Google Gemini

• Google Gemini, Gemini via AI Studio for prototyping or Vertex AI for production.
• - Gemini through two doors: AI Studio (free tier, prototyping) and Vertex AI (region pinning, quotas, contracts)
• Pitch is long context at low cost and genuinely native audio in and out
• The deepest regional footprint here, including Australian regions competitors cannot match.
• Built by Google.
• Founded 1998.

• We track it in 1 comparison, LLM APIs.
• Every claim below is a cell in a table you can open and check rather than an impression.
• Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it wins.

• Effort control, Yes, which only 4 of 18 tools here manage.
• AU region, Yes, which only 4 of 17 tools here manage.
• $/M cache read, $0.2 /M tok, 3rd of 8.

• Each of those is ranked against the whole roster on its category page, not against a hand-picked subset.
• A first place here means first of everything we list.

Where it gives ground.

• Max output, 65,536 tokens, 5th of 6. DeepSeek records 384,000 tokens.

• None of these disqualify it on their own.
• They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

• Provenance. 24 of 27 tracked fields carry a value for Google Gemini, and 17 of those cite a document you can open.
• Last verified 2026-07-30.
• Every figure keeps its own provenance, measured by us, claimed by the vendor, inferred, or community-reported, and we would rather print a dash than a guess.

• Its nearest neighbour in our data is Qwen.
• Google Gemini is ahead on Computer use (Partial against No) and Schema output (Yes against Partial).
• Qwen takes $/M output ($7.5 /M tok against $12 /M tok).
• That pattern repeats across the rest of the roster, see Google Gemini alternatives for the other rivals, each compared the same way.

Company

Google

Founded

1998

Fields we track

24 of 27

Last verified

2026-07-30

The cheapest million-token window and only genuinely native audio, but the fastest-churning line-up here, so expect yearly migrations.

Effort control

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

AU region

●Yes1st of 17

Inferredverified 2026-01-15

$/M input

$2/M tok4th of 9

Vendor-claimedverified 2026-07-30source

$/M output

$12/M tok6th of 9

Vendor-claimedverified 2026-07-30source

$/M cache read

$0.2/M tok3rd of 8

Vendor-claimedverified 2026-07-30source

Max output

65,536tokens5th of 6

Vendor-claimedverified 2026-07-30source

Tool use

●Yes1st of 18

Vendor-claimedverified 2026-07-30source

Schema output

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

Google Gemini is the right call in these situations, each one drawn from a field we actually record.

• Workloads that genuinely need 500K+ tokens of context per request.
• Voice and video products wanting one model rather than a pipeline.
• Australian or regulated deployments that must pin inference to a region.
• Effort control is your binding constraint. Google Gemini records Yes, which only 4 of the 18 tools in the LLM APIs roster do. We define that field as a request-level parameter that trades reasoning depth against cost and latency;.
• AU region is your binding constraint. Google Gemini records Yes, which only 4 of the 17 tools in the LLM APIs roster do. We define that field as whether inference can be pinned to Australian infrastructure;.
• $/M cache read is your binding constraint. Google Gemini records $0.2 /M tok, 3rd of 8 in the LLM APIs roster.

Reach for something else when any of the following is a requirement rather than a nice-to-have.

• Max output. Google Gemini records 65,536 tokens, 5th of 6 in the LLM APIs roster. DeepSeek records 384,000 tokens on the same field. If that is a hard requirement rather than a preference, start elsewhere.

• We publish this block because a comparison that only lists what a tool is good at is marketing.
• Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know.

• 3 of the 27 fields we track for Google Gemini are still blank: TTFT p50, Output tok/s and Notice period.
• Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest.
• If you know any of these figures and can point at a document, tell us.