts up to 200k. Long-context rates where noted.

Batch API: roughly 50% off. Cached input: 10x cheaper than base input. Web search tool: $10 per 1k calls.

Google API pricing

Per 1M tokens, paid tier, prompts up to 200k where tiered.

Batch: 50% off. Grounded search: $14 per 1k queries after 5,000 free per month. A free tier exists for most Flash models.

Chat plans

What this costs at a real workload

The standardized scenario: 1,000 requests per day at roughly 1,000 input and 1,000 output tokens each, which works out to about 30M input and 30M output tokens per month. Monthly cost = 30 x input rate + 30 x output rate.

Tokenizer caveat for the Claude rows: Fable 5, Sonnet 5, and Opus 4.7+ use a new tokenizer that produces roughly 30% more tokens for the same text. Comparing per-token rates across providers understates Anthropic's effective cost per request by about that margin. Rate cards don't tell you what the invoice will say. The tokenizer does.

At the flagship level, Gemini 3.1 Pro runs $420 per month against $1,050 for gpt-5.6-sol, a 2.5x gap. At the value end, gpt-5.6-luna at $210 sits between Gemini 3.5 Flash at $315 and the much cheaper Flash-Lite.

The spread across all six providers is 143x between the most and least expensive model for the same workload. The full 15-row table is on the AI Pricing Index. For how per-token rates turn into unit economics, see token economics.

Timeline of past updates

OpenAI

• July 9, 2026: gpt-5.6 family launch. Sol at $5 / $30, terra at $2.50 / $15, luna at $1 / $6

• April 24, 2026: gpt-5.5 launch at $5 / $30, doubling flagship input over gpt-5.4

• April 9, 2026: ChatGPT Pro $100 tier added below the $200 tier

• April 2, 2026: Team plan renamed Business, $25/user/mo

• March 5, 2026: gpt-5.4 family launch at $2.50 / $15, doubling over gpt-5

• August 2025: GPT-5 launch at $1.25 / $10

Trajectory: Flagship input went $1.25 to $2.50 to $5.00 in under a year. 4x.

Google

• May 2026 (I/O): AI Ultra restructured, $99.99 5x tier added, 20x tier cut from $249.99 to $199.99

• May 19, 2026: Gemini 3.5 Flash at $1.50 / $9. 3x over Gemini 3 Flash

• February 19, 2026: Gemini 3.1 Pro preview at $2 / $12, 3.1 Flash-Lite at $0.125 / $0.75

• December 17, 2025: Gemini 3 Flash preview at $0.50 / $3, up 67% from 2.5 Flash

• November 18, 2025: Gemini 3 Pro preview at $2 / $12, up from 2.5 Pro's $1.25 / $10

• June 2025: Gemini 2.5 Flash GA at $0.30 / $2.50

Trajectory: The Flash tier is 5x more expensive than 13 months ago. The era of the ever-cheaper budget model ended here first.

The AI Pricing Index

Both providers sit at the high end of the index. OpenAI's flagship input went from $1.25 to $5.00 in under a year. Google's Flash tier costs 5x what it did 13 months ago. If you build on either, budget for a price event roughly every two months, in either direction.

The index counts documented list-price events per provider over the trailing 12 months, recounted monthly. Full methodology and all six provider timelines are on the AI Pricing Index.

What this means for your own pricing

If you build on OpenAI, Google, or both, this pricing complexity becomes your pricing complexity. Four problems show up regardless of which side you pick.

• The margin problem. Every user interaction has variable cost. A power user generating long responses on a flagship model costs 10-50x more than a casual user on a value model. Per-seat pricing doesn't see this. You need usage-aware billing to protect margins.

• The model mix problem. Products route different requests to different models, and the table above shows the cost range that creates inside a single product. Your billing system needs to know which model served which request and price accordingly.

• The credit translation problem. Many AI products abstract provider costs with credit-based pricing. That means maintaining a conversion layer from usage to credits to dollars. Every provider price event forces you to re-derive the conversion rates.

• The visibility problem. Finance needs margin by customer, by feature, by model, by month. If your metering system lives apart from your billing system, that reconciliation happens in spreadsheets.

The index above adds the repricing angle: these two providers logged 12 price events in 12 months between them. Each one forces you to re-derive your own margins, credit conversion rates, and FX exposure. Solvimon runs that layer: metering per provider, credits and wallets as first-class primitives, rate cards you update without an engineering sprint. See Solvimon for AI.

Related

• OpenAI vs Anthropic pricing. Two flagships priced within dollars, and a tokenizer that changes the math.

• Gemini vs DeepSeek pricing. The two price-aggressive providers, compared on the same workload.