GILD verdict
Choose the OpenAI API for most workloads, since GPT-6.1 Sol leads Gemini on Artificial Analysis's index at a lower cost per task.
Teams that need both run them behind one gateway, as Wealthsimple and DoorDash do, so decide which carries each workload. GPT-6.1 Sol scores 55 on Intelligence Index v5.0 (Oct 27, 2026) at US$1.27 per task, above Gemini 4 Argon's 50 at US$3.84 and Gemini 3.8 Flash's 31 at US$7.77. Choose the Gemini API when your spend runs through Google Cloud or you want to prototype on a free tier. Argon's introductory price ends on November 30, 2026, and at its standard US$4 and US$20 the same token use would put its cost per task above GPT-6 Astra's US$6.24; Flash's introductory price ends on December 31, 2026. At GILD's workload of 60,000 tasks a month, GPT-6.1 Sol with caching costs US$1,632 against US$2,400 for Argon uncached at its introductory price.
Teams that use both run them behind one gateway: Wealthsimple added Gemini beside OpenAI for its 1-million-token context, and DoorDash serves OpenAI and Gemini models together while naming Gemini's one-year model lifetime as migration work across hundreds of use cases. On price, OpenAI costs less per finished task at every level below the flagship. GPT-6 Luna lists at US$0.10 input and US$0.50 output, below every Gemini 3-generation model. The Artificial Analysis Intelligence Index v5.0 (Oct 27, 2026) measured Luna at about a fifth of Gemini 3.5 Flash-Lite's cost per task with a higher score, 28 to 12. Only the older Gemini 2.5 Flash-Lite is cheaper on output. Both platforms offer a 50% batch discount, cheaper cached input, Flex, and remote MCP support.
What teams report
Run both 2
Wealthsimple
Wealthsimple's gateway started with OpenAI and Cohere and added Gemini for its 1-million-token context window, then Amazon Bedrock; in September 2024 its v2 gateway API mirrored OpenAI's API specification (InfoQ, Feb 6, 2025).
“Following that, we started expanding the LLM providers that we supported. The catalyst for this was Gemini. Around that time, Gemini had launched their 1-million-token context window models, and we were really interested to see how this could circumvent a lot of our previous challenges with the context window limitations.”
Mandy Gu, Author of the InfoQ article on Wealthsimple's platform, WealthsimpleInfoQ, , write-up DoorDash
DoorDash started vendor-first with OpenAI, Anthropic and Gemini models behind one gateway. Its platform lead names cost at scale and Gemini's one-year model lifetime, which forces migrations across hundreds of use cases, as the cracks that followed (InfoQ talk, Oct 3, 2026).
“Similarly, on the model side, we went vendor first. Let's use OpenAI, Anthropic, Gemini models. Folks started getting productive. Folks learned how to do prompt engineering. They were able to scale up. At DoorDash scale, at some point, cost becomes an issue. There were a few other cracks that started to appear. Like the one is the deprecation treadmill. How many of you know that Gemini has a one-year lifetime for models? In one year, the model gets shut down, and this applies to every new model. At the end of that one year, you have to switch to a newer model, whether or not it meets your business needs. I could probably accept that if I was a single product team and I was working on my product, but I as a platform team, I have to think about hundreds of use cases and how I'm going to drive hundreds of teams to migrate to the latest model without necessarily giving them a business argument of the benefits.”
Swaroop Chitlur, Lead, Generative AI Platform, DoorDashInfoQ, , recording
Choosing between OpenAI API and Gemini API
Choose OpenAI API if
- Your high-volume work fits GPT-6 Luna, which lists at US$0.10 input and US$0.50 output per million tokens, below Gemini 3.5 Flash-Lite's US$0.30 and US$2.50.
- You want OpenAI's agent tooling: the Agents SDK, the hosted Agents API, and remote MCP servers in the Responses API.
- You need regional processing in the US or the EU, at a 10% uplift on eligible models.
- Your gateway already speaks OpenAI's request shape, as Wealthsimple's v2 gateway API has since September 2024, so adding OpenAI models needs no new client code.
Choose Gemini API if
- You want to prototype on a free tier before attaching billing; free-tier content is used to improve Google's products, and paid-tier content is not.
- Gemini 3.8 Flash meets your quality bar at US$0.75 input and US$3.75 output per million tokens through December 31, 2026.
- You want flagship reasoning while Gemini 4 Argon's introductory price of US$2 input and US$10 output lasts; it ends on November 30, 2026, and the standard price from December 1 is US$4 and US$20.
- Your organization already spends on Google Cloud, which counts toward Gemini's higher usage tiers.
Side by side
| Dimension | OpenAI API | Gemini API |
|---|---|---|
| Monthly bill at one workloadGILD's calculation from published rates, fetched Oct 28, 2026: 2,000 tasks a day for 30 days, each with 10,000 input tokens and 2,000 output tokens. The cached OpenAI lines bill 7,000 of those input tokens as cache reads and 1,000 as 30-minute cache writes. Gemini's lines are uncached, because its cache storage is billed per hour. Taxes and negotiated discounts are excluded. | US$1,632 a month on GPT-6.1 Sol with caching, US$2,400 without; US$83.70 on GPT-6 Luna with caching1013 | US$2,400 a month on Gemini 4 Argon without caching at its introductory price, US$4,800 at its standard price from December 1, 2026; US$900 on Gemini 3.8 Flash through December 31, 2026, then US$1,80024 |
| Current lineupStandard paid-tier list prices in US dollars per million input / output tokens. | GPT-6 Astra US$10 / US$50; GPT-6.1 Sol US$2 / US$10; GPT-6.1 Terra US$1 / US$5; GPT-6 Luna US$0.10 / US$0.50 (prompts up to 272,000 tokens) | Gemini 4 Argon US$2 / US$10 through November 30, 2026, then US$4 / US$20. Gemini 3.1 Pro (pre-release) US$2 / US$12 for prompts up to 200,000 tokens. Gemini 3.8 Flash US$0.75 / US$3.75 through December 31, 2026, then US$1.50 / US$7.50. Gemini 3.5 Flash-Lite US$0.30 / US$2.50. Gemini 2.5 Flash-Lite US$0.10 / US$0.40 |
| Independent scoresArtificial Analysis Intelligence Index v5.0 (Oct 27, 2026) at each model's highest-scoring reasoning effort, max for OpenAI's models and high for Gemini's, and its average API cost per index task at list prices with caching, checked Oct 28, 2026. | GPT-6 Astra 57 at US$6.24 per task; GPT-6.1 Sol 55 at US$1.27; GPT-6.1 Terra 51 at US$0.96; GPT-6 Luna 28 at US$0.151345 | Gemini 4 Argon 50 at US$3.84 per task; Gemini 3.8 Flash 31 at US$7.77; Gemini 3.5 Flash-Lite 12 at US$0.69; Gemini 3.1 Pro preview not scored on Index v5.0167 |
| Measured success rate | 98% of requests completed without an upstream error on Requesty's gateway in April 20268 | 96% on Google Cloud's Vertex AI route in the same data; Requesty does not list the Developer API separately8 |
| Free use | A free tier in allowed geographies with a US$100 monthly usage limit; Build starts after US$5 in credit purchases | Free tier with free input and output tokens on a limited set of models; free-tier content is used to improve Google's products |
| Context window | 1,050,000 tokens with up to 128,000 output tokens on GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, and GPT-6 Luna | 1,048,576 input tokens and 65,536 output tokens on Gemini 3.8 Flash |
| Long prompts | Above 272,000 input tokens, 2x input and cache rates and 1.5x output for the full request | Gemini 3.1 Pro's listed rate covers prompts up to 200,000 tokens; Flash models list one rate |
| Cached input | On by default; reads cost 0.1x the input rate (0.05x on GPT-6.1 Sol and GPT-6.1 Terra). On GPT-5.6 and later, writes cost 1.25x for the default 30-minute cache or 2.5x for a 12-hour cache | Implicit caching on by default for Gemini 2.5 and newer, from 4,096 tokens on Gemini 3.8 Flash. Cached input costs US$0.075 per million tokens through December 31, 2026, with a listed storage price of US$0.50 per million tokens per hour |
| Batch and Flex | Batch and Flex at 50% of Standard; batches complete within 24 hours | Batch and Flex at 50% of Standard on the paid tier; batches target 24 hours |
| Faster processing | Fast mode at 2x Standard, up to 2.5x faster; Ultrafast mode at 6x on GPT-6.1 Sol (US and EU residency) and GPT-6 Astra (US residency only) | Priority inference at US$1.35 / US$6.75 on Gemini 3.8 Flash through December 31, 2026, 1.8x Standard |
| Self-serve limits | Monthly usage limits of US$500 on Build, US$5,000 on Launch, and US$200,000 on Grow, plus organization and project hard spend limits | Billing tier caps of US$250 on Tier 1, US$2,000 on Tier 2, and US$20,000 to US$100,000+ on Tier 3, plus spend-based rate limits per 10 minutes |
| Data use | Not used for training unless you opt in; abuse monitoring logs kept up to 30 days; Zero Data Retention by approval | Paid-tier prompts and responses are not used to improve Google's products and are logged for a limited period to detect abuse |
| Where it runs | OpenAI's API; the same GPT-6 models on Microsoft Azure and Amazon Bedrock, billed by those clouds | Gemini API and Google AI Studio, with enterprise deployments on Gemini Enterprise Agent Platform |
| Agent tooling | Agents SDK, the Agents API (beta) running a managed Codex harness, and remote MCP servers in the Responses API | Parallel and compositional function calling, and remote MCP servers through the Interactions API |
What practitioners report, side by side
Each product's figures come from its own practitioners, for the group you pick.
Overall signal
OpenAI API reads +3 and Gemini API 0, each from its own practitioners. OpenAI API: 22 positive, 20 negative of 65. Gemini API: 24 positive, 25 negative of 74.
The score counts the authors from teams and leaders that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so OpenAI API's +3 rests on votes split 21 positive, 22 mixed and 19 negative and Gemini API's 0 on 23, 24 and 23.
A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Run a pilot on your own prompts to measure output quality and cost per task.
Footprint on the AI Application Platform stack
They differ on one of 9 layers. On Observability, OpenAI API supports it and Gemini API touches it. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.
| Layer | OpenAI API | Gemini API |
|---|---|---|
| Operate | ||
| Observability | Supports | Touches |
The 8 layers where they match
| Layer | OpenAI API | Gemini API |
|---|---|---|
| Model | ||
| Model APIs | Core | Core |
| Model gateway | Touches | Touches |
| Build | ||
| Orchestration | Supports | Supports |
| Tool use | Core | Core |
| Data | ||
| Retrieval | Supports | Supports |
| Evaluate | ||
| Evaluation | Touches | Touches |
| Operate | ||
| Cloud deployment | Supports | Supports |
| Guardrails | Supports | Supports |
What practitioners praise
Among teams and leaders, OpenAI API alone has three praise themes that reach 5 sources from 3 authors. Stability and Tool Calling & Structured Output lead, at 2 of 5 and 1 of 8. Of the three themes both carry, Model Quality differs most. It is praised in 14 of the 18 OpenAI API sources that discuss it, against 11 of 12 for Gemini API. Gemini API alone has three praise themes that reach 5 sources from 3 authors. Multimodal & Long Context and Tool Use & Structured Output lead, at 13 of 13 and 4 of 9.
| Praised for | OpenAI API | Gemini API |
|---|---|---|
| Model Quality | 14 of 18 | 11 of 12 |
| Cost Efficiency | 12 of 14 | 16 of 17 |
| Speed | 6 of 8 | 6 of 9 |
| Stability | 2 of 5 | 2 of 2 so far |
| Tool Calling & Structured Output | 1 of 8 | Not a theme in its sources |
| Long Context | 0 of 5 | Not a theme in its sources |
| Multimodal & Long Context | Not a theme in its sources | 13 of 13 |
| Tool Use & Structured Output | Not a theme in its sources | 4 of 9 |
| Developer Experience | 3 of 4 so far | 3 of 6 |
Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.
A count ending in "so far" has under 5 sources from 3 authors among teams and leaders. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.
What practitioners raise as concerns
Gemini API's sources raise Latency more often than OpenAI API's. It is raised in 9 of the 12 OpenAI API sources that discuss it, against 11 of 11 for Gemini API. OpenAI API alone has seven concern themes that reach 5 sources from 3 authors. Token Cost and Inconsistent Results lead, at 7 of 7 each. Gemini API alone has five concern themes that reach 5 sources from 3 authors. Model Changes and Billing & Spend Control lead, at 14 of 14 and 9 of 9.
| Raised as a concern | OpenAI API | Gemini API |
|---|---|---|
| Latency | 9 of 12 | 11 of 11 |
| Token Cost | 7 of 7 | Not a theme in its sources |
| Inconsistent Results | 7 of 7 | Not a theme in its sources |
| Model & API Changes | 7 of 8 | Not a theme in its sources |
| Long-Context Limits | 6 of 6 | 4 of 4 so far |
| Data Handling | 5 of 7 | 3 of 4 so far |
| Setup & SDK Friction | 4 of 5 | Not a theme in its sources |
| Rate Limits & Reliability | 4 of 8 | Not a theme in its sources |
| Model Changes | Not a theme in its sources | 14 of 14 |
| Billing & Spend Control | Not a theme in its sources | 9 of 9 |
| Reliability & Capacity | Not a theme in its sources | 9 of 9 |
| Rate Limits & Tiers | Not a theme in its sources | 8 of 8 |
| Two Platforms & Setup | Not a theme in its sources | 5 of 5 |
33 sources
- Artificial Analysis: LLM Leaderboard (Oct 28, 2026)
- Artificial Analysis: Changelog (Oct 28, 2026)
- Artificial Analysis: GPT-6 Astra analysis (Oct 28, 2026)
- Artificial Analysis: GPT-6.1 Sol analysis (Oct 28, 2026)
- Artificial Analysis: GPT-6 Luna analysis (Oct 28, 2026)
- Artificial Analysis: Gemini 4 Argon analysis (Oct 28, 2026)
- Artificial Analysis: Gemini 3.8 Flash analysis (Oct 28, 2026)
- Requesty: Operational metrics per provider, April 2026 (May 9, 2026)
- OpenAI API docs: Models
- OpenAI API docs: Pricing
- OpenAI API docs: GPT-6.1 Terra
- OpenAI API docs: GPT-6 Luna
- OpenAI API docs: Prompt caching
- OpenAI API docs: Batch API
- OpenAI API docs: Flex processing
- OpenAI API docs: Fast mode
- OpenAI API docs: Changelog
- OpenAI API docs: Rate limits
- OpenAI API docs: Data controls in the OpenAI platform
- OpenAI API docs: Agents
- OpenAI API docs: MCP servers
- Gemini API docs: Models
- Gemini API docs: Gemini 3.8 Flash
- Gemini API docs: Pricing
- Gemini API docs: Context caching
- Gemini API docs: Batch API
- Gemini API docs: Flex inference
- Gemini API docs: Rate limits
- Gemini API docs: Billing
- Gemini API Additional Terms of Service
- Gemini API docs: Function calling
- InfoQ: Mandy Gu on Wealthsimple's gen AI platform and LLM gateway (Feb 6, 2025)
- InfoQ: Building a GenAI Platform at DoorDash, talk by Swaroop Chitlur and Siddharth Kodwani (Oct 3, 2026)