{"id":2099,"date":"2026-08-03T18:32:02","date_gmt":"2026-08-03T18:32:02","guid":{"rendered":"https:\/\/convly.ai\/gemini-api-pricing\/"},"modified":"2026-08-03T18:32:02","modified_gmt":"2026-08-03T18:32:02","slug":"gemini-api-pricing","status":"publish","type":"page","link":"https:\/\/convly.ai\/de\/gemini-api-pricing\/","title":{"rendered":"Gemini-API-Preise (2026): Kosten pro 1 Mio. Tokens f\u00fcr jedes Modell"},"content":{"rendered":"

Gemini API pricing starts at $1.25 per 1M input tokens on Gemini 2.5 Pro and reaches
\n$2 on Gemini 3.1 Pro, with output between $7.50 and $12.<\/strong> The headline rates are
\ncompetitive, but Gemini’s pricing has a feature none of its rivals share to the same degree:
\nprompts above roughly 200K tokens are billed at a higher rate on several models. For a family
\nsold on its million-token context, that is the number that decides your real bill.<\/p>\n

\n

Gemini API pricing: every model, per 1M tokens<\/h2>\n \n \n \n \n \n \n \n
Model<\/th>Input $\/1M<\/th>Output $\/1M<\/th>\n Blended $\/1M<\/th>Context<\/th>\n <\/tr><\/thead>\n
Gemini 3.6 Flash<\/a><\/td>\n $1.50<\/td>\n $7.50<\/td>\n $2.70<\/strong><\/td>\n 1M<\/td>\n <\/tr>\n
Gemini 2.5 Pro<\/a><\/td>\n $1.25<\/td>\n $10.00<\/td>\n $3.00<\/strong><\/td>\n 1M (1,048,576 tokens)<\/td>\n <\/tr>\n
Gemini 3.5 Flash<\/a><\/td>\n $1.50<\/td>\n $9.00<\/td>\n $3.00<\/strong><\/td>\n 1M<\/td>\n <\/tr>\n
Gemini 3.1 Pro<\/a><\/td>\n $2.00<\/td>\n $12.00<\/td>\n $4.00<\/strong><\/td>\n 1.05M<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n

Blended is the effective rate at a 4:1 input-to-output mix, which is what a\n typical chat or retrieval workload actually produces. It is the number to compare across\n vendors \u2014 a headline input price hides how much the output side costs.<\/p>\n\n

What Gemini costs per month<\/h2>\n \n \n \n \n \n
Workload<\/th>Tokens \/ month<\/th>\n Gemini 3.6 Flash
cheapest<\/span><\/th>\n 		Gemini 3.1 Pro
most capable tier<\/span><\/th>\n <\/tr><\/thead>\n
Side project<\/td>\n 1M in \/ 0.25M out<\/td>\n $3.38<\/strong><\/td>\n $5.00<\/strong><\/td>\n <\/tr>\n
Small team<\/td>\n 20M in \/ 5M out<\/td>\n $68<\/strong><\/td>\n $100<\/strong><\/td>\n <\/tr>\n
Production<\/td>\n 200M in \/ 50M out<\/td>\n $675<\/strong><\/td>\n $1,000<\/strong><\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n

Model your own volumes in the\n AI API cost calculator<\/a>.<\/p>\n\n

How Gemini compares to other providers<\/h2>\n

Each provider's cheapest priced model, so the comparison is like for like on entry cost.<\/p>\n \n \n \n \n \n \n \n \n \n \n \n \n \n
Provider<\/th>Cheapest model<\/th>Blended $\/1M<\/th><\/tr><\/thead>\n
Mistral AI<\/td>\n Mistral 7B<\/a><\/td>\n $0.0220<\/td>\n <\/tr>\n
Meta<\/td>\n Llama 3.1 8B<\/a><\/td>\n $0.0220<\/td>\n <\/tr>\n
Alibaba<\/td>\n Qwen3 8B<\/a><\/td>\n $0.0600<\/td>\n <\/tr>\n
Google this page<\/span><\/td>\n Gemma 3 4B<\/a><\/td>\n $0.0600<\/td>\n <\/tr>\n
Microsoft<\/td>\n Phi-4<\/a><\/td>\n $0.0840<\/td>\n <\/tr>\n
DeepSeek<\/td>\n DeepSeek V4-Flash<\/a><\/td>\n $0.168<\/td>\n <\/tr>\n
Moonshot AI<\/td>\n Kimi K2.7 Code<\/a><\/td>\n $0.980<\/td>\n <\/tr>\n
Anthropic<\/td>\n Claude Haiku 4.5<\/a><\/td>\n $1.80<\/td>\n <\/tr>\n
Zhipu AI<\/td>\n GLM 5.2<\/a><\/td>\n $2.00<\/td>\n <\/tr>\n
xAI<\/td>\n Grok 4<\/a><\/td>\n $5.40<\/td>\n <\/tr>\n
OpenAI<\/td>\n GPT-5.6 Sol<\/a><\/td>\n $10.00<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n

Cheapest is not the same as best value \u2014 check capability alongside price on the\n LLM leaderboard<\/a>, or browse every\n model in the AI models database<\/a>.<\/p>\n <\/div>\n\n

Which Gemini model should you use?<\/h2>\n

The interesting thing about Google’s current line is that the cheaper tier is often the
\nbetter one.<\/p>\n

Gemini 3.5 Flash<\/strong> outperforms Gemini 3.1 Pro on coding and agentic
\nbenchmarks while running roughly four times faster and about 25% cheaper, with the same
\n1M-token context. When the faster, cheaper model also scores higher, the Pro tier stops being
\nthe default and becomes a specialist choice. For coding assistants, agent loops and anything
\nwhere a user is waiting on a response, Flash wins on every axis at once \u2014 which is rare in
\nmodel selection, where speed, cost and quality normally trade against each other.<\/p>\n

Gemini 3.6 Flash<\/strong>, released 21 July 2026, is the newest in the line. Its
\ninput rate is unchanged at $1.50 while output drops from $9 to $7.50 \u2014 a 17% cut on the side of
\nthe bill that usually dominates. For generation-heavy workloads that is a straightforward
\nsaving with no capability trade-off implied by the pricing, which makes it the default choice
\nwithin the Flash tier for new work. On prompt-heavy retrieval workloads the two are
\ninterchangeable on cost, since input is identical. To clear up a common search: there is no
\nGemini 4 as of July 2026.<\/p>\n

Gemini 3.1 Pro<\/strong> is worth choosing for multimodal depth rather than by
\ndefault, given that Flash beats it on coding at a lower price.<\/p>\n

Gemini 2.5 Pro<\/strong> remains widely deployed and well supported. It reached
\ngeneral availability on 17 June 2025 and was a benchmark leader in coding, mathematics and
\nlong-context reasoning, with a native 1,048,576-token context and fully multimodal input \u2014 text,
\nimages, audio, video and PDFs. Newer 3.x models supersede it, but at $1.25 input it is still
\none of the strongest value picks among frontier reasoning models.<\/p>\n

The 200K threshold is the thing to plan around<\/h2>\n

Gemini’s long-context billing is where budgets break. Gemini 3.1 Pro bills prompts above
\n200K tokens at roughly double the input rate. Gemini 2.5 Pro is explicitly tiered: $1.25 per
\n1M input up to 200K, rising to $2.50 above it, with output moving from $10 to $15.<\/p>\n

The practical consequence is that a feature costing one amount in testing can cost twice
\nthat in production, because the threshold is crossed per request and depends entirely on how
\nmuch a user pasted in. Budgeting a long-context feature on the headline figure will understate
\nthe bill substantially once real prompts start arriving.<\/p>\n

There are two sensible responses. Design around the threshold with aggressive retrieval and
\nreranking so prompts stay under it \u2014 which usually improves answer quality anyway, since
\nattention across very long contexts is not uniform. Or compare against a model that charges one
\nflat rate across its whole window; Claude Opus
\n4.8 has no long-context premium at all<\/a>, which for a genuinely retrieval-heavy workload can
\nwork out cheaper despite a higher sticker price.<\/p>\n

Google’s open models are priced separately<\/h2>\n

Gemma 3 is Google’s open-weight family and sits outside this table because it is a different
\nproposition: 4B, 12B and 27B models under an open licence, multimodal across text and images,
\n128K context, and cheap enough on hosted APIs ($0.05 to $0.16 per 1M) that the real decision is
\nwhether to run them yourself. Gemma 3 27B needs about 16 GB of VRAM at 4-bit, which puts it on
\na single consumer card. Size it in the
\nVRAM calculator<\/a>, or work out whether
\nself-hosting beats the API at your volume with the
\nself-hosting vs API
\ncalculator<\/a>.<\/p>\n

\n

Frequently asked questions<\/h2>\n
\n
How much does the Gemini API cost?<\/summary>

Gemini API pricing runs from $2.70 per 1M blended tokens on Gemini 3.6 Flash up to $4.00 on Gemini 3.1 Pro. Blended assumes a 4:1 input-to-output mix. Input and output are billed separately, and output is always the more expensive side.<\/p><\/details>\n

What is the cheapest Gemini model?<\/summary>

Gemini 3.6 Flash, at $1.50 per 1M input tokens and $7.50 per 1M output. A small-team workload of 20M input and 5M output tokens a month costs about $68 on it.<\/p><\/details>\n

How much does Gemini cost per month?<\/summary>

At 20M input and 5M output tokens a month, Gemini 3.6 Flash costs about $68 and Gemini 3.1 Pro about $100. A side project at 1M\/0.25M costs a small fraction of that. Use the AI API cost calculator for your own volumes.<\/p><\/details>\n

Is Gemini cheaper than Mistral AI?<\/summary>

On entry-level pricing, Gemini starts at $2.70 per 1M blended and Mistral AI starts at $0.0220 on Mistral 7B. Mistral AI is the cheaper entry point, though capability differs \u2014 compare both on the leaderboard before switching.<\/p><\/details>\n

Why does Gemini charge more for output tokens than input?<\/summary>

Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost more to serve. This is why prompt-heavy workloads such as retrieval and classification are far cheaper to run than generation-heavy ones, and why the blended rate matters more than the headline input price.<\/p><\/details>\n <\/div>\n

Prices are the published list rates for each model's primary API and are reviewed as\n providers change them. Volume, batch and cached-input discounts are not included. Last reviewed\n August 2026.<\/p>\n<\/div>\n\n