Model comparison
Gemini 3.8 Flash vs GPT-6.1 Sol: pricing, context and features compared
Gemini 3.8 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-6.1 Sol costs $2 and $10. Here is how they compare on specs, on what real workloads cost, and on which to pick for what, from prices updated daily.
A common question is whether Google’s Flash tier is good enough for work that would otherwise go to OpenAI’s main model. The price gap is large, so it is worth knowing exactly how large for your workload.
- Input / 1M
- $0.75
- Output / 1M
- $3.75
- Context
- 1,048,576
- Max output
- 65,536
- Input / 1M
- $2
- Output / 1M
- $10
- Context
- 1,050,000
- Max output
- 128,000
Verdict
Gemini 3.8 Flash or GPT-6.1 Sol: which to pick
Gemini 3.8 Flash has the lower list prices: 2.7× cheaper per input token and 2.7× per output token. On our data, Gemini 3.8 Flash has the edge for the lowest bill, very long prompts, non-text inputs, repeated prompts and batch jobs, and GPT-6.1 Sol for long replies. For scale, a support chatbot handling 1,000 requests a day costs about $64.45 a month on Gemini 3.8 Flash and $169.57 on GPT-6.1 Sol.
- Lowest bill for typical workloadsPick Gemini 3.8 Flash
- Gemini 3.8 Flash costs less on all 4 workloads below, 2.5× to 2.7× cheaper than GPT-6.1 Sol at list prices.
- Very long prompts (whole codebases, long documents)Pick Gemini 3.8 Flash
- Both read about 1,048,576 tokens per request. One 830,000-token prompt with a 2,000-token reply costs $0.63 on Gemini 3.8 Flash and $3.35 on GPT-6.1 Sol. GPT-6.1 Sol charges $4 / $15 per 1M once a prompt passes 272,000 tokens.
- Long replies (reports, large code files)Pick GPT-6.1 Sol
- GPT-6.1 Sol can write up to 128,000 tokens in one reply, against 65,536 for Gemini 3.8 Flash.
- Images, audio, video or files in the promptPick Gemini 3.8 Flash
- Gemini 3.8 Flash also accepts audio and video, which GPT-6.1 Sol doesn’t.
- Repeated long prompts (prompt caching)Pick Gemini 3.8 Flash
- Gemini 3.8 Flash bills cached input at $0.075 per 1M (10% of its input price), against $0.10 per 1M (5% of its input price) for GPT-6.1 Sol.
- Overnight batch jobsPick Gemini 3.8 Flash
- Both offer batch prices; on the document summariser workload Gemini 3.8 Flash costs $37.64 a month against $100.38.
- Self-hosting or choosing your own hostEither
- Neither has downloadable weights: both are only available through their provider’s API and cloud partners.
Criteria use only list prices, published limits and the features each provider declares. We don’t rank answer quality; test both models on your own prompts before you commit.
Specs
Gemini 3.8 Flash vs GPT-6.1 Sol side by side
| Spec | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| Provider | OpenAI | |
| Input / 1M tokens | $0.75 (better) | $2 |
| Output / 1M tokens | $3.75 (better) | $10 |
| Cached input / 1M | $0.075 (better) | $0.10 |
| Cache write / 1M | No write charge listed | $2.50 |
| Batch in / out | $0.375 / $1.875 (better) | $1 / $5 |
| Long-prompt price | None | $4 / $15 over 272,000 |
| Context window | 1,048,576 | 1,050,000 |
| Max output | 65,536 | 128,000 (better) |
| Accepts | text, images, files, audio, video | text, images, files |
| Image input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning mode | Yes | Yes |
| Prompt caching | Yes | Yes |
| Structured output | Yes | Yes |
| Open weights | No | No |
| Knowledge cutoff | Not published | Not published |
| First listed | 2026-09-02 | 2026-09-29 |
Prices in US dollars per million tokens. Highlighted: the lower price or larger limit where the difference is over 5%. Features are as declared by each provider’s API; “First listed” is when our data source first listed the model. Long-prompt prices apply to the whole request once the prompt passes the threshold.
Costs
What Gemini 3.8 Flash and GPT-6.1 Sol cost for real workloads
| Workload | Tokens in / out | Requests/day | Gemini 3.8 Flash / month | GPT-6.1 Sol / month | Cheaper |
|---|---|---|---|---|---|
| Support chatbotCalculator: Gemini 3.8 Flash · GPT-6.1 Sol | 1,500 / 400 | 1,000 | $64.4550% cached | $169.5750% cached | Gemini 3.8 Flash (2.6×) |
| RAG appCalculator: Gemini 3.8 Flash · GPT-6.1 Sol | 6,000 / 500 | 500 | $84.6320% cached | $223.8720% cached | Gemini 3.8 Flash (2.6×) |
| Coding agentCalculator: Gemini 3.8 Flash · GPT-6.1 Sol | 40,000 / 2,000 | 200 | $96.7380% cached | $238.4780% cached | Gemini 3.8 Flash (2.5×) |
| Document summariserCalculator: Gemini 3.8 Flash · GPT-6.1 Sol | 8,000 / 600 | 300 | $37.64batch | $100.38batch | Gemini 3.8 Flash (2.7×) |
Each row uses the same token counts for both models and list prices, with caching and batch discounts only where the provider publishes them. A month is 365 ÷ 12 days. The support chatbot reads 50% of its prompt from the cache. The RAG app reads 20% of its prompt from the cache. The coding agent reads 80% of its prompt from the cache. The document summariser runs as a batch job where a batch price exists. Open either model in the LLM cost calculator to change any number.
Tokenizers
Are their per-token prices comparable?
Not exactly. Each model splits text into tokens its own way, so the same prompt can be a different number of tokens on Gemini 3.8 Flash and GPT-6.1 Sol, and the cheaper per-token price isn’t always the cheaper bill. Where a tokenizer isn’t public or measured we say so rather than guess.
| Model | Tokenizer | Tokens per 1,000 words | Output per 1M words |
|---|---|---|---|
| Gemini 3.8 Flash | GeminiOur measurement | 1,186 | $4.45 |
| GPT-6.1 Sol | OpenAI hasn’t published the tokenizer GPT-6.1 Sol uses, so its count for a given text isn’t known exactly. | ||
English prose, measured on the Universal Declaration of Human Rights (2026-10-11) or taken from the provider’s published words-per-token figure. Code, JSON and other languages use more tokens per word. Count your own text in the token counter or convert with tokens to words.
FAQ
Gemini 3.8 Flash vs GPT-6.1 Sol questions
Is Gemini 3.8 Flash cheaper than GPT-6.1 Sol?
Gemini 3.8 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GPT-6.1 Sol costs $2 and $10. For a support chatbot handling 1,000 requests a day, that is about $64.45 a month on Gemini 3.8 Flash against $169.57 on GPT-6.1 Sol, so Gemini 3.8 Flash is 2.6× cheaper there. Try your own numbers in the LLM cost calculator.
Which has the bigger context window, Gemini 3.8 Flash or GPT-6.1 Sol?
Their context windows are about the same size. Gemini 3.8 Flash reads up to 1,048,576 tokens (roughly 786,432 English words) and writes up to 65,536 tokens per reply. GPT-6.1 Sol reads up to 1,050,000 tokens (roughly 787,500 English words) and writes up to 128,000 tokens per reply. The window is shared between your prompt and the reply. Word counts use a rough 0.75 words per token; real ratios depend on the tokenizer.
Is Gemini 3.8 Flash or GPT-6.1 Sol better for coding?
We don’t publish benchmark scores, so this page can’t say which writes better code. What we can show is cost: a coding agent re-sending 40,000 tokens of context (80% cached) and writing 2,000 tokens, 200 times a day, costs about $96.73 a month on Gemini 3.8 Flash and $238.47 on GPT-6.1 Sol. Both support tool calling. Test both on tasks from your own repository.
Can Gemini 3.8 Flash and GPT-6.1 Sol read images?
Gemini 3.8 Flash accepts images, files, audio and video as well as text. GPT-6.1 Sol accepts images and files as well as text. So both can read screenshots, photos and scanned pages. This is what each provider declares for its API, not a measure of how well it works.
Are Gemini 3.8 Flash and GPT-6.1 Sol open source?
No. Neither Gemini 3.8 Flash nor GPT-6.1 Sol has published weights: you can only use them through Google’s and OpenAI’s APIs and their cloud partners. If you need a model you can host yourself, compare open-weight options in the model comparison table.
Do Gemini 3.8 Flash and GPT-6.1 Sol count tokens the same way?
Not necessarily. Each model splits text into tokens with its own tokenizer, so the same prompt can be a different number of tokens on each, and per-token prices aren’t directly comparable. Gemini 3.8 Flash uses about 1,186 tokens per 1,000 English words (our measurement). OpenAI hasn’t published the tokenizer GPT-6.1 Sol uses, so its count for a given text isn’t known exactly. Count your own text with the token counter or convert with tokens to words.